the importance of writing a research paper is

• How to write a research paper

Last updated

11 January 2024

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With proper planning, knowledge, and framework, completing a research paper can be a fulfilling and exciting experience. 

Though it might initially sound slightly intimidating, this guide will help you embrace the challenge. 

By documenting your findings, you can inspire others and make a difference in your field. Here's how you can make your research paper unique and comprehensive.

• What is a research paper?

Research papers allow you to demonstrate your knowledge and understanding of a particular topic. These papers are usually lengthier and more detailed than typical essays, requiring deeper insight into the chosen topic.

To write a research paper, you must first choose a topic that interests you and is relevant to the field of study. Once you’ve selected your topic, gathering as many relevant resources as possible, including books, scholarly articles, credible websites, and other academic materials, is essential. You must then read and analyze these sources, summarizing their key points and identifying gaps in the current research.

You can formulate your ideas and opinions once you thoroughly understand the existing research. To get there might involve conducting original research, gathering data, or analyzing existing data sets. It could also involve presenting an original argument or interpretation of the existing research.

Writing a successful research paper involves presenting your findings clearly and engagingly, which might involve using charts, graphs, or other visual aids to present your data and using concise language to explain your findings. You must also ensure your paper adheres to relevant academic formatting guidelines, including proper citations and references.

Overall, writing a research paper requires a significant amount of time, effort, and attention to detail. However, it is also an enriching experience that allows you to delve deeply into a subject that interests you and contribute to the existing body of knowledge in your chosen field.

• How long should a research paper be?

Research papers are deep dives into a topic. Therefore, they tend to be longer pieces of work than essays or opinion pieces. 

However, a suitable length depends on the complexity of the topic and your level of expertise. For instance, are you a first-year college student or an experienced professional? 

Also, remember that the best research papers provide valuable information for the benefit of others. Therefore, the quality of information matters most, not necessarily the length. Being concise is valuable.

Following these best practice steps will help keep your process simple and productive:

1. Gaining a deep understanding of any expectations

Before diving into your intended topic or beginning the research phase, take some time to orient yourself. Suppose there’s a specific topic assigned to you. In that case, it’s essential to deeply understand the question and organize your planning and approach in response. Pay attention to the key requirements and ensure you align your writing accordingly. 

This preparation step entails

Deeply understanding the task or assignment

Being clear about the expected format and length

Familiarizing yourself with the citation and referencing requirements 

Understanding any defined limits for your research contribution

Where applicable, speaking to your professor or research supervisor for further clarification

2. Choose your research topic

Select a research topic that aligns with both your interests and available resources. Ideally, focus on a field where you possess significant experience and analytical skills. In crafting your research paper, it's crucial to go beyond summarizing existing data and contribute fresh insights to the chosen area.

Consider narrowing your focus to a specific aspect of the topic. For example, if exploring the link between technology and mental health, delve into how social media use during the pandemic impacts the well-being of college students. Conducting interviews and surveys with students could provide firsthand data and unique perspectives, adding substantial value to the existing knowledge.

When finalizing your topic, adhere to legal and ethical norms in the relevant area (this ensures the integrity of your research, protects participants' rights, upholds intellectual property standards, and ensures transparency and accountability). Following these principles not only maintains the credibility of your work but also builds trust within your academic or professional community.

For instance, in writing about medical research, consider legal and ethical norms , including patient confidentiality laws and informed consent requirements. Similarly, if analyzing user data on social media platforms, be mindful of data privacy regulations, ensuring compliance with laws governing personal information collection and use. Aligning with legal and ethical standards not only avoids potential issues but also underscores the responsible conduct of your research.

3. Gather preliminary research

Once you’ve landed on your topic, it’s time to explore it further. You’ll want to discover more about available resources and existing research relevant to your assignment at this stage. 

This exploratory phase is vital as you may discover issues with your original idea or realize you have insufficient resources to explore the topic effectively. This key bit of groundwork allows you to redirect your research topic in a different, more feasible, or more relevant direction if necessary. 

Spending ample time at this stage ensures you gather everything you need, learn as much as you can about the topic, and discover gaps where the topic has yet to be sufficiently covered, offering an opportunity to research it further. 

4. Define your research question

To produce a well-structured and focused paper, it is imperative to formulate a clear and precise research question that will guide your work. Your research question must be informed by the existing literature and tailored to the scope and objectives of your project. By refining your focus, you can produce a thoughtful and engaging paper that effectively communicates your ideas to your readers.

5. Write a thesis statement

A thesis statement is a one-to-two-sentence summary of your research paper's main argument or direction. It serves as an overall guide to summarize the overall intent of the research paper for you and anyone wanting to know more about the research.

A strong thesis statement is:

Concise and clear: Explain your case in simple sentences (avoid covering multiple ideas). It might help to think of this section as an elevator pitch.

Specific: Ensure that there is no ambiguity in your statement and that your summary covers the points argued in the paper.

Debatable: A thesis statement puts forward a specific argument––it is not merely a statement but a debatable point that can be analyzed and discussed.

Here are three thesis statement examples from different disciplines:

Psychology thesis example: "We're studying adults aged 25-40 to see if taking short breaks for mindfulness can help with stress. Our goal is to find practical ways to manage anxiety better."

Environmental science thesis example: "This research paper looks into how having more city parks might make the air cleaner and keep people healthier. I want to find out if more green spaces means breathing fewer carcinogens in big cities."

UX research thesis example: "This study focuses on improving mobile banking for older adults using ethnographic research, eye-tracking analysis, and interactive prototyping. We investigate the usefulness of eye-tracking analysis with older individuals, aiming to spark debate and offer fresh perspectives on UX design and digital inclusivity for the aging population."

6. Conduct in-depth research

A research paper doesn’t just include research that you’ve uncovered from other papers and studies but your fresh insights, too. You will seek to become an expert on your topic––understanding the nuances in the current leading theories. You will analyze existing research and add your thinking and discoveries.  It's crucial to conduct well-designed research that is rigorous, robust, and based on reliable sources. Suppose a research paper lacks evidence or is biased. In that case, it won't benefit the academic community or the general public. Therefore, examining the topic thoroughly and furthering its understanding through high-quality research is essential. That usually means conducting new research. Depending on the area under investigation, you may conduct surveys, interviews, diary studies , or observational research to uncover new insights or bolster current claims.

7. Determine supporting evidence

Not every piece of research you’ve discovered will be relevant to your research paper. It’s important to categorize the most meaningful evidence to include alongside your discoveries. It's important to include evidence that doesn't support your claims to avoid exclusion bias and ensure a fair research paper.

8. Write a research paper outline

Before diving in and writing the whole paper, start with an outline. It will help you to see if more research is needed, and it will provide a framework by which to write a more compelling paper. Your supervisor may even request an outline to approve before beginning to write the first draft of the full paper. An outline will include your topic, thesis statement, key headings, short summaries of the research, and your arguments.

9. Write your first draft

Once you feel confident about your outline and sources, it’s time to write your first draft. While penning a long piece of content can be intimidating, if you’ve laid the groundwork, you will have a structure to help you move steadily through each section. To keep up motivation and inspiration, it’s often best to keep the pace quick. Stopping for long periods can interrupt your flow and make jumping back in harder than writing when things are fresh in your mind.

10. Cite your sources correctly

It's always a good practice to give credit where it's due, and the same goes for citing any works that have influenced your paper. Building your arguments on credible references adds value and authenticity to your research. In the formatting guidelines section, you’ll find an overview of different citation styles (MLA, CMOS, or APA), which will help you meet any publishing or academic requirements and strengthen your paper's credibility. It is essential to follow the guidelines provided by your school or the publication you are submitting to ensure the accuracy and relevance of your citations.

11. Ensure your work is original

It is crucial to ensure the originality of your paper, as plagiarism can lead to serious consequences. To avoid plagiarism, you should use proper paraphrasing and quoting techniques. Paraphrasing is rewriting a text in your own words while maintaining the original meaning. Quoting involves directly citing the source. Giving credit to the original author or source is essential whenever you borrow their ideas or words. You can also use plagiarism detection tools such as Scribbr or Grammarly to check the originality of your paper. These tools compare your draft writing to a vast database of online sources. If you find any accidental plagiarism, you should correct it immediately by rephrasing or citing the source.

12. Revise, edit, and proofread

One of the essential qualities of excellent writers is their ability to understand the importance of editing and proofreading. Even though it's tempting to call it a day once you've finished your writing, editing your work can significantly improve its quality. It's natural to overlook the weaker areas when you've just finished writing a paper. Therefore, it's best to take a break of a day or two, or even up to a week, to refresh your mind. This way, you can return to your work with a new perspective. After some breathing room, you can spot any inconsistencies, spelling and grammar errors, typos, or missing citations and correct them. 

• The best research paper format 

The format of your research paper should align with the requirements set forth by your college, school, or target publication. 

There is no one “best” format, per se. Depending on the stated requirements, you may need to include the following elements:

Title page: The title page of a research paper typically includes the title, author's name, and institutional affiliation and may include additional information such as a course name or instructor's name. 

Table of contents: Include a table of contents to make it easy for readers to find specific sections of your paper.

Abstract: The abstract is a summary of the purpose of the paper.

Methods : In this section, describe the research methods used. This may include collecting data , conducting interviews, or doing field research .

Results: Summarize the conclusions you drew from your research in this section.

Discussion: In this section, discuss the implications of your research . Be sure to mention any significant limitations to your approach and suggest areas for further research.

Tables, charts, and illustrations: Use tables, charts, and illustrations to help convey your research findings and make them easier to understand.

Works cited or reference page: Include a works cited or reference page to give credit to the sources that you used to conduct your research.

Bibliography: Provide a list of all the sources you consulted while conducting your research.

Dedication and acknowledgments : Optionally, you may include a dedication and acknowledgments section to thank individuals who helped you with your research.

• General style and formatting guidelines

Formatting your research paper means you can submit it to your college, journal, or other publications in compliance with their criteria.

Research papers tend to follow the American Psychological Association (APA), Modern Language Association (MLA), or Chicago Manual of Style (CMOS) guidelines.

Here’s how each style guide is typically used:

Chicago Manual of Style (CMOS):

CMOS is a versatile style guide used for various types of writing. It's known for its flexibility and use in the humanities. CMOS provides guidelines for citations, formatting, and overall writing style. It allows for both footnotes and in-text citations, giving writers options based on their preferences or publication requirements.

American Psychological Association (APA):

APA is common in the social sciences. It’s hailed for its clarity and emphasis on precision. It has specific rules for citing sources, creating references, and formatting papers. APA style uses in-text citations with an accompanying reference list. It's designed to convey information efficiently and is widely used in academic and scientific writing.

Modern Language Association (MLA):

MLA is widely used in the humanities, especially literature and language studies. It emphasizes the author-page format for in-text citations and provides guidelines for creating a "Works Cited" page. MLA is known for its focus on the author's name and the literary works cited. It’s frequently used in disciplines that prioritize literary analysis and critical thinking.

To confirm you're using the latest style guide, check the official website or publisher's site for updates, consult academic resources, and verify the guide's publication date. Online platforms and educational resources may also provide summaries and alerts about any revisions or additions to the style guide.

Citing sources

When working on your research paper, it's important to cite the sources you used properly. Your citation style will guide you through this process. Generally, there are three parts to citing sources in your research paper: 

First, provide a brief citation in the body of your essay. This is also known as a parenthetical or in-text citation. 

Second, include a full citation in the Reference list at the end of your paper. Different types of citations include in-text citations, footnotes, and reference lists. 

In-text citations include the author's surname and the date of the citation. 

Footnotes appear at the bottom of each page of your research paper. They may also be summarized within a reference list at the end of the paper. 

A reference list includes all of the research used within the paper at the end of the document. It should include the author, date, paper title, and publisher listed in the order that aligns with your citation style.

10 research paper writing tips:

Following some best practices is essential to writing a research paper that contributes to your field of study and creates a positive impact.

These tactics will help you structure your argument effectively and ensure your work benefits others:

Clear and precise language:  Ensure your language is unambiguous. Use academic language appropriately, but keep it simple. Also, provide clear takeaways for your audience.

Effective idea separation:  Organize the vast amount of information and sources in your paper with paragraphs and titles. Create easily digestible sections for your readers to navigate through.

Compelling intro:  Craft an engaging introduction that captures your reader's interest. Hook your audience and motivate them to continue reading.

Thorough revision and editing:  Take the time to review and edit your paper comprehensively. Use tools like Grammarly to detect and correct small, overlooked errors.

Thesis precision:  Develop a clear and concise thesis statement that guides your paper. Ensure that your thesis aligns with your research's overall purpose and contribution.

Logical flow of ideas:  Maintain a logical progression throughout the paper. Use transitions effectively to connect different sections and maintain coherence.

Critical evaluation of sources:  Evaluate and critically assess the relevance and reliability of your sources. Ensure that your research is based on credible and up-to-date information.

Thematic consistency:  Maintain a consistent theme throughout the paper. Ensure that all sections contribute cohesively to the overall argument.

Relevant supporting evidence:  Provide concise and relevant evidence to support your arguments. Avoid unnecessary details that may distract from the main points.

Embrace counterarguments:  Acknowledge and address opposing views to strengthen your position. Show that you have considered alternative arguments in your field.

7 research tips 

If you want your paper to not only be well-written but also contribute to the progress of human knowledge, consider these tips to take your paper to the next level:

Selecting the appropriate topic: The topic you select should align with your area of expertise, comply with the requirements of your project, and have sufficient resources for a comprehensive investigation.

Use academic databases: Academic databases such as PubMed, Google Scholar, and JSTOR offer a wealth of research papers that can help you discover everything you need to know about your chosen topic.

Critically evaluate sources: It is important not to accept research findings at face value. Instead, it is crucial to critically analyze the information to avoid jumping to conclusions or overlooking important details. A well-written research paper requires a critical analysis with thorough reasoning to support claims.

Diversify your sources: Expand your research horizons by exploring a variety of sources beyond the standard databases. Utilize books, conference proceedings, and interviews to gather diverse perspectives and enrich your understanding of the topic.

Take detailed notes: Detailed note-taking is crucial during research and can help you form the outline and body of your paper.

Stay up on trends: Keep abreast of the latest developments in your field by regularly checking for recent publications. Subscribe to newsletters, follow relevant journals, and attend conferences to stay informed about emerging trends and advancements. 

Engage in peer review: Seek feedback from peers or mentors to ensure the rigor and validity of your research . Peer review helps identify potential weaknesses in your methodology and strengthens the overall credibility of your findings.

• The real-world impact of research papers

Writing a research paper is more than an academic or business exercise. The experience provides an opportunity to explore a subject in-depth, broaden one's understanding, and arrive at meaningful conclusions. With careful planning, dedication, and hard work, writing a research paper can be a fulfilling and enriching experience contributing to advancing knowledge.

How do I publish my research paper? 

Many academics wish to publish their research papers. While challenging, your paper might get traction if it covers new and well-written information. To publish your research paper, find a target publication, thoroughly read their guidelines, format your paper accordingly, and send it to them per their instructions. You may need to include a cover letter, too. After submission, your paper may be peer-reviewed by experts to assess its legitimacy, quality, originality, and methodology. Following review, you will be informed by the publication whether they have accepted or rejected your paper. 

What is a good opening sentence for a research paper? 

Beginning your research paper with a compelling introduction can ensure readers are interested in going further. A relevant quote, a compelling statistic, or a bold argument can start the paper and hook your reader. Remember, though, that the most important aspect of a research paper is the quality of the information––not necessarily your ability to storytell, so ensure anything you write aligns with your goals.

Research paper vs. a research proposal—what’s the difference?

While some may confuse research papers and proposals, they are different documents. 

A research proposal comes before a research paper. It is a detailed document that outlines an intended area of exploration. It includes the research topic, methodology, timeline, sources, and potential conclusions. Research proposals are often required when seeking approval to conduct research. 

A research paper is a summary of research findings. A research paper follows a structured format to present those findings and construct an argument or conclusion.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

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Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your topic and get the reader interested
• Provide background or summarize existing research
• Position your own approach
• Detail your specific research problem and problem statement
• Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

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Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

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This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

• What research gap is your work intended to fill?
• What limitations in previous work does it address?
• What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

• This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
• We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

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The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

• This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
• This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

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Writing your research paper

How to start your research paper [step-by-step guide]

All research papers have pretty much the same structure. If you can write one type of research paper, you can write another. Learn the steps to start and complete your research paper in our guide.

How to write a problem statement

What is a problem statement and how do you write one? This guide answers all your questions and includes an example of a problem statement.

How to write a research paper outline

Structuring the outline of your research paper early on is important. Read on to learn how to structure a research paper outline and to see examples, including an outline template.

How to write a research proposal

What is a research proposal and what should you use it for? This step-by-step guide teaches you how to structure and write a research proposal.

How to write an abstract

Not sure how to write an abstract for your paper? Clear your doubts with this guide, follow our tips, and start writing your abstract!

What are the different types of research papers?

Learn all you need to know about research papers, what differentiates a research paper from a thesis, and what types of research papers there are.

What is a research paper?

Are you confused about what a research paper actually is and what differentiates it from a thesis? Clarify it once and for all with our guide.

What is an appendix in a paper

Not sure what an appendix in a paper is? This guides defines what an appendix is and how to format one.

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Not sure what the abstract of a paper is? Learn the definition of an abstract and how to format one in this guide with resources.

Writing a Research Paper

This page lists some of the stages involved in writing a library-based research paper.

Although this list suggests that there is a simple, linear process to writing such a paper, the actual process of writing a research paper is often a messy and recursive one, so please use this outline as a flexible guide.

Discovering, Narrowing, and Focusing a Researchable Topic

• Try to find a topic that truly interests you
• Try writing your way to a topic
• Talk with your course instructor and classmates about your topic
• Pose your topic as a question to be answered or a problem to be solved

Finding, Selecting, and Reading Sources

You will need to look at the following types of sources:

• library catalog, periodical indexes, bibliographies, suggestions from your instructor
• primary vs. secondary sources
• journals, books, other documents

Grouping, Sequencing, and Documenting Information

The following systems will help keep you organized:

• a system for noting sources on bibliography cards
• a system for organizing material according to its relative importance
• a system for taking notes

Writing an Outline and a Prospectus for Yourself

Consider the following questions:

• What is the topic?
• Why is it significant?
• What background material is relevant?
• What is my thesis or purpose statement?
• What organizational plan will best support my purpose?

Writing the Introduction

In the introduction you will need to do the following things:

• present relevant background or contextual material
• define terms or concepts when necessary
• explain the focus of the paper and your specific purpose
• reveal your plan of organization

Writing the Body

• Use your outline and prospectus as flexible guides
• Build your essay around points you want to make (i.e., don’t let your sources organize your paper)
• Integrate your sources into your discussion
• Summarize, analyze, explain, and evaluate published work rather than merely reporting it
• Move up and down the “ladder of abstraction” from generalization to varying levels of detail back to generalization

Writing the Conclusion

• If the argument or point of your paper is complex, you may need to summarize the argument for your reader.
• If prior to your conclusion you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to add your points up, to explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction.
• Perhaps suggest what about this topic needs further research.

Revising the Final Draft

• Check overall organization : logical flow of introduction, coherence and depth of discussion in body, effectiveness of conclusion.
• Paragraph level concerns : topic sentences, sequence of ideas within paragraphs, use of details to support generalizations, summary sentences where necessary, use of transitions within and between paragraphs.
• Sentence level concerns: sentence structure, word choices, punctuation, spelling.
• Documentation: consistent use of one system, citation of all material not considered common knowledge, appropriate use of endnotes or footnotes, accuracy of list of works cited.

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How to write your first research paper.

Writing a research manuscript is an intimidating process for many novice writers in the sciences. One of the stumbling blocks is the beginning of the process and creating the first draft. This paper presents guidelines on how to initiate the writing process and draft each section of a research manuscript. The paper discusses seven rules that allow the writer to prepare a well-structured and comprehensive manuscript for a publication submission. In addition, the author lists different strategies for successful revision. Each of those strategies represents a step in the revision process and should help the writer improve the quality of the manuscript. The paper could be considered a brief manual for publication.

It is late at night. You have been struggling with your project for a year. You generated an enormous amount of interesting data. Your pipette feels like an extension of your hand, and running western blots has become part of your daily routine, similar to brushing your teeth. Your colleagues think you are ready to write a paper, and your lab mates tease you about your “slow” writing progress. Yet days pass, and you cannot force yourself to sit down to write. You have not written anything for a while (lab reports do not count), and you feel you have lost your stamina. How does the writing process work? How can you fit your writing into a daily schedule packed with experiments? What section should you start with? What distinguishes a good research paper from a bad one? How should you revise your paper? These and many other questions buzz in your head and keep you stressed. As a result, you procrastinate. In this paper, I will discuss the issues related to the writing process of a scientific paper. Specifically, I will focus on the best approaches to start a scientific paper, tips for writing each section, and the best revision strategies.

1. Schedule your writing time in Outlook

Whether you have written 100 papers or you are struggling with your first, starting the process is the most difficult part unless you have a rigid writing schedule. Writing is hard. It is a very difficult process of intense concentration and brain work. As stated in Hayes’ framework for the study of writing: “It is a generative activity requiring motivation, and it is an intellectual activity requiring cognitive processes and memory” [ 1 ]. In his book How to Write a Lot: A Practical Guide to Productive Academic Writing , Paul Silvia says that for some, “it’s easier to embalm the dead than to write an article about it” [ 2 ]. Just as with any type of hard work, you will not succeed unless you practice regularly. If you have not done physical exercises for a year, only regular workouts can get you into good shape again. The same kind of regular exercises, or I call them “writing sessions,” are required to be a productive author. Choose from 1- to 2-hour blocks in your daily work schedule and consider them as non-cancellable appointments. When figuring out which blocks of time will be set for writing, you should select the time that works best for this type of work. For many people, mornings are more productive. One Yale University graduate student spent a semester writing from 8 a.m. to 9 a.m. when her lab was empty. At the end of the semester, she was amazed at how much she accomplished without even interrupting her regular lab hours. In addition, doing the hardest task first thing in the morning contributes to the sense of accomplishment during the rest of the day. This positive feeling spills over into our work and life and has a very positive effect on our overall attitude.

Rule 1: Create regular time blocks for writing as appointments in your calendar and keep these appointments.

2. start with an outline.

Now that you have scheduled time, you need to decide how to start writing. The best strategy is to start with an outline. This will not be an outline that you are used to, with Roman numerals for each section and neat parallel listing of topic sentences and supporting points. This outline will be similar to a template for your paper. Initially, the outline will form a structure for your paper; it will help generate ideas and formulate hypotheses. Following the advice of George M. Whitesides, “. . . start with a blank piece of paper, and write down, in any order, all important ideas that occur to you concerning the paper” [ 3 ]. Use Table 1 as a starting point for your outline. Include your visuals (figures, tables, formulas, equations, and algorithms), and list your findings. These will constitute the first level of your outline, which will eventually expand as you elaborate.

The next stage is to add context and structure. Here you will group all your ideas into sections: Introduction, Methods, Results, and Discussion/Conclusion ( Table 2 ). This step will help add coherence to your work and sift your ideas.

Now that you have expanded your outline, you are ready for the next step: discussing the ideas for your paper with your colleagues and mentor. Many universities have a writing center where graduate students can schedule individual consultations and receive assistance with their paper drafts. Getting feedback during early stages of your draft can save a lot of time. Talking through ideas allows people to conceptualize and organize thoughts to find their direction without wasting time on unnecessary writing. Outlining is the most effective way of communicating your ideas and exchanging thoughts. Moreover, it is also the best stage to decide to which publication you will submit the paper. Many people come up with three choices and discuss them with their mentors and colleagues. Having a list of journal priorities can help you quickly resubmit your paper if your paper is rejected.

Rule 2: Create a detailed outline and discuss it with your mentor and peers.

3. continue with drafts.

After you get enough feedback and decide on the journal you will submit to, the process of real writing begins. Copy your outline into a separate file and expand on each of the points, adding data and elaborating on the details. When you create the first draft, do not succumb to the temptation of editing. Do not slow down to choose a better word or better phrase; do not halt to improve your sentence structure. Pour your ideas into the paper and leave revision and editing for later. As Paul Silvia explains, “Revising while you generate text is like drinking decaffeinated coffee in the early morning: noble idea, wrong time” [ 2 ].

Many students complain that they are not productive writers because they experience writer’s block. Staring at an empty screen is frustrating, but your screen is not really empty: You have a template of your article, and all you need to do is fill in the blanks. Indeed, writer’s block is a logical fallacy for a scientist ― it is just an excuse to procrastinate. When scientists start writing a research paper, they already have their files with data, lab notes with materials and experimental designs, some visuals, and tables with results. All they need to do is scrutinize these pieces and put them together into a comprehensive paper.

3.1. Starting with Materials and Methods

If you still struggle with starting a paper, then write the Materials and Methods section first. Since you have all your notes, it should not be problematic for you to describe the experimental design and procedures. Your most important goal in this section is to be as explicit as possible by providing enough detail and references. In the end, the purpose of this section is to allow other researchers to evaluate and repeat your work. So do not run into the same problems as the writers of the sentences in (1):

1a. Bacteria were pelleted by centrifugation. 1b. To isolate T cells, lymph nodes were collected.

As you can see, crucial pieces of information are missing: the speed of centrifuging your bacteria, the time, and the temperature in (1a); the source of lymph nodes for collection in (b). The sentences can be improved when information is added, as in (2a) and (2b), respectfully:

2a. Bacteria were pelleted by centrifugation at 3000g for 15 min at 25°C. 2b. To isolate T cells, mediastinal and mesenteric lymph nodes from Balb/c mice were collected at day 7 after immunization with ovabumin.

If your method has previously been published and is well-known, then you should provide only the literature reference, as in (3a). If your method is unpublished, then you need to make sure you provide all essential details, as in (3b).

3a. Stem cells were isolated, according to Johnson [23]. 3b. Stem cells were isolated using biotinylated carbon nanotubes coated with anti-CD34 antibodies.

Furthermore, cohesion and fluency are crucial in this section. One of the malpractices resulting in disrupted fluency is switching from passive voice to active and vice versa within the same paragraph, as shown in (4). This switching misleads and distracts the reader.

4. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. We took ratings of enjoyment, mood, and arousal as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness [ 4 ].

The problem with (4) is that the reader has to switch from the point of view of the experiment (passive voice) to the point of view of the experimenter (active voice). This switch causes confusion about the performer of the actions in the first and the third sentences. To improve the coherence and fluency of the paragraph above, you should be consistent in choosing the point of view: first person “we” or passive voice [ 5 ]. Let’s consider two revised examples in (5).

5a. We programmed behavioral computer-based experiments of Study 1 by using E-Prime. We took ratings of enjoyment, mood, and arousal by using Visual Analogue Scales (SI Methods) as the patients listened to preferred pleasant music and unpreferred music. We operationalized the preferred and unpreferred status of the music along a continuum of pleasantness. 5b. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. Ratings of enjoyment, mood, and arousal were taken as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness.

If you choose the point of view of the experimenter, then you may end up with repetitive “we did this” sentences. For many readers, paragraphs with sentences all beginning with “we” may also sound disruptive. So if you choose active sentences, you need to keep the number of “we” subjects to a minimum and vary the beginnings of the sentences [ 6 ].

Interestingly, recent studies have reported that the Materials and Methods section is the only section in research papers in which passive voice predominantly overrides the use of the active voice [ 5 , 7 , 8 , 9 ]. For example, Martínez shows a significant drop in active voice use in the Methods sections based on the corpus of 1 million words of experimental full text research articles in the biological sciences [ 7 ]. According to the author, the active voice patterned with “we” is used only as a tool to reveal personal responsibility for the procedural decisions in designing and performing experimental work. This means that while all other sections of the research paper use active voice, passive voice is still the most predominant in Materials and Methods sections.

Writing Materials and Methods sections is a meticulous and time consuming task requiring extreme accuracy and clarity. This is why when you complete your draft, you should ask for as much feedback from your colleagues as possible. Numerous readers of this section will help you identify the missing links and improve the technical style of this section.

Rule 3: Be meticulous and accurate in describing the Materials and Methods. Do not change the point of view within one paragraph.

3.2. writing results section.

For many authors, writing the Results section is more intimidating than writing the Materials and Methods section . If people are interested in your paper, they are interested in your results. That is why it is vital to use all your writing skills to objectively present your key findings in an orderly and logical sequence using illustrative materials and text.

Your Results should be organized into different segments or subsections where each one presents the purpose of the experiment, your experimental approach, data including text and visuals (tables, figures, schematics, algorithms, and formulas), and data commentary. For most journals, your data commentary will include a meaningful summary of the data presented in the visuals and an explanation of the most significant findings. This data presentation should not repeat the data in the visuals, but rather highlight the most important points. In the “standard” research paper approach, your Results section should exclude data interpretation, leaving it for the Discussion section. However, interpretations gradually and secretly creep into research papers: “Reducing the data, generalizing from the data, and highlighting scientific cases are all highly interpretive processes. It should be clear by now that we do not let the data speak for themselves in research reports; in summarizing our results, we interpret them for the reader” [ 10 ]. As a result, many journals including the Journal of Experimental Medicine and the Journal of Clinical Investigation use joint Results/Discussion sections, where results are immediately followed by interpretations.

Another important aspect of this section is to create a comprehensive and supported argument or a well-researched case. This means that you should be selective in presenting data and choose only those experimental details that are essential for your reader to understand your findings. You might have conducted an experiment 20 times and collected numerous records, but this does not mean that you should present all those records in your paper. You need to distinguish your results from your data and be able to discard excessive experimental details that could distract and confuse the reader. However, creating a picture or an argument should not be confused with data manipulation or falsification, which is a willful distortion of data and results. If some of your findings contradict your ideas, you have to mention this and find a plausible explanation for the contradiction.

In addition, your text should not include irrelevant and peripheral information, including overview sentences, as in (6).

6. To show our results, we first introduce all components of experimental system and then describe the outcome of infections.

Indeed, wordiness convolutes your sentences and conceals your ideas from readers. One common source of wordiness is unnecessary intensifiers. Adverbial intensifiers such as “clearly,” “essential,” “quite,” “basically,” “rather,” “fairly,” “really,” and “virtually” not only add verbosity to your sentences, but also lower your results’ credibility. They appeal to the reader’s emotions but lower objectivity, as in the common examples in (7):

7a. Table 3 clearly shows that … 7b. It is obvious from figure 4 that …

Another source of wordiness is nominalizations, i.e., nouns derived from verbs and adjectives paired with weak verbs including “be,” “have,” “do,” “make,” “cause,” “provide,” and “get” and constructions such as “there is/are.”

8a. We tested the hypothesis that there is a disruption of membrane asymmetry. 8b. In this paper we provide an argument that stem cells repopulate injured organs.

In the sentences above, the abstract nominalizations “disruption” and “argument” do not contribute to the clarity of the sentences, but rather clutter them with useless vocabulary that distracts from the meaning. To improve your sentences, avoid unnecessary nominalizations and change passive verbs and constructions into active and direct sentences.

9a. We tested the hypothesis that the membrane asymmetry is disrupted. 9b. In this paper we argue that stem cells repopulate injured organs.

Your Results section is the heart of your paper, representing a year or more of your daily research. So lead your reader through your story by writing direct, concise, and clear sentences.

Rule 4: Be clear, concise, and objective in describing your Results.

3.3. now it is time for your introduction.

Now that you are almost half through drafting your research paper, it is time to update your outline. While describing your Methods and Results, many of you diverged from the original outline and re-focused your ideas. So before you move on to create your Introduction, re-read your Methods and Results sections and change your outline to match your research focus. The updated outline will help you review the general picture of your paper, the topic, the main idea, and the purpose, which are all important for writing your introduction.

The best way to structure your introduction is to follow the three-move approach shown in Table 3 .

Adapted from Swales and Feak [ 11 ].

The moves and information from your outline can help to create your Introduction efficiently and without missing steps. These moves are traffic signs that lead the reader through the road of your ideas. Each move plays an important role in your paper and should be presented with deep thought and care. When you establish the territory, you place your research in context and highlight the importance of your research topic. By finding the niche, you outline the scope of your research problem and enter the scientific dialogue. The final move, “occupying the niche,” is where you explain your research in a nutshell and highlight your paper’s significance. The three moves allow your readers to evaluate their interest in your paper and play a significant role in the paper review process, determining your paper reviewers.

Some academic writers assume that the reader “should follow the paper” to find the answers about your methodology and your findings. As a result, many novice writers do not present their experimental approach and the major findings, wrongly believing that the reader will locate the necessary information later while reading the subsequent sections [ 5 ]. However, this “suspense” approach is not appropriate for scientific writing. To interest the reader, scientific authors should be direct and straightforward and present informative one-sentence summaries of the results and the approach.

Another problem is that writers understate the significance of the Introduction. Many new researchers mistakenly think that all their readers understand the importance of the research question and omit this part. However, this assumption is faulty because the purpose of the section is not to evaluate the importance of the research question in general. The goal is to present the importance of your research contribution and your findings. Therefore, you should be explicit and clear in describing the benefit of the paper.

The Introduction should not be long. Indeed, for most journals, this is a very brief section of about 250 to 600 words, but it might be the most difficult section due to its importance.

Rule 5: Interest your reader in the Introduction section by signalling all its elements and stating the novelty of the work.

3.4. discussion of the results.

For many scientists, writing a Discussion section is as scary as starting a paper. Most of the fear comes from the variation in the section. Since every paper has its unique results and findings, the Discussion section differs in its length, shape, and structure. However, some general principles of writing this section still exist. Knowing these rules, or “moves,” can change your attitude about this section and help you create a comprehensive interpretation of your results.

The purpose of the Discussion section is to place your findings in the research context and “to explain the meaning of the findings and why they are important, without appearing arrogant, condescending, or patronizing” [ 11 ]. The structure of the first two moves is almost a mirror reflection of the one in the Introduction. In the Introduction, you zoom in from general to specific and from the background to your research question; in the Discussion section, you zoom out from the summary of your findings to the research context, as shown in Table 4 .

Adapted from Swales and Feak and Hess [ 11 , 12 ].

The biggest challenge for many writers is the opening paragraph of the Discussion section. Following the moves in Table 1 , the best choice is to start with the study’s major findings that provide the answer to the research question in your Introduction. The most common starting phrases are “Our findings demonstrate . . .,” or “In this study, we have shown that . . .,” or “Our results suggest . . .” In some cases, however, reminding the reader about the research question or even providing a brief context and then stating the answer would make more sense. This is important in those cases where the researcher presents a number of findings or where more than one research question was presented. Your summary of the study’s major findings should be followed by your presentation of the importance of these findings. One of the most frequent mistakes of the novice writer is to assume the importance of his findings. Even if the importance is clear to you, it may not be obvious to your reader. Digesting the findings and their importance to your reader is as crucial as stating your research question.

Another useful strategy is to be proactive in the first move by predicting and commenting on the alternative explanations of the results. Addressing potential doubts will save you from painful comments about the wrong interpretation of your results and will present you as a thoughtful and considerate researcher. Moreover, the evaluation of the alternative explanations might help you create a logical step to the next move of the discussion section: the research context.

The goal of the research context move is to show how your findings fit into the general picture of the current research and how you contribute to the existing knowledge on the topic. This is also the place to discuss any discrepancies and unexpected findings that may otherwise distort the general picture of your paper. Moreover, outlining the scope of your research by showing the limitations, weaknesses, and assumptions is essential and adds modesty to your image as a scientist. However, make sure that you do not end your paper with the problems that override your findings. Try to suggest feasible explanations and solutions.

If your submission does not require a separate Conclusion section, then adding another paragraph about the “take-home message” is a must. This should be a general statement reiterating your answer to the research question and adding its scientific implications, practical application, or advice.

Just as in all other sections of your paper, the clear and precise language and concise comprehensive sentences are vital. However, in addition to that, your writing should convey confidence and authority. The easiest way to illustrate your tone is to use the active voice and the first person pronouns. Accompanied by clarity and succinctness, these tools are the best to convince your readers of your point and your ideas.

Rule 6: Present the principles, relationships, and generalizations in a concise and convincing tone.

4. choosing the best working revision strategies.

Now that you have created the first draft, your attitude toward your writing should have improved. Moreover, you should feel more confident that you are able to accomplish your project and submit your paper within a reasonable timeframe. You also have worked out your writing schedule and followed it precisely. Do not stop ― you are only at the midpoint from your destination. Just as the best and most precious diamond is no more than an unattractive stone recognized only by trained professionals, your ideas and your results may go unnoticed if they are not polished and brushed. Despite your attempts to present your ideas in a logical and comprehensive way, first drafts are frequently a mess. Use the advice of Paul Silvia: “Your first drafts should sound like they were hastily translated from Icelandic by a non-native speaker” [ 2 ]. The degree of your success will depend on how you are able to revise and edit your paper.

The revision can be done at the macrostructure and the microstructure levels [ 13 ]. The macrostructure revision includes the revision of the organization, content, and flow. The microstructure level includes individual words, sentence structure, grammar, punctuation, and spelling.

The best way to approach the macrostructure revision is through the outline of the ideas in your paper. The last time you updated your outline was before writing the Introduction and the Discussion. Now that you have the beginning and the conclusion, you can take a bird’s-eye view of the whole paper. The outline will allow you to see if the ideas of your paper are coherently structured, if your results are logically built, and if the discussion is linked to the research question in the Introduction. You will be able to see if something is missing in any of the sections or if you need to rearrange your information to make your point.

The next step is to revise each of the sections starting from the beginning. Ideally, you should limit yourself to working on small sections of about five pages at a time [ 14 ]. After these short sections, your eyes get used to your writing and your efficiency in spotting problems decreases. When reading for content and organization, you should control your urge to edit your paper for sentence structure and grammar and focus only on the flow of your ideas and logic of your presentation. Experienced researchers tend to make almost three times the number of changes to meaning than novice writers [ 15 , 16 ]. Revising is a difficult but useful skill, which academic writers obtain with years of practice.

In contrast to the macrostructure revision, which is a linear process and is done usually through a detailed outline and by sections, microstructure revision is a non-linear process. While the goal of the macrostructure revision is to analyze your ideas and their logic, the goal of the microstructure editing is to scrutinize the form of your ideas: your paragraphs, sentences, and words. You do not need and are not recommended to follow the order of the paper to perform this type of revision. You can start from the end or from different sections. You can even revise by reading sentences backward, sentence by sentence and word by word.

One of the microstructure revision strategies frequently used during writing center consultations is to read the paper aloud [ 17 ]. You may read aloud to yourself, to a tape recorder, or to a colleague or friend. When reading and listening to your paper, you are more likely to notice the places where the fluency is disrupted and where you stumble because of a very long and unclear sentence or a wrong connector.

Another revision strategy is to learn your common errors and to do a targeted search for them [ 13 ]. All writers have a set of problems that are specific to them, i.e., their writing idiosyncrasies. Remembering these problems is as important for an academic writer as remembering your friends’ birthdays. Create a list of these idiosyncrasies and run a search for these problems using your word processor. If your problem is demonstrative pronouns without summary words, then search for “this/these/those” in your text and check if you used the word appropriately. If you have a problem with intensifiers, then search for “really” or “very” and delete them from the text. The same targeted search can be done to eliminate wordiness. Searching for “there is/are” or “and” can help you avoid the bulky sentences.

The final strategy is working with a hard copy and a pencil. Print a double space copy with font size 14 and re-read your paper in several steps. Try reading your paper line by line with the rest of the text covered with a piece of paper. When you are forced to see only a small portion of your writing, you are less likely to get distracted and are more likely to notice problems. You will end up spotting more unnecessary words, wrongly worded phrases, or unparallel constructions.

After you apply all these strategies, you are ready to share your writing with your friends, colleagues, and a writing advisor in the writing center. Get as much feedback as you can, especially from non-specialists in your field. Patiently listen to what others say to you ― you are not expected to defend your writing or explain what you wanted to say. You may decide what you want to change and how after you receive the feedback and sort it in your head. Even though some researchers make the revision an endless process and can hardly stop after a 14th draft; having from five to seven drafts of your paper is a norm in the sciences. If you can’t stop revising, then set a deadline for yourself and stick to it. Deadlines always help.

Rule 7: Revise your paper at the macrostructure and the microstructure level using different strategies and techniques. Receive feedback and revise again.

5. it is time to submit.

It is late at night again. You are still in your lab finishing revisions and getting ready to submit your paper. You feel happy ― you have finally finished a year’s worth of work. You will submit your paper tomorrow, and regardless of the outcome, you know that you can do it. If one journal does not take your paper, you will take advantage of the feedback and resubmit again. You will have a publication, and this is the most important achievement.

What is even more important is that you have your scheduled writing time that you are going to keep for your future publications, for reading and taking notes, for writing grants, and for reviewing papers. You are not going to lose stamina this time, and you will become a productive scientist. But for now, let’s celebrate the end of the paper.

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Online Guide to Writing and Research

The research process, explore more of umgc.

• Online Guide to Writing

Planning and Writing a Research Paper

Work Your Sources into Your Research Writing

Working your sources into your writing is a very important part of the writing process and gets easier over time.  You must also decide whether you will quote , paraphrase , or summarize the material when incorporating resources into your writing. 

Academic integrity encompasses the practice of engaging with source material meaningfully and ethically, to the benefit of your own learning and the discourse community with which you interact.  UMGC has carefully developed a philosophy of, approach to, and tutorial about academic integrity that can be found here: Philosophy of Academic Integrity   Please review this material and familiarize yourself with both the best practices in this area and how to avoid running afoul of expectations.

Quoting, Paraphrasing, Summarizing, and Citing Your Sources

How to incorporate your sources.

How you incorporate your sources into your writing depends on how you are using them and why you are writing your paper. Many students have difficulty deciding when to quote, paraphrase, or summarize, and then when to cite a source. 

Understanding Why We Use Citations

Understanding why writers use citations in academic research can help you decide when to use them.  Citing reliable sources gives your research and writing credibility, showing your familiarity with the work of a scholarly community and your understanding of how you are contributing to it.  It also shows the reader that you have done the research and have gone to great lengths to make your paper as strong and clear as possible.  

How to Work Citations and Paraphrasing Into Your Own Writing

Keep in mind that sometimes it is difficult to figure out how to work the quotations and paraphrases into your own style of writing. You want to avoid using lengthy blocks of quotations or lengthy paraphrases of the sources. For more information about quoting and paraphrasing resources, check out Chapter 5, “ Academic Integrity and Documentation .”  Also, please take a look at the UMGC library Citing and Writing LibGuide .

Research Styles

• OBJECTIVE RESEARCHER
• CONTEXT CREATOR

At this level, you are expected to remain objective and impartial when presenting the research, with no personal opinions given. You report the information, taking on the role of an experimental researcher or even an investigative reporter. 

Here, you are expected to put your sources in the context of a greater issue or debate. You have to offer enough explanation and discussion (through your own comprehension and interpretation) to help your reader see the connection between the material you are researching and the other references. 

At this level, you help the reader understand the relationship, significance, and authority of the reference material by introducing and discussing its sources.

Here, you are asked to judge the source materials and their usefulness for your research project. This last position, most commonly found in literary, musical, or other fine arts criticism, involves you, the researcher, as a critical thinker in assessing the sources. 

Key Takeaways

• Acknowledging intellectual ownership shows respect for those who have contributed to the field of knowledge and for the achievements in that field.
• Citing reliable sources gives your research and writing credibility, showing your familiarity with the work of a scholarly community and your understanding of how you are contributing to it.

Mailing Address: 3501 University Blvd. East, Adelphi, MD 20783 This work is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . © 2022 UMGC. All links to external sites were verified at the time of publication. UMGC is not responsible for the validity or integrity of information located at external sites.

Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Nature of Research

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

Writing Handbooks

Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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• Academic Writing / APA

The Importance of Formatting

by Purdue Global Academic Success Center and Writing Center · Published May 13, 2015 · Updated October 23, 2019

Dr. Tamara Fudge, professor in the School of Business and IT, Kaplan University

Too often students complain that I am too tough on them for not following APA formatting . Lest anyone think this post is an apology, I will disappoint you! Formatting is important .

Sure, there are PowerPoints, podcasts, and other kinds of assignments, but most papers written in my school are to be completed using APA style, which was developed by the American Psychological Association. This method defines not just how sources are to be cited and referenced, but how the paper should look overall, including the size of margins, how far to indent first lines of paragraphs, where page numbers are placed, and more.

There are other formats , too, including but not limited to MLA (from the Modern Language Association) and Chicago (short for Chicago Manual of Style). Each style is picky about how words are placed on the page. It’s not that APA is any better than the others; it just happens to be the method of choice for my situation. The important thing is that there is a declared standard .

Why is formatting so important that I will dare to take a point or two off when it’s not followed?

• It demonstrates that you can follow instructions . If you were a hiring manager, you would not want to hire someone who either doesn’t, won’t, or can’t follow directions.
• It provides consistency . Your readers, whether they are your professors, your boss and coworkers, or your clients, won’t have to guess how you organized your ideas.
• It facilitates practice of discipline and adherence to standards . I can’t think of a field that doesn’t have some set of standards, such as how to meet web accessibility issues, provide network security, or maintain HIPAA requirements. Learning to stick to standards takes practice.
• It allows you to focus your efforts on content. There are no surprises in how you create a cover page or put the reference list together when you use an established method. Once you are used to the methodology – seriously, it’s not that difficult – you can spend the bulk of your writing time researching and organizing ideas into words.

Knowing how to use a prescribed formatting style can also help you excel in your career.   For example, one of the ways to move upward in your chosen field is to become a published author; journals typically require a format and may summarily reject any submissions not meeting their standards . It’s also important to know that one of the reasons companies sometimes lose out on grants is that the writers didn’t follow posted guidelines (“The Top Five Reasons Grant Applications Are Rejected”, n.d.).

Legal documents also have very specific formatting. According to an article regarding California civil procedures, “there is a rule for everything … right down to the type of paper to use and the requirement to hole-punch your pleadings” (Haubrich-Hass, 2012, para. 1). I’ve been told that deviating from the requirements might well have an unhappy ending for the lawyer’s client.

My insistence on following rules should only make students stronger candidates for the workplace of their choice. They learn to follow instructions, provide consistency, practice discipline , and then have the ability to focus on content when they have mastered formatting. If I don’t insist on adherence to the rules, I fail to teach these things to my students. And so again, without apology, I declare that formatting is important !

Haubrich-Hass, B. (2012). Formatting California proceedings. Retrieved from  https://thecalifornialitigator.com/pleadings/formatting-california-pleadings-2/

The top five reasons grant applications are rejected. (n.d.). Retrieved from https://www.grantgopher.com/news/articletype/articleview/articleid/1153/the-top-five-reasons-grant-applications-are-rejected.aspx

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Reblogged this on Empowered Composition and commented: How can learning citation guidelines empower students? Dr. Fudge has some great reasons.

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Writing an Effective Research Paper: Structure & Content

Essential Guidelines for Structuring a Research Paper

Lecturer: kevin j. heintz, m.a. english.

This lecture was presented at ChungAng University in Seoul, South Korea in November 2018. Wordvice/Essay Review Managing Editor Kevin J. Heintz explains how to organize and compose a research manuscript that will get your study published in top journals.

Even researchers whose first language is English must learn some specific rules and follow some standard conventions when writing research papers. This takes a completely different skillset than essay writing or sending emails to your professors and friends, and therefore it is a good idea for every researcher to keep learning how to improve research writing.

Research is about more than just the scientific principles and discoveries you are making—it is about sharing these discoveries with fellow researchers and with the public. And to do this, researchers must publish their work in journals. Strong writing is key to making your research more accessible and powerful, and therefore this presentation is not about the rigors of research, but the demands of research writing. The methods and information in this lecture can be applied to almost any kind of research paper, although of course the exact structure and content will be somewhat determined by where you are submitting your research.

Lecture Content

• Overview of Research Paper Writing
• The Structure of a Research Paper
• Composing Your Paper Sections
• Tips for Improving Quality of Writing

*Quizzes are given throughout the lecture to test your comprehension and understanding.

Research Paper Structure Overview

“what should a research paper do”.

• Share the knowledge you have gained about a specific area of study with other researchers
• Show how your study fits into current science.
• Inform the public about important scientific activity.
• Explain clearly and succinctly the context of your study, including relevant literature (Introduction), the methods used for research and analysis (Methods), the findings of your study (Results), and the implications for these results and further research that might be needed (Discussion and Conclusion).

“What are the most important factors to consider when writing a research paper?”

The research you conduct should of course be novel, timely, rigorous, and hopefully interesting. But you must also transmit your scientific research into  writing —a well-written paper will greatly improve your chances of getting accepted into journals. Here is an overview of the factors that help create quality writing in a research paper:

• All of the parts of your paper should fit together in an order that makes sense.
• Include all necessary information in each section needed to understand the other sections.
• Do not repeat information unless it is necessary.
• Ensure that your sentences are grammatically and logically coherent.

Organization

• Most scientific papers follow the  IMRD  structure—be sure to put the right parts in the right section (e.g., don’t include the literature review in the Methods section).
• As you do research you will notice that there are a great many pieces of information and data you COULD include in your paper. However, you need to conform to length guidelines and keep your paper focused. Therefore, you should be sure that you are choosing a proper number of items to focus on for each section.
• For example, if your study has 10 results but your paper can only be 4,000 words, you might want to narrow down these results to only those that support your hypothesis, perhaps the 3-5 most important results.
• The same applies to the Introduction, where you must choose what background, context, and relevant literature to include. Be sure to only include information that gives readers a focused and relevant understanding of your area of study.
• Clarity is related to coherence, organization, and relevance. It means ensuring that each paragraph and sentence in your paper is natural and easy to read and understand: proper grammar, phrasing, and style are key to writing a paper that is readable and comprehensible to both experts and possibly non-experts, depending on your target audience.
• Perhaps the most important rule is to  conform to the formatting guidelines and other style conventions of the journal to which you are submitting.  Check the “GUIDE FOR AUTHORS” section of the journal or conference, or if the paper is for a class, ensure that you are using the proper formatting requirements. Here is one handy site:  OWL—Online Writing Lab at Purdue University

Research Paper Structure

The general structure of scientific research papers is IMR&D (Introduction, Methods, Results, and Discussion). The information moves from broad to specific to broad again as seen in this diagram, the Introduction and Discussion taking up the most room in your paper and the Methods and Results usually being the shortest ad most focused sections. However, the order in which you write your paper will not be the same as the final order of the information. Let’s first look briefly at what each section does and then discuss how to organize and compose your work.

Introduction Section

What does it do.

*Discusses the problem to be solved (purpose statement)

*Describes where your research fits into the current science (background and context)

*Uses primary literature with citations and summarizes the current understanding of the problem (“literature review”)

When do you write it?

*Write it last—after the conclusion and before the title and abstract

Methods Section

*Tells how you did the study—what materials and methods of research and analysis were used.

*First section you write—after preparing your figures and tables

Results Section

What does it do.

*Explains the important findings of your study that help to answer your research question or hypothesis and address your purpose statement.

*After the Methods and before the Discussion/Conclusion

Discussion/Conclusion Section

*Explains what your findings mean and what the implications and importance are both to your specific area of research and in a broader context (i.e., to the wider field or to society ).

*Includes limitations to your study and discusses possible future research that is needed to answer your research question more clearly and address closely related questions.

*After the Results Section and before the Introduction

Composing Your Research Paper Sections

This portion of the lecture focuses on developing techniques for composing your paper. You should always go back through your paper after one section is finished and correct or change another part, but by composing in this order you will be sure to include all of the important information. Not that the Methods and Results sections are written first. The reason for this is because you will not be changing or adding to these sections after you have evaluated your research—they represent the core data of your study.

Step 1: Prepare the figures and tables

Most likely, your research paper will use some figures, tables, or other graphics—they are also core data because they are usually numbers representing your findings and methods used. We won’t go into the details of how to prepare these here, but in the  Results section , we will go over how to write captions for the figures based on the data and research questions. For a detailed explanation of preparing and formatting figures, check out these sites (every journal will have their own formatting guidelines):

• Springer Online Research Resources
• ACSESS Digital Library (ASA, CSSA, and SSSA publications for reference)

Step 2: Write the Method s section

This section responds to the question  “How was the problem studied and analyzed?”

The Methods section should:

• Describe how an experiment was done
• Give a rationale for why specific experimental procedures were chosen
• Describe what was done to answer the research question and how it was done.
• Explain how the results were analyzed

Organization of Methods

Write the Methods section in this order to ensure proper organization and make it easier for readers to understand how your study was carried out:

• Description of materials used, including site and sample
• Explanation of how materials were prepared
• Explanation of how measurements were made and calculations performed
• Explanation of statistical methods to analyze data

Tips for the Methods Section

• Organize description of preparations, measurements, and protocol chronologically
• List the Methods in the same order as they will appear in the Results section
• Material should be organized by topic from most to least important
• Headings can be used to separated different results; paragraphs are often used instead

Step 3: Write the Results

This section responds to the question  “What did you find?”  Only the direct results of  your  research should be presented here, not any results from other studies. This is essentially an analysis of the data explained in sentence form so that it is easier to read and put into context.

The Results section should include:

• Findings presented in the same order as in the Methods section
• Data presented in tables, charts, graphs, and other figures (placed among research text or on a separate page)
• Reports on data collection, recruitment, and/or participants
• Data that corresponds to the central research question(s)
• Secondary findings (secondary outcomes, subgroup analyses, etc.)

Organization of Results

Write the Results in the same order as you wrote your Methods. One trusted method of writing the results is addressing specific research questions presented in the figures. Within each research question, present the type of data that addresses that research question.

Sample research question asked in a survey:

“What do hospital patients over age 55 think about postoperative care?”

Present this answer as a statement based on the data:

“Hospital patients over the age of 55 were 30% more likely to report negative experiences after postoperative care (M=83; see Fig. 1).”

Elaborate on this finding with secondary information included in the same paragraph:

“The most common negative issues reported were inattention by nurses, lack of proper medicine and a prolonged waiting period for personal issues ((P>12), (W>13), and (D>10); see Fig. 3).”

Caption your figures with the same method, using the data and research question to create phrases that give context to the data:

“Figure 1: Attitudes towards postoperative care in patients over the age of 55.”

Grammar Guidelines for Results

• When referencing figures, use the present tense; when discussing events of the experiment/study, use past tense
• Passive or active voice are generally acceptable—but consistency is most important. (Read articles from target journal).
• Cite the figure or table every time you reference it, just as you would another text.

Dos and Don’ts for Results

• Limit your results to only those that address your research questions; return to the Results section later after you have completed the Introduction and remove less relevant information.
• Indicate the statistical tests used with all relevant parameters. E.g., mean and standard deviation (SD): 44% (±3); median and interpercentile range: 7 years (4.5 to 9.5 years).
• Use mean and standard deviation to report normally distributed data.
• Use median and interpercentile range to report skewed data.
• For numbers, use two significant digits unless more precision is necessary (2.08, not 2.07856444).
• Never use percentages for very small samples. E.g., “one out of two” should not be replaced by 50%.

Step 4: Write the Discussion/Conclusion

This section responds to the question  “What do the results mean?”  This section is easy to write, but difficult to write well. It requires more than a simply analysis—you have to interpret and “sell” your data to the journal and researchers, explaining just how important your findings are. In fact, many manuscripts are rejected because the Discussion section is weak.

The Discussion and Conclusion are often considered to be part of the same section, but the Conclusion is sometimes considered a separate section. At any rate, the Conclusion will be a very short and clear justification of your work or suggestion for future studies.

In the Discussion Section you should:

• Critique your study—be honest about the effectiveness of your design; suggest modifications and improvement.
• Answer this question: “Did your study contribute to knowledge in the field or not?”
• Discuss the impact of this research on related research within the domain

Pre-writing Questions to Answer for the Discussion:

• How do these results relate to the original question or objectives outlined in the Introduction section?
• Do the data support your hypothesis?
• Are your results consistent with what other investigators have reported?
• Discuss weaknesses and discrepancies. If your results were unexpected, try to explain why
• Is there another way to interpret your results?
• What further research would be necessary to answer the questions raised by your results?

Organization of the Discussion Section

The Discussion section is more open than the Results and Methods section, but you should always focus first on what is MOST important and then move to what is less important to your research problem. Divide the analysis of results by paragraph and do not combine unrelated datasets in one paragraph

• The first paragraph/part should summarize the process, the results, and the overall purpose of this study.
• The second paragraph/part should answer questions about the limitations and potential flaws or shortcomings of this study (e.g., the “failure to reveal clear relationships between samples or groups”). Assesses which of the results are most useful in answering the research question.
• The third paragraph should focus on the successes of the study and highlight which method or approach yielded the best results or those most closely hypothesized. You can also compare the results of different methods and assess which was more fruitful and why.
• In subsequent paragraphs, discuss the implications of this research and compare it to the results of other studies. This is the other section (in addition to the Introduction) where you can cite related studies to show how your study compares.

The Conclusion paragraph offers you a chance to briefly show how your work advances the field from the present state of knowledge. It adds a sort of exclamation point at the end of your paper and makes it more memorable as well.

Add a justification for your work here as well as indicate extensions and wider implications, as well as suggest future studies/experiments and point out any work that is currently ongoing. Do not simply repeat the Introduction or abstract here—extend the claims or questions raised in these sections.

Dos and Don’ts for Discussion/Conclusion

• Don’t be TOO broad about the impact of this research—set some limitations.
• Don’t include new terms or ideas in this section—they should be presented in the Introduction.
• Use specific expressions: instead of “higher temperature” write “41ºC”; instead of “at a lower rate” write “0.7% less”; instead of “highly significant” write “p<0.001.”

Step 5: Write the Introduction

The  Introduction section might be the most important section of the body of your paper—it comes first and introduces what you will be doing, telling readers why your work is important.

A good introduction should:

• Establish the context of the work
• State the purpose of the work in the form of a hypothesis, question, or problem investigated
• Give aims and rationale for your approach

Pre-writing questions to answer for the Introduction

• What is the problem to be solved? (background and problem)
• What do we know about this problem? (literature)
• Are there any existing solutions? (literature)
• What are the limitations or gaps in knowledge of existing solutions?
• What do you hope to achieve with this study? (hypothesis/statement of purpose)

Organization of the Introduction

• Background information
• Motivations
• Key primary literature
• Hypothesis/research problem investigated
• Approaches and rationale

Improving Quality of Writing

In order to write an effective research paper, authors need to know what areas of their writing to improve, and this includes avoiding grammar and style errors. Among the top writing errors we see at  Wordvice  are the following:

• Article and Determiner Misuses
• Nominalization and Wordiness
• Usage of Past and Present Tense

Receiving Language Editing Before Submission

After you are finished writing your Results section and have polished the rest of your research paper, be sure to submit your manuscript to an English proofreading service and paper editing service  before delivering it to journal editors for publication. And learn more about the  editing process  to determine which kind of revision your paper needs.

Wordvice Resources

• How to Write a Research Paper Introduction
• Writing the Results Section of a Research Paper
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Related Resources

• Springer Online Research Resources  (Springer)
• ACSESS Digital L ibrary (ASA, CSSA, and SSSA publications for reference)  (ACSESS Digital Library)

Lecture Research Paper Reference

Yoon S-R, Kim SH, Lee H-W, Ha J-H (2017) A novel method to rapidly distinguish the geographical origin of traditional fermented-salted vegetables by mass fingerprinting. PLoS ONE 12(11): e0188217.

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Beginner’s Guide to Research

Click here to download a .pdf copy of our Beginner’s Guide to Research !

Last updated : July 18, 2024

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Most professors will require the use of academic (AKA peer-reviewed) sources for student writing. This is because these sources, written for academic audiences of specific fields, are helpful for developing your argument on many topics of interest in the academic realm, from history to biology. While popular sources like news articles also often discuss topics of interest within academic fields, peer-reviewed sources offer a depth of research and expertise that you cannot find in popular sources. Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):

Popular vs. academic sources:

• Popular sources are publicly accessible periodicals–newspapers, magazines, and blogs–such as The Washington Post or The New Yorker . These sources are most often written for non-academic audiences, but can be helpful for finding general information and a variety of opinions on your topic.
• Academic sources , known also as peer reviewed or scholarly articles, are those that have undergone peer review before being published. Typically, these articles are written for other scholars in the field and are published in academic journals, like Feminist Studies or The American Journal of Psychology . Literature reviews, research projects, case studies, and notes from the field are common examples.

Primary vs. secondary sources:

• Primary sources are articles written by people directly involved in what they were writing about, including: News reports and photographs, diaries and novels, films and videos, speeches and autobiographies, as well as original research and statistics.
• Secondary sources , on the other hand, are second hand accounts written about a topic based on primary sources. Whether a journal article or other academic publication is considered a secondary source depends on how you use it.

How to Find Academic Sources

Finding appropriate academic sources from the hundreds of different journal publications can be daunting. Therefore, it is important to find databases –digital collections of articles–relevant to your topic to narrow your search. Albertson’s Library has access to several different databases, which can be located by clicking the “Articles and Databases” tab on the website’s homepage, and navigating to “Databases A-Z” to refine your search. Popular databases include: Academic Search Premier and Proquest Central (non-specific databases which include a wide variety of articles), JSTOR (humanities and social sciences, from literature to history), Web of Science (formal sciences and natural sciences such as biology and chemistry), and Google Scholar (a web search engine that searches scholarly literature and academic sources). If you are unable to access articles from other databases, make sure you’re signed in to Alberton’s Library through Boise State!

Performing a Database Search

Databases include many different types of sources besides academic journals, however, including book reviews and other periodicals. Using the search bar , you can limit search results to those containing specific keywords or phrases like “writing center” or “transfer theory.” Utilizing keywords in your search–names of key concepts, authors, or ideas–rather than questions is the most effective way to find articles in databases. When searching for a specific work by title, placing the title in quotation marks will ensure your search includes only results in that specific word order. In the example below, search terms including the author (“Virginia Woolf”) and subject (“feminism”) are entered into the popular database EBSCOhost:

Refining Your Search Results

Many databases have a bar on the left of the screen where you can further refine your results. For example, if you are only interested in finding complete scholarly articles, or peer-reviewed ones, you can toggle these different options to further limit your search. These options are located under the “Refine Results” bar in EBSCOhost, divided into different sections, with a display of currently selected search filters and filter options to refine your search based on your specific needs, as seen in the figure below:

Search results can also be limited by subject : If you search “Romeo and Juliet” on Academic Search Premier to find literary analysis articles for your English class, you’ll find a lot of other sources that include this search term, such as ones about theater production or ballets based on Shakespeare’s play. However, if you’re writing a literary paper on the text of the play itself, you might limit your search results to “fiction” to see only articles that discuss the play within the field of literature. Alternatively, for a theater class discussing the play, you might limit your search results to “drama.”

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Importance of Research Paper Writing for Students

Writing a research paper is very important for students to demonstrate their ability to understand how to start future scholarly works, find an answer to a question scientifically, relate what has been learned, receive critical peer feedback, and learn more strategically and effectively.

Let's take a look at the importance of research paper writing for students and how to write a good research paper.

Why Research Paper Writing is Important for Students and How to Write a Good Research Paper?

All the assignments written by students serve a certain purpose. All assignments develop various life skills and help young people to become better. One of the most complicated and important pieces of writing is a research paper. It is a complex piece that requires good writing skills, in-depth research skills, time, patience, and knowledge. Not all students can handle it properly and frequently complain about it. They do not understand the value and importance of research paper writing. You will be content with what they told about the benefits for all learners. Therefore, read on to define how research paper writing benefits all students.

Read Here: Best Essay Writing Services for Students in California

Development of Writing Skills

As students have to write research papers, they surely develop their writing skills. Even though they need some scientific data, it must be explained plainly on paper. They have to use technical writing. It is applied by skilled writers to explain people without proper knowledge of some specific and complex phenomena. This task isn’t easy, but thanks to research paper writing, it becomes possible. 

While writing a research paper, students improve:

• Active vocabulary;
• The ability to connect various ideas;
• The ability to explain complex terms in simple words.

Don't forget about useful phrases. 

Improvement of Research Skills

The name of this assignment also tells that students develop research skills. To disclose a certain topic, you have to operate with scientific data. Thus, in-depth research is required. Thanks to research, you learn how to identify what facts are relevant and which ones do not suit the purpose of your project.

A Boost in Other Academic Skills

The process of writing a research paper likewise triggers other academic skills. You simply cannot write a perfect research paper without involving the next skills:

• Analytical;
• Problem-solving;
• Critical thinking;
• Communication skills ;
• Listening, etc.

You will surely read a lot, and good reading comprehension is very vital. However, you may require good listening skills because you may deal with videos. You will use your critical thinking and analytical skill to define how to represent the data you have found. Thanks to problem-solving skills , you will understand how to find the solution to the issue.

Enhanced Knowledge and Experience

During the process of writing and researching a research paper, students always learn something new. It helps to get more experienced and enlarge their knowledge. Every new topic brings new information. Oftentimes, students have to define data about other related topics and so it is possible to learn more about various themes and improve their communication skills .

An Ability to Handle Complex Tasks

The accomplishment of a research paper is a complicated task. We have already talked about its complexity and what it demands at the beginning of the article. You have to use various skills and spend heaps of precious time to get the answers. All these struggles make you stronger, more experienced and build your self-confidence . In time you will be able to handle similar tasks, as well as non-academic tasks. It is an enormous benefit for the whole lifespan.

Some Tips to Write a Good Research Paper

Now, we want to offer a short guide with smart tips about how to write a good research paper. There are several important stages you should know about. We will explain how to complete them properly.

Choose a good topic

Everything begins with a selection of a topic. It is supposed to be interesting to your readers. Try to get in their heads and ask yourself what would be useful to disclose and learn about. You will have to study the latest trends in the direction you have chosen for your research project. After you find relevant ideas, choose the one that appeals to you because your opinion matters as well! You are supposed to be enthusiastic about what you research.

Research and outline

Once you get the idea of what can be covered, research it! Be sure you use only reliable and useful sources. Otherwise, your project will be worthless. Therefore, always verify all the sites, articles, journals, and other pieces of information to be sure they are 100% trustworthy.

Take smart notes of what you’ve found and use them in your outline. Your outline should contain all the writing stages, with some brief instructions about what should be done there. Decide where and how to use the facts you’ve found. You may also add deadlines for each stage to control the process of writing and meet your deadline.

Craft a thesis statement

It is vital to have a “working” thesis statement. We have used the word “working” because the initial thesis may change when you finish writing the main parts of your project. Sometimes the thesis statement is changed entirely. That’s why you should have some version to start with.

Write and edit

Once you’re through the preliminary stages, you should go to writing. Be specific in every stage and don’t go astray. It is a scientific paper, and you cannot go in circles until you come to the point. 

Here are a few vital tips for writing proper research paper sections:

• Write short sentences;
• Break long paragraphs into smaller chunks;
• Cover one point or idea per paragraph;
• Be straightforward and concise;
• Avoid using unknown words, slang, jargon, etc.;
• Always explain all technical terms you had to use;
• Make smooth transitions and end your opinions logically;
• Stick to the passive voice.

Do not submit your project before it’s revised. No one is perfect, and some mistakes are inevitable, especially when you write such complicated papers. Revise and edit your projects twice or even thrice. 

Apply various methods to do that:

• Read in your head and read aloud;
• Read from the last line to the first line;
• Ask others to review your paper;
• Use at least one grammar checker.

When you use various methods, you surely have more chances to define some weaknesses in your argumentation, clarify, or simply grammar. It is very good when you find someone competent to review your project. Oftentimes, another person sees the gaps you have omitted.

Use technology as well. A good plagiarism checker may show the mistakes you have no idea of. It saves heaps of precious time because the smart tool scans the text in less than a minute and shows all the mistakes.

Read Here: Top 5 Pro Tips To Craft A Science Essay

As you can see, writing research papers is very useful for every student. It gives them a huge boost in various academic skills. Besides, young people can learn new information in various aspects of life and thus become experienced. It surely has many potential dividends for their future careers.

Read Also: 15 Copywriting Tips for Industry Newcomers

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

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APA format: Basic Guide for Researchers

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• Oakland University
• Oakland University Alumni Association
• Class Notes

OUWB’s Afonso, Wasserman named Dean’s Distinguished Professors

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Two professors from Oakland University William Beaumont School of Medicine have each achieved the faculty rank of Dean’s Distinguished Professor. 

The Oakland University Board of Trustees approved the title changes recommended by Oakland University President Ora Hirsch Pescovitz, M.D., at its regular meeting on June 28, 2024.

Nelia Afonso, M.D., professor, and Jason Wasserman, Ph.D., professor — both from OUWB’s Department of Foundational Medical Studies — each received the title.

“This prestigious recognition is not merely a personal achievement but a testament to the collaborative efforts of our academic community,” said Afonso.

Wasserman shared similar thoughts.

“The thing that means the most to me is that it was the result of a nomination of at least 10 of my colleagues,” he said. “It’s a really nice honor.”

The rank of Dean’s Distinguished Professor was established in 2021 upon approval from the OU Provost and the OU Assistant Vice President of Academic Human Resources. It’s a permanent, honorific title that acknowledges contributions of employed, full-time, tenured faculty at the rank of professor.

According to the recommendations from Pescovitz, the designation “shall be afforded to awardees who have superior teaching skills that encompass the breadth and depth of their discipline, a distinguished record of public service, and scholarly, creative, and artistic achievements.”

‘Truly been a privilege’

Sarah Lerchenfeldt, Pharm.D., associate professor and interim co-chair, Department of Foundational Medical Studies, nominated Afonso.

In her nomination, Lerchenfeldt said Afonso “exemplifies the criteria” for the title.

“Since joining OUWB as a founding faculty member, she has demonstrated unparalleled dedication to medical education, significantly enhancing both the academic and practical aspects of these fields,” wrote Lerchenfeldt.

The nomination noted Afonso’s scholarly excellence, particularly in the domains of education, clinical skills, women’s health, and vaccine hesitancy. Lerchenfeldt said that Afonso was principal investigator for the Merck Investigator Studies program project called “Promoting Vaccine Confidence in Medical and Dental Students.” The project secured about $179,000 in funding.

The nomination also pointed to Afonso’s efforts to develop and refine the curriculum for OUWB’s Art and Practice of Medicine (APM) course, previous awards she received, commitment to service, and leadership roles with organizations like the Southeast Michigan Center for Medical Education.

“Dr. Afonso’s tenure at OUWB has been marked by a commitment to advancing medical education, research, and community health,” wrote Lerchenfeldt. “Her work not only reflects the values and mission of our institution, but also sets a benchmark for academic and professional excellence.”

Afonso said it has “truly been a privilege” to receive the title.

“I have been fortunate to be part of this medical school since its inception and I am grateful for the numerous opportunities provided that have fostered my growth as a physician, educator, and researcher,” she said. “I appreciate the dedication and commitment of our faculty and staff, whose support has been instrumental in implementing various curricular innovations.”

‘Invaluable member of OUWB faculty’  

Wasserman , who joined OUWB in 2013, was nominated by a group of 10 other faculty from the Department of Foundational Medical Studies.

They called him a “prolific and influential scholar” on several topics: homelessness, clinical bioethics, and Holocaust medicine.

“His scholarship and research range from core bioethics topics, such as autonomy and informed consent, to empirical research and qualitative inquiry into homelessness and medical ethics,” they wrote, and noted that he has published three books, 12 book chapters and supplements, 64 peer-reviewed journal articles, 22 editor-reviewed articles, and seven invited articles.

“Dr. Wasserman’s extensively cited research has contributed to important debates surrounding ethics in medical education, care for homeless individuals, pediatric ethical concerns, euthanasia, the rights of patients without decision-making capacity, and immunization policy,” wrote the nominators.

They also noted Wasserman’s roles in shaping the Medical Humanities and Clinical Bioethics (MHCB) curriculum, replacing traditional essay assignments with extemporaneous self-reflection videos, development of interactive iBooks, serving as a mentor to more than 50 OUWB students for their  Embark  projects, and developing a national training course in bioethics for the Arnold P. Gold Foundation.

The nominators also mentioned Wasserman’s history of service. In 2020, he was appointed as one of only two Provost Fellows for Faculty Diversity at Oakland University. In 2022, he was honored with the OU Founder’s Day Award for Faculty Excellence in Diversity, Equity, and Inclusion. He also chaired the admissions committee for five years, served as director of student professionalism for nine years, co-founded  Street Medicine Oakland , launched the Center for Moral Values in Health Medicine, and more.

And he has plans to do even more, including launching a new student-led journal on ethics, humanities, and social justice, and a health care ethics debate tournament that will be open to all OU students.  

“One of the reasons I came to OUWB was because it was a new school and there was a lot of opportunity to be entrepreneurial,” he said. “I didn’t want to go to some well-established place where the expectation is you teach your courses, you write papers, and otherwise just let things run.”

For more information, contact Andrew Dietderich, senior marketing specialist, OUWB, at [email protected] .

To request an interview, visit the OUWB Communications & Marketing  webpage .

This work is licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License .

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• Published: 17 July 2024

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• Subha Subramanian 2 ,
• Demetrius Perry 1 ,
• Benjamin P. Kay   ORCID: orcid.org/0000-0003-3628-8029 3 ,
• Evan M. Gordon   ORCID: orcid.org/0000-0002-2276-5237 4 ,
• Timothy O. Laumann   ORCID: orcid.org/0000-0002-0428-427X 1 ,
• T. Rick Reneau   ORCID: orcid.org/0000-0002-0211-2482 4 ,
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• Abraham Z. Snyder   ORCID: orcid.org/0000-0002-3379-9627 3 , 4 ,
• Ginger E. Nicol 1   na1 &
• Nico U. F. Dosenbach   ORCID: orcid.org/0000-0002-6876-7078 3 , 4 , 8 , 20 , 22   na1  

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• Consciousness
• Drug development
• Neural circuits
• Neuroscience

A single dose of psilocybin, a psychedelic that acutely causes distortions of space–time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials 1 , 2 , 3 , 4 . In animal models, psilocybin induces neuroplasticity in cortex and hippocampus 5 , 6 , 7 , 8 . It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6–12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

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Psychedelic drugs can reliably induce powerful acute changes in the perception of self, time and space by agonism of the serotonin 2A (5-HT 2A ) receptor 9 , 10 . In clinical trials, a single high dose of psilocybin (25 mg) has demonstrated rapid and sustained symptom relief in depression 1 , 2 , 3 , 11 , 12 , 13 , 14 , addiction 4 , 15 and end-of-life anxiety 13 , 14 . Together, these observations indicate that psychedelics should induce potent acute (lasting roughly 6 hours) and persistent (24 hours to 21 days) neurobiological changes.

In rodent models, transient activation of the 5-HT 2A receptors by a psychedelic can alter neuronal communication in 5-HT 2A -rich regions (for example, the medial frontal lobe) and induce persistent plasticity-related phenomena 5 , 6 , 7 . Synaptogenesis in the medial frontal lobe and anterior hippocampus is thought to be key to the neurotrophic antidepressant effects of psilocybin 5 , 16 , 17 . Yet, inherent limitations of rodent models, and imperfect homology to the human 5-HT 2A receptor 18 , limit the generalizability of these assertions.

Understanding the effects of psychedelics on human brain networks is critical to unlocking their therapeutic mechanisms. In humans, during the roughly 6 hour duration of action, psilocybin increases glutamate signalling and glucose metabolism 19 , 20 , 21 , broadly decreases the power of electrophysiological signals 22 , reduces hemodynamic fluctuations 23 and decreases segregation between functional networks 24 . The drivers of these acute changes are poorly understood, particularly in the subcortex. Preliminary efforts to identify network changes in the weeks after psilocybin have yielded mixed results 25 , 26 , 27 . Persistent effects of psilocybin on clinically relevant circuits have yet to be characterized in humans.

The ventromedial prefrontal cortex and anterior and middle hippocampus are functionally connected to the default mode network (DMN) 28 , 29 . Increased FC between the hippocampus and DMN has been associated with depression symptoms 30 and decreased FC is associated with treatment 31 , 32 . These 5-HT 2A receptor rich 33 and depression associated default mode regions 34 , 35 , 36 are candidates for mediating the neurotrophic antidepressant effects of psychedelics.

Precision functional mapping uses dense repeated functional magnetic resonance imaging (fMRI) sampling 37 , 38 , 39 , 40 , 41 to reveal the time course of individual-specific intervention-driven brain changes 42 . This approach accounts for inter-individual variability in brain networks 37 and capitalizes on the high stability of networks within individuals from day to day 38 . Using precision functional mapping, we observed individual-specific acute and persistent brain changes following a single high dose of psilocybin.

Healthy young adults received 25 mg psilocybin and 40 mg methylphenidate (MTP, generic name Ritalin, dose-matched for arousal effects) 1–2 weeks apart and underwent regular MRI sessions (roughly 18 per participant) before, during, between and after the two drug doses (Extended Data Fig. 1 , Supplementary Table 1 and Supplementary Video  1 : data quality metrics for 129 total MRI visits). Dense predrug sampling familiarized participants with the scanner and established baseline variability.

Psilocybin disrupts brain connectivity

Psilocybin acutely caused profound and widespread brain FC changes (Fig. 1a ) across most of the cerebral cortex ( P  < 0.05 based on two-sided linear mixed-effects (LME) model and permutation testing), but most prominent in association networks (FC change mean (standard deviation, s.d.): association cortex 0.44 (0.03), primary cortex 0.36 (0.05)). In the subcortex the largest psilocybin-associated FC changes were seen in DMN connected parts of the thalamus, basal ganglia, cerebellum and hippocampus 29 , 43 , 44 (Fig. 1a and Extended Data Fig. 2 ). In the hippocampus, foci of strong FC disruption were located in the anterior hippocampus (Montreal Neurological Institute coordinates −24, −22, −16 and 24, −18, −16). Other large FC disruptions were seen in mediodorsal and paraventricular thalamus 45 and anteromedial caudate. In the cerebellum, the largest FC changes were seen in the DMN connected areas 44 (Fig. 1a ).

FC change (Euclidean distance) was calculated across the cortex and subcortical structures. Effects of drug condition were tested with an LME model in n  = 6 longitudinally sampled participants over ten sessions with psilocybin and six sessions with methylphenidate (MTP) ( a and b are thresholded at P  < 0.05 based on permutation testing with TFCE; see unthresholded statistical maps in Extended Data Fig. 2 ). a , Psilocybin-associated FC change, including in subcortex. a, anterior; p, posterior; L, left; R, right. b , MTP-associated FC change. c , Typical day-to-day variability as a control to the drug conditions (unthresholded: not included in LME model). d , Average FC change in individual-defined networks. Open circles represent individual participants. FC change is larger in DMN than other networks. Rotation-based null model (spin test 62 , 97 ): ten psilocybin doses, 1,000 permutations, one-sided P spin  < 0.001, ( P spin  > 0.05 for all other networks). ** P  < 0.001, uncorrected. e , Whole-brain FC change (Euclidean distance from baseline) for all rest scans across conditions. FC change for MTP, psilocybin and day-to-day are in comparison to same-participant baseline. White dots indicate median, vertical lines indicate quartiles. LME model predicting whole-brain FC change: ten psilocybin doses (275 observations), estimate (95% CI) = 15.83 (13.50, 18.15), t (266)  = 13.39, P  = 1.36 × 10 −31 , uncorrected. For the full FC distance matrix with session labels, see Extended Data Fig. 3 . f , g , Comparison of the differences in FC change to differences in psychedelic experiences. f , Individual FC change maps and MEQ30 scores for two exemplars (see Extended Data Fig. 4 for all drug sessions). g , The relationship between whole-brain FC change and mystical experience rating is plotted for all drug sessions (psilocybin and MTP). The LME model demonstrated a significant relationship: 16 drug doses (ten psilocybin, six MTP), estimate (95% CI) = 69.78 (50.15, 89.41), t (13)  = 7.68, P  = 3.5 × 10 −6 , uncorrected. h , The relationship between FC change and MEQ30 ( r 2 ) is mapped across the cortical surface.

By comparison, MTP-associated FC changes localized to sensorimotor systems (Fig. 1b and Extended Data Fig. 2 ) and paralleled the map of day-to-day variability (Fig. 1c ) probably due to arousal effects 39 . Psilocybin-associated FC change was largest in the DMN (Fig. 1d and Supplementary Fig. 1 ; averaged across all psilocybin sessions; spin test, 1,000 permutations, one-sided P spin  < 0.001; P spin  > 0.05 for all other networks). However, MTP-associated FC change was largest in motor and action networks ( P spin  = 0.002; P spin  > 0.05 for all other networks; Supplementary Fig. 1b ).

Despite MTP and psilocybin causing similar increases in heart rate (Supplementary Fig. 2 ), the effects of psilocybin on FC were more than threefold larger than the effects of MTP (Fig. 1e ; post hoc two-sided t -test; P  = 3.6 × 10 −6 , uncorrected). The psilocybin effects also dwarfed those of other control conditions (Fig. 1e ; day-to-day change (normalized) 1; task 1.22, MTP 1.10, high head motion 1.29, psilocybin 3.52, between person 3.53; Extended Data Fig. 3 ; these effects were robust to preprocessing choices: Supplementary Figs. 3 and 4 ). To put the effects of psilocybin into perspective, it helps to consider that the mean changes in brain organization caused by the drug were as large as the differences in brain organization between different people (Fig. 1e ).

The psychedelic experience

The large amount of data collected per participant, under the individual-specific imaging model, allowed us to move beyond group-analyses and compare the subjective psychedelic experience (30-item Mystical Experience Questionnaire, MEQ30) 46 to brain function data session-by-session (Fig. 1f ). The MEQ30 is a self-assessment instrument used to measure the intensity and quality of mystical experiences, including feelings of connectedness, transcendence of time and space, and a sense of awe, with a maximum score of 150 (ref. 46 ). Across psilocybin sessions and participants, FC change tracked with the intensity of the subjective experience (Fig. 1f and Extended Data Fig. 4 ). Correlating the whole-brain FC change ( x axis) against the MEQ30 scores ( y axis) for all drug sessions (Fig. 1g ) revealed an r 2  = 0.81 (LME model predicting MEQ30 score: effect of FC change, t (13)  = 7.68; P  = 3.5 × 10 −6 , uncorrected). Head motion was not significantly correlated with MEQ30 scores (effect of framewise displacement, t (13)  = −1.26, P  = 0.23, uncorrected). Projecting the relationship between someone’s mystical experience and the corresponding FC change onto the brain (Fig. 1h , vertex-wise) showed it to be driven by association cortex, relatively sparing primary motor and sensory regions. Of the four MEQ30 dimensions (mystical, positive mood, transcendence of time and space, and ineffability), the one most strongly correlating with brain change was transcendence (for example, ‘loss of your usual sense of time or space’, r 2  = 0.86, Supplementary Fig. 5 ), however, all dimensions were highly correlated ( r  > 0.8). Repeated sampling enabled us to determine that the inter-individual variability in the effects of psilocybin in the brain was more likely related to differences in drug effects than measurement error (likelihood ratio test of participant-specific response to psilocybin, P  = 0.00245, uncorrected) 47 , 48 .

The psychedelic dimension

To examine the latent dimensions of brain network changes we performed multidimensional scaling (MDS) on the parcellated FC matrices from every fMRI scan 38 . MDS is blind to session labels (for example, drug, participant). Yet, dimension 1, which explained the largest amount of variability, separated psilocybin from other scans (Fig. 2a ), apart from one session during which the participant (P5R) had emesis 30 minutes after taking psilocybin (dark red dots on the left of Fig. 2a ). The higher score on dimension 1 associated with psilocybin, corresponded to reduced segregation between the DMN and other networks (fronto-parietal 49 , dorsal attention 50 , salience 51 and action-mode 52 , 53 ) that are typically anticorrelated with it 54 (Fig. 2b and Extended Data Fig. 5 ). To determine whether this reflects a common effect of psilocybin that generalizes across datasets and psychedelics, we calculated dimension 1 scores for extant datasets from participants receiving intravenous (i.v.) psilocybin 55 and lysergic acid diethylamide (LSD) 56 . Psychedelic treatment increased dimension 1 in nearly every participant in the psilocybin and LSD datasets (Fig. 2c ), suggesting that this is a common effect across psychedelic drugs and individuals.

MDS blind to session labels was used to assess brain changes across conditions. a , In the scatter plots, each point represents whole-brain FC from a single 15 min scan, plotted in a multidimensional space on the basis of the similarity between scans. Dimensions 1 and 4 showed strong effects of psilocybin. The top shows scans coded on the basis of drug condition. Dark red denotes that the participant had an episode of emesis shortly after taking psilocybin. The bottom shows scans coloured on the basis of participant identity. Dimension 1 separates psilocybin from non-drug and MTP scans in most cases. See Extended Data Fig. 5 for the dimension 1–4 weight matrices. b , Visualization of dimension 1 weights. The top 1% of edges (connections) are projected onto the brain (green indicates connections that are increased by psilocybin). Cerebellar connections are included although the structure is not shown. c , Re-analysis of dimension 1 in extant datasets with intravenous psilocybin (left, ref. 55 , paired two-sided t -test of change in dimension 1 score, n  = 9, t (8)  = 2.97, P  = 0.0177, uncorrected) and LSD (right, ref. 56 , paired two-sided t -test: n  = 16, t (15)  = 4.58, P  = 3.63 × 10 −4 , uncorrected). * P  < 0.05, ** P  < 0.001, uncorrected. d , Average effects of psilocybin on network FC, shown separately for within-network integration (left) and between network segregation (right). For network integration (left), blue indicates a loss of FC (correlations) between regions within the same network. For network segregation (right), blue indicates a loss of FC (anticorrelations) to all other regions in different networks; see Extended Data Fig. 6 for a full correlation matrix. Dissolution of functional brain organization corresponds to decreased within-network integration and decreased between network segregation.

Subtraction of average FC (psilocybin minus baseline) revealed a pattern of FC change similar to dimension 1 (Fig. 2d and Extended Data Figs. 5 and 6 ). Consistent with previous psychedelics studies 24 , psilocybin increased FC between networks (particularly fronto-parietal, default mode and dorsal attention), whereas FC within networks was relatively less affected. A similar pattern of loss of segregation between brain networks is produced by nitrous oxide and ketamine 57 , suggesting that the psychedelic dimension observed here may generalize to psychedelic-like dissociative drugs.

By comparison, MTP decreased within-network FC in the sensory, motor and auditory regions (Extended Data Fig. 6 ), consistent with previous reports 58 and similar to the effects of caffeine 39 . To verify that observations in our sample ( n  = 6) were generalizable, we compared stimulant effects in our study to those in the Adolescent Brain Cognitive Development (ABCD) Study 59 ( n  = 487 taking stimulants). The effect of stimulant use in ABCD was consistent with MTP-associated FC changes in our dataset (Extended Data Fig. 6 ).

Desynchronization explains FC change

Multi-unit recording studies suggest that agonism of 5-HT 2A receptors by psychedelics desynchronizes populations of neurons that typically co-activate 60 . We proposed that this phenomenon, observed at a larger spatial scale, might account for psilocybin-associated FC change (Fig. 1 ). We observed that the typically stable spatial structure of resting fMRI fluctuations was disrupted and desynchronized by psilocybin (Supplementary Videos  2 – 7 : brain activity time courses during drug sessions for each participant). Therefore, we quantified brain signal synchrony using normalized global spatial complexity (NGSC): a measure of spatial entropy that is independent of the number of signals 61 . NGSC calculates cumulative variance explained by subsequent spatiotemporal patterns (Fig. 3a ). The lowest value of NGSC (0) means that the time course for every vertex and/or voxel is identical. The highest value of NGSC (equal to one) means that the time course for every vertex and/or voxel is independent, indicating maximal desynchronization (or spatial entropy).

a , NGSC captures the complexity of brain activity patterns. It is derived from the number of spatial principal components needed to explain the underlying structure. Higher entropy equals desynchronized activity. On the right is variance explained by subsequent principal components for psilocybin in red, MTP in blue and no drug in grey for P6. b , Whole-brain entropy (NGSC) is shown for every fMRI scan for a single participant (P6). At right, increases during psilocybin were present in all participants. Sample sizes are provided in Supplementary Table 1 . Grey bars indicate condition means. c , Parcel entropy (computed on individual-specific parcels) within functional brain areas shows similar psilocybin-driven increases as whole-brain entropy. d , Psilocybin-associated spatial entropy (individual-specific parcels, averaged across participants) is visualized on the cortical surface. Psilocybin-associated increases in entropy were largest in association cortex. e , LSD-associated increases in spatial entropy were similar to those induced by psilocybin (using data from ref. 56 ). f , Increases corresponded spatially to 5-HT 2A receptor density 33 . In b – d , n  = 6 participants, 272 observations (scans). For e , n  = 16 participants.

Psilocybin significantly increased NGSC acutely with values returning to predrug baseline by the following session (Fig. 3b,c ). The increase in NGSC was observed at the whole-brain level (Fig. 3b ; LME model, estimate (95% confidence interval (CI)) = 0.0510 (0.0343, 0.0676), t (265)  = 6.8, P  = 2.0 × 10 −6 , uncorrected) and correlated with the subjective experience (MEQ30: Extended Data Fig. 7 ; r  = 0.80, P  = 3.52 × 10 −4 , uncorrected, after single outlier removal), whereas nuisance variables did not. Increased NGSC was also observed for individual-defined brain areas 62 (Fig. 3c ; LME model, estimate (95% CI) = 0.0149 (0.0071, 0.0228), t (265)  = 3.74, P  = 2.30 × 10 −4 , uncorrected), with the largest increases in association cortex and minimal changes in primary cortex (Fig. 3d ). Global and local desynchronization replicated in an LSD dataset 56 (Fig. 3e ) and the distribution of these effects correlated with 5-HT 2A receptor density (Fig. 3f ; bivariate correlation NGSC psilocybin to Cimbi-36 binding, r  = 0.39, P  = 1.9 × 10 −13 ; NGSC LSD to Cimbi-36 binding, r  = 0.32, P  = 4.5 × 10 −9 , uncorrected) 33 , 63 .

Task engagement reduces desynchronization

To investigate how psilocybin-driven brain changes are influenced by task states, participants were asked to complete a simple auditory–visual matching task in the scanner ( Methods , perceptual fMRI task). Participants performed this task with more than 80% accuracy during drug sessions (Extended Data Fig. 8a–c ). Engagement in the task significantly decreased the magnitude of psilocybin-associated network disruption and desynchronization (Fig. 4 ; LME model interaction of task × psilocybin: FC change P  = 5.49 × 10 −5 , NGSC P  = 4.82 × 10 −8 , uncorrected). These results were robust to scan order effects (Supplementary Fig. 6 ) and regression of evoked responses (Supplementary Fig. 7 ).

a , Psilocybin-associated FC change from resting scans (left) and from task scans (right). b , Regional NGSC change (psilocybin minus baseline) from rest scans (left) and from task scans (right). Bar graphs on the bottom indicate the corresponding whole-brain FC change ( a ) and whole-brain NGSC values ( b ) during rest and task for baseline and drug conditions. LME models indicated an interaction of task × psilocybin on FC change ( n  = 7 with task data on psilocybin, estimate (95% CI) = −6.48 (−9.59, −3.37), t (265)  = −6.48, P  = 5.49 × 10 −5 , uncorrected) and an interaction of task × psilocybin on NGSC ( n  = 7 with task data on psilocybin, estimate (95% CI) = −0.042 (−0.056, −0.027), t (265)  = −5.62, P  = 4.82 × 10 −8 , uncorrected). Bars indicate mean and error bars indicate s.e.m.. ** P  < 0.001, uncorrected.

The reduction of psilocybin-driven brain changes during task performance seems to parallel the psychological principle of ‘grounding’: directing one’s attention externally as a means of alleviating intense or distressing thoughts or emotions. Grounding techniques are commonly used in psychedelic-associated psychotherapy to lessen overwhelming or distressing effects of psilocybin 64 . Task-related reductions in network desynchronization provide strong evidence for context-dependent effects of psilocybin on brain activity and FC 65 and fill an important gap between preclinical studies of context dependence 66 , 67 and clinical observations 68 .

Classical animal studies documented that psychedelics reduce optic tract responses to photic stimulation of the retina, indirectly reducing visual cortex activation 69 , 70 . We replicated these effects by documenting reduced task-evoked responses in primary visual cortex (Extended Data Fig. 8d–g ). To assess whether psilocybin affects the magnitude of hemodynamic responses elsewhere, we analysed evoked responses during the perceptual task in other task-related regions of interest (Extended Data Fig. 8f,g ). But the magnitudes of other evoked responses were not significantly changed by psilocybin (two-way analysis of variance of drug and participant; effect of drug: left V1 P  = 0.03, right V1 P  = 0.02, all other P  > 0.1, uncorrected).

Persistent decrease in hippocampal FC

To assess whether persistent neurotrophic and psychological effects of psychedelics might be associated with persistent FC changes after psilocybin, we compared FC changes 1–21 days post-psilocybin to pre-psilocybin. Whole-brain FC change scores were small (normalized FC change (range) of 1.05 (0.94, 1.27)), indicating that the brain’s network structure had mostly returned to baseline (Extended Data Fig. 2 ).

Atypical cortico-hippocampal connectivity has been associated with affective symptoms 30 and hippocampus neurogenesis is observed after psilocybin 6 . Further, acute decreases in hippocampal glutamate after psilocybin correlate with decreased DMN connectivity and ego dissolution 21 . Thus, we investigated whether the same region of the anterior hippocampus that showed strong acute FC change also showed persistent FC change. We observed significant FC change in the 3 week postdrug period (Fig. 5a,b ; LME mean change 0.095, P pre– post-psilocybin  = 0.0033, uncorrected). No persistent FC differences were observed post-MTP ( Methods , section ‘Persistent effects analysis’; LME ‘FC change’ 90% CI (−0.056, 0.080); equivalence δ  = ±0.086, P pre– post-MTP  = 0.77).

a , Hippocampus FC change maps (left hippocampus; unthresholded t -maps, as in Extended Data Fig. 2 ). Acute psilocybin FC change is shown on top and persistent FC change (3 weeks after psilocybin) on the bottom. b , Each dot represents the FC change score for the anterior hippocampus for a single scan before (left) and after (right) psilocybin for every participant (coloured as in Fig. 2 ). Participants showed a post-psilocybin increase in FC change in the anterior hippocampus (LME model, pre- versus post-psilocybin; n  = 6 participants, 186 observations, estimate (95% CI) = 0.095 (0.032, 0.168), t (182)  = 2.97, P  = 0.0033, uncorrected). c , Connectivity from an anterior hippocampus seed (Montreal Neurological Institute coordinates −24, −22, −16 and 24, −18, −16) pre-psilocybin (left), post-psilocybin (middle) and persistent change (post- minus pre-) for an exemplar participant (P3). The red border on the right-most brain outlines the individual-specific DMN. A decrease in hippocampal FC with parietal and frontal components of the DMN is seen. d , Time course of anterior hippocampus minus DMN for all participants and scans (participant colours as in b ). A moving average is shown in black. e , Schematic of hippocampal-cortical circuits, reproduced from ref. 29 , CC BY 4.0 .

FC between the anterior hippocampus and DMN was decreased postpsilocybin (Fig. 5c,d ). Time-course visualization, after aligning them so that psilocybin dose was day 0, suggests that connectivity is reduced for 3 weeks following a single psilocybin dose (Fig. 5d ; AntHip-DMN FC mean (95% CI): pre-psilocybin was 0.180 (0.169, 0.192); post-psilocybin was 0.163 (0.150, 0.176)). AntHip-DMN FC values returned to pre-psilocybin baseline by the replication visit 6–12 months later, however, the smaller replication sample ( n  = 4 with one pre-psilocybin visit each) was not statistically powered to detect small changes. This observation is compelling, as it localized to the anterior hippocampus, a brain region showing substantial synaptogenesis following psilocybin 6 . Reduced hippocampal-cortical FC may reflect increased plasticity of self-oriented hippocampal circuits 31 (Fig. 5e ).

From micro- to macro-scale psychedelic effects

The synchronized patterns of cofluctuations during the resting state are believed to reflect the brain’s perpetual task of modelling reality 71 . It follows that the stability of functional network organization across day, task, MTP and arousal levels (but not between individuals), reflects the subjective stability of waking consciousness. By contrast, the much larger changes induced by psilocybin fit with participants’ subjective reports of a radical change in consciousness. The large magnitude of effects of psilocybin, in comparison to the effects of MTP, suggests that observed changes are not merely due to increased arousal or non-specific effects of monoaminergic stimulation 72 .

Our observation that psychedelics desynchronize brain activity regionally and globally provides a bridge between previous findings at the micro- and macro-scales of neuroscience. Multi-unit recording studies suggest that agonism of 5-HT 2A receptors by psychedelics does not uniformly increase or decrease firing of pyramidal neurons, but rather serves to desynchronize pairs or populations of neurons that co-activate under typical conditions 60 . Meanwhile, previous resting fMRI studies have reported a range of acute changes following ingestion of psilocybin 55 , 63 , ayahuasca 73 and LSD 56 , 74 , which broadly converge on a loss of network connectivity and an increase in global integration 24 , 75 . Disruption of synchronized activity at several scales may explain the paradoxical observation that psychedelics produce an increase in metabolic activity 19 , 20 , a decrease in the power of local fluctuations 22 , 76 and a loss of the brain’s segregated network structure 23 , 56 . This desynchronization of neural activity has been described as an increase in entropy or randomness of brain activity in the psychedelic state 77 , 78 . Our results support the hypothesis that these changes underpin the cognitive and perceptual changes associated with psychedelics.

Desynchrony may drive persistent change

The dramatic departure from typical synchronized patterns of co-activity may be key to understanding the acute effects of psilocybin and also its persistent neurotrophic effects. Changes in resting activity are linked to shifts in glutamate-dependent signalling during psilocybin exposure 21 , 79 , 80 . This phenomenon, shared by ketamine and psychedelics, engages homeostatic plasticity mechanisms 81 , 82 , a neurobiological response to large deviations in typical network activity patterns 83 , 84 , 85 . This response to novelty includes rapid upregulation in expression of BDNF , M TOR , E EF2 and other plasticity-related immediate early genes 8 , 80 , which are thought to have a key role in the antidepressant response 86 . Consistent with this notion, psilocybin produced the largest changes in the DMN, frequently associated with neuropsychiatric disorders 34 , 35 , 87 , 88 , 89 , 90 , 91 , and in a region of the anterior and middle hippocampus associated with the self 29 , 92 and the present moment 93 .

Psychedelics rapidly induce synaptogenesis in the hippocampus and cortex, effects that seem to be necessary for rapid antidepressant-like effects in animal models 7 , 17 . However, understanding the underpinnings of the behavioural effects of psychedelics requires human studies. Advances in precision functional mapping 37 , 94 , 95 and individual-level characterization enabled us to identify desynchronization of resting-state fMRI signals, connect these changes with subjective psychedelic effects and localize these changes to depression-relevant circuits (DMN, hippocampus). These analyses rely on precise characterization of an individuals’ baseline brain organization (for example, individual definition of brain areas, networks and day-to-day variability) to understand how that organization is altered by an intervention. This precision drug mechanism study was conducted in non-depressed volunteers. Verification of the proposed antidepressant mechanism of psilocybin will require precision patient studies. New methods to measure neurotrophic markers in the human brain 96 will provide a critical link between mechanistic observations at the cellular, brain networks and psychological levels.

Regulatory approvals and registrations

Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki and procedures established by the Washington University in Saint Louis Institutional Review Board. All participants were compensated for their time. All aspects of this study were approved by the Washington University School of Medicine (WUSOM) Internal Review Board, the Washington University Human Research Protection Office (WU HRPO), the Federal Drug Administration (IND no. 202002165) and the Missouri Drug Enforcement Agency (DEA) under a federal DEA schedule 1 research licence and registered with ClinicalTrials.gov identifier NCT04501653. Psilocybin was supplied by Usona Institute through Almac Clinical Services.

Study design

Healthy young adults ( n  = 7, 18–45 years) were enrolled between April 2021 and March 2023 in a randomized cross-over precision functional brain mapping study at Washington University in Saint Louis (see  Supplementary Methods for inclusion and exclusion criteria). The purpose of the study was to evaluate differences in individual-level connectomics before, during and after psilocybin exposure. Participants underwent imaging during drug sessions (with MRI starting 1 h after drug ingestion) with 25 mg psilocybin or 40 mg MTP, as well as non-drug imaging sessions. Drug condition categories were (1) baseline, (2) drug 1 (MTP or psilocybin), (3) between, (4) drug 2 and (5) after. Randomization allocation was conducted using REDCap and generated by team members who prepared study materials including drug or placebo but otherwise had no contact with participants. A minimum of three non-drug imaging sessions were completed during each non-drug window: baseline, between and after drug sessions. The number of non-drug MRI sessions was dependent on availability of the participant, scanner and scanner support staff. Dosing day imaging sessions were conducted 60–180 min following drug administration during peak blood concentration 98 . One participant (P2) was not able tolerate fMRI while on psilocybin, and had trouble staying awake on numerous fMRI visits after psilocybin and was thus excluded from analysis (except for data quality metrics in Extended Data Fig. 1 ).

MTP was selected as the active control condition to simulate the cardiovascular effects and physiological arousal (that is, controlling for dopaminergic effects) associated with psilocybin 99 . Usona Institute, a US non-profit medical research organization, provided good manufacturing practices for psilocybin.

Drug sessions were facilitated by two clinical research staff who completed an approved in-person or online facilitator training programme provided by Usona Institute, as part of the phase 2 study (ClinicalTrials.gov identifier NCT03866174). The role of the study facilitators was to build a therapeutic alliance with the participant throughout the study, prepare them for their drug dosing days and to observe and maintain participant safety during dosing day visits 64 . The pair consisted of an experienced clinician (lead clinical facilitator) and a trainee (cofacilitator).

The predefined primary outcome measure was precision functional mapping (numerous visits, very long scans to produce individual connectomes) examining the effects of psilocybin on cortical and cortico- subcortical brain networks that could explain its rapid and sustained behavioural effects. Predefined secondary outcome measures included (1) assessment of hemodynamic response to evaluate how 5-HT 2A receptor agonism by psychedelics may alter neurovascular coupling, (2) assessment of acute psychological effects of psilocybin using the MEQ30 score ( Supplementary Methods ) and (3) assessment of personality change using the International Personality Item Pool-Five-Factor Model 100 . Changes in pulse rate and respiratory rate during psilocybin and placebo were later added as secondary outcome measures and personality change was abandoned because it was clear that we would not be powered to detect personality change.

Replication protocol

Participants were invited to return 6–12 months after completing the initial cross-over study for a replication protocol. This included 1–2 baseline fMRIs, a psilocybin session (identical to the initial session, except for lack of blinding) and 1–2 ‘after’ sessions within 4 days of the dose.

Participants

Healthy adults aged 18–45 years were recruited by campus-wide advertisement and colleague referral. Participants ( n  = 7) were enrolled from March 2021 to May 2023. Participants were required to have had at least one previous lifetime psychedelic exposure (for example, psilocybin, mescaline, ayahuasca, LSD), but no psychedelics exposure within the past 6 months. Individuals with psychiatric illness (depression, psychosis or addiction) based on the DSM-5 were excluded. Demographics and data summary details are provided in Supplementary Table 1 . One of the authors (N.U.F.D.) was a study participant.

Participants were scanned roughly every other day over the course of the experiment (Extended Data Fig. 1 ). Imaging was performed at a consistent time of day to minimize diurnal effects in FC 101 . Neuroimaging was performed on a Siemens Prisma scanner (Siemens) in the neuroimaging laboratories at the Washington University Medical Center.

Structural scans (T1w and T2w) were acquired for each participant at 0.9 mm isotropic resolution, with real-time motion correction. Structural scans from different sessions were averaged together for the purposes of Freesurfer segmentation and nonlinear atlas registrations.

To capture high-resolution images of blood oxygenation level-dependent (BOLD) signal, we used an echo-planar imaging sequence 102 with 2 mm isotropic voxels, multiband 6, multi-echo 5 (times to echo: 14.20, 38.93, 63.66, 88.39, 113.12 ms) 103 , repetition or relaxation time: 1,761 ms, flip angle of 68° and in-plane acceleration 104 (IPAT or grappa) of 2. This sequence acquired 72 axial slices (144 mm coverage). Each resting scan included 510 frames (lasting 15 min, 49 s) as well as three frames at the end used to provide estimate electronic noise.

Every session included two 15-min resting-state fMRI (rs-fMRI) scans, during which participants were instructed to hold still and look at a white fixation crosshair presented on a black background. Head motion was tracked in real time using Framewise Integrated Real-time MRI Monitoring software (FIRMM) 105 . An eye-tracking camera (EyeLink) was used to monitor participants for drowsiness.

Perceptual (matching) fMRI task

Participants also completed a previously validated event-related fMRI task. This was a suprathreshold auditory–visual matching task in which participants were presented with a naturalistic visual image (duration 500 ms) and coincident spoken English phrase, and were asked to respond with a button press to indicate whether the image and phrase were ‘congruent’ (for example, an image of a beach and the spoken word ‘beach’) or ‘incongruent’. Both accuracy and response time of button presses were recorded. Each trial was followed by a jittered inter-stimulus interval optimized for event-related designs. In a subset of imaging sessions, two task fMRI scans were completed following the two resting scans. Task fMRI scans used the same sequence used in resting fMRI, included 48 trials (24 congruent, 24 incongruent) and lasted a total of 410 s. In analyses, high motion frames were censored 106 and the two task scans were concatenated to better match the length of the rs-fMRI scans. Note the stimulus order in the two trials did not vary across session. The order of rest and task scans was not counterbalanced across sessions to avoid concern that task scans may influence subsequent rest scans.

Resting fMRI processing and resting-state network definition

Resting fMRI data were preprocessed using an in-house processing pipeline. In brief, this included removal of thermal noise using NORDIC denoising 107 , 108 , 109 , correction for slice timing and field distortions, alignment, optimal combination of many echoes by weighted summation 110 , normalization, nonlinear registration, bandpass filtering and scrubbing at a movement threshold of 0.3 mm to remove reduce the influence of confounds 111 . Tissue-based regressors were computed in volume (white matter, ventricles, extra-axial cerebrospinal fluid) 112 and applied following projection to surface. Task-based regressors were only applied when indicated. Details on rs-fMRI preprocessing are provided in  Supplementary Methods . Visualizations of motion, physiological traces and signal across the brain (‘grayplots’) before and after processing 113 are provided in Supplementary Video  1 .

Surface generation and brain areal parcellation

Surface generation and processing of functional data followed similar procedures to Glasser et al. 114 . To compare FC and resting-state networks across participants, we used a group-based surface parcellation and community assignments generated previously 62 .

For subcortical regions, we used a set of regions of interest 115 generated to achieve full coverage and optimal region homogeneity. A subcortical limbic network was defined on the basis of neuroanatomy: amygdala, anteromedial thalamus, nucleus accumbens, anterior hippocampus and posterior hippocampus 116 , 117 . These regions were expanded to cover anatomical structures (for example, anterior hippocampus) 31 .

To generate region-wise connectivity matrices, time courses of all surface vertices or subcortical voxels within a region were averaged. FC was then computed between each region timeseries using a bivariate correlation and then Fisher z -transformed for group comparison.

Individualized network and brain area mapping

We identified canonical large-scale networks using the individual-specific network matching approach described previously 43 , 44 , 62 . In brief, cortical surface and subcortical volume assignments were derived using the graph-theory-based Infomap algorithm 118 . In this approach, we calculated the correlation matrix from all cortical vertices and subcortical voxels, concatenated across all a participant’s scans. Correlations between vertices within 30 mm of each other were set to zero. The Infomap algorithm was applied to each participant’s correlation matrix thresholded at a range of edge densities spanning from 0.01 to 2%. At each threshold, the algorithm returned community identities for each vertex and voxel. Communities were labelled by matching them at each threshold to a set of independent group average networks described previously 62 . In each individual and in the average, a ‘consensus’ network assignment was derived by collapsing assignments across thresholds, giving each node the assignment it had at the sparsest possible threshold at which it was successfully assigned to one of the known group networks. See Extended Data Fig. 4 and Supplementary Fig. 1 for individual and group mode assignments, respectively. The following networks were included: the association networks including the DMN, fronto-parietal, dorsal attention, parietal memory, ventral attention, action-mode, salience and context networks; and the primary networks including the visual, somato-motor, somato-motor face and auditory networks.

To compute local (areal) desynchronization, we also defined brain areas at the individual level using a previously described areal parcellation approach 39 . In brief, for each participant, vertex-wise FC was averaged across all sessions to generate a dense connectome. Then, abrupt transitions in FC values across neighbouring vertices were used to identify boundaries between distinct functional areas.

To take advantage of the multilevel precision functional mapping study design, a LME model was used. Every scan was labelled on the following dimensions: participant identity (ID), MRI visit, task (task or rest), drug condition (prepsilocybin, psilocybin, MTP, postpsilocybin) and head motion (average framewise displacement). The rs-fMRI metrics (described below) were set as the dependent variable, drug (drug condition), task, framewise displacement (motion) and drug × task were defined as fixed effects, and participant ID and MRI session were random effects.

Let y ij be the rs-fMRI metric (for example, FC change score at a given vertex) for the j th observation (15 min fMRI scan) within the i th participant. The LME model can be written as:

β 0 is the intercept term.

β drug , β FD , β task and β task-by-drug are the coefficients for the fixed effects predictors.

drug ij , frame displacement ij (FD ij ) and task ij are the values of the fixed effects predictors for the j th observation within the i th group.

u 0 i represents the random intercept for the i th participant, accounting for individual-specific variability.

v 0 j represents the random intercept for the j th observation within the i th participant, capturing scan-specific variability.

ε ij is the error term representing unobserved random variation.

In MATLAB (Wilkinsonian notation), this model is expressed for every vertex Y (vertex) = fitlme(groupd, FC_Change(vertex) ~ drug + framewise displacement + task + task-by-drug + (1 |SubID) + (1 |session)).

To compensate for the implementations of this LME model on many rs-fMRI-related dependent variables, differences were highlighted when P  < 0.001. All P values reported are not corrected for multiple comparisons.

Vertex-wise FC change

FC change (‘distance’) was calculated at the vertex level to generate FC change maps and a LME model (equation ( 1 )) was used in combination with wild bootstrapping 119 , 120 and threshold-free cluster enhancement (TFCE) 95 , 121 to estimate P values for t -statistic maps resulting from the model (Figs. 1a–d and  4 ). Wild bootstrapping is an approach to permutation testing that was designed for models that are not independent and identically distributed, and are heteroscedastic.

First, a FC change map was generated for every scan by computing, for each vertex, the average distance between its FC seedmap and the FC seedmap for each of that participant’s baseline scans. As each participant had several baseline visits, FC change was computed for baseline scans by computing distance from all other baseline scans (excluding scans within the same visit). This provided a measure of day-to-day variability. Second, the distance value was used as the dependent variable y ij in the LME model to generate a t -statistic. Third, a wild bootstrapping procedure was implemented as follows. Several bootstrap samples ( B  = 1,000) were generated using the Rademacher procedure 120 , in which the residuals were randomly inverted. Specifically, a Rademacher vector was generated by randomly assigning −1 or 1 values with equal probability to the residual of each observation. By element-wise multiplication of the original residuals with the Rademacher vector, bootstrap samples were created to capture the variability in the data.

For the observed t -statistic-map and each bootstrap sample, the TFCE algorithm was applied to enhance the sensitivity to clusters of significant voxels or regions while controlling for multiple comparisons. The value of the enhanced cluster statistic derived from the bootstrap samples was used to create a null distribution under the null hypothesis. By comparing the original observed cluster statistic with the null distribution, P values were derived to quantify the statistical significance of the observed effect. The P values were obtained on the basis of the proportion of bootstrap samples that produced a maximum cluster statistic exceeding the observed cluster statistic.

The combined approach of wild bootstrapping with the Rademacher procedure and TFCE provided the method to estimate P values for our multilevel (drug condition, participant, session, task) design. This methodology accounted for the complex correlation structure, effectively controlled for multiple comparisons and accommodated potential autocorrelation in the residuals through the Rademacher procedure. By incorporating these techniques, association with psilocybin and other conditions was reliably identified amid noise and spatial dependencies.

Whole-brain FC change

For analyses in Figs. 1e,g , 2 and 4a (bottom), Extended Data Fig. 3 and Supplementary Figs. 3 , 4 and 6 , distance calculations were computed on the FC matrix using z -transformed bivariate correlation of time courses from parcellated brain areas 62 . The effects of day-to-day, drug condition, task and framewise displacement and drug × task were directly examined by calculating the distance between functional network matrices generated from each scan. Root-mean-squared Euclidean distance was computed between the linearized upper triangles of the parcellated FC matrix between each pair of 15 min fMRI scans, creating a second-order distance matrix (Extended Data Fig. 3 ). Subsequently, the average distance (reported as ‘whole-brain FC change’) was examined for FC matrices that were from the same individual within a single session, from the same individual across days (‘day-to-day’), from the same participant between drug and baseline (for example, psilocybin), from the same individual but different tasks (‘task:rest’), from the same individual between highest motion scans and baseline (‘hi:lo motion’), from different individuals (‘between person’). In the ‘high head motion’ comparison (‘hi:lo motion’ in Supplementary Fig. 3 ), the two non-drug scans with the highest average framewise displacement were labelled and compared against all other baseline scans.

A LME model (equation ( 1 )) and post hoc t -tests were used to assess statistical differences between drug conditions. A related approach using z -transformed bivariate correlation (‘similarity’ rather than distance) was also taken and results were unchanged (Supplementary Fig. 3c ).

Likelihood ratio test of participant-specific response

To test whether variability in participant-specific response to psilocybin was larger than would be expected by chance, we used a likelihood ratio test for variance of random slopes for a participant-specific response to psilocybin 48 . The difference in log likelihood ratios was compared to a null distribution of 1 million draws from a mixture of chi-squared distributions with degrees of freedom 1 and 2. We note that the likelihood ratio test of variance components is a non-standard problem 47 as the covariance matrix of the random effects is positive definite and the variances of random effects are non-negative. Finally, the test statistic for the likelihood ratio in this LME model was compared against a 50/50 mixture of two independent chi-squared distributions, each with one and two degrees of freedom, respectively.

Assessing subjective experience

Subjective experience was assessed for drug sessions using the MEQ30 46  ( Supplementary Methods ). The MEQ30 is designed to capture the core domains of the subjective effects of psychedelics (as compared to the altered states of consciousness rating scales that more broadly assess effects of psychoactive drugs 122 ) and is related to the therapeutic benefits of psychedelics. We applied a LME model across all drug sessions, similar to the one described above, but with MEQ30 total score as the dependent variable. Whole-brain FC change and framewise displacement were modelled as fixed effects, and participant was modelled as a random effect. The same model was solved using FC change from every vertex to generate a vertex-wise map of the FC change versus MEQ30.

Normalized FC change

The conditions above were compared by calculating normalized FC change scores using the following procedure: we (1) determined FC change for each condition compared to baseline as described above, (2) subtracted within-session distance for all conditions (such that within-session FC change was 0), (3) divided all conditions by day-to-day distance (such that day-to-day FC change was equal to 1). Thus, normalized whole-brain FC change values (for example, psilocybin versus base was 3.52) could be thought of as proportional to day-to-day variability.

Data-driven MDS

We used a classical MDS approach to cluster parcellated connectomes across fMRI scans, as previously described 38 . This data-driven approach was used to identify how different parameters (for example, task, drug, individual) affect similarity and/or distance between networks. MDS places data in multidimensional space on the basis of the dissimilarity (Euclidean distance) among data points, which in this case means a data point represents the linearized upper triangle of a FC matrix. Every matrix was entered into the classical MDS algorithm (implemented using MATLAB 2019, cmdscale.m). Many dimensions of the data were explored. The eigenvectors were multiplied by the original FC matrices to generate a matrix of eigenweights that corresponded to each dimension. These eigenweights were also applied to other rs-fMRI psychedelics datasets to generate dimensions scores (section ‘Other datasets’).

Rotation-based null model (spin test) for network specificity

To assess network specificity of FC change values, we calculated average FC change of matched null networks consisting of randomly rotated networks with preserved size, shape and relative position to each other 62 , 97 . To create matched random networks, we rotated each hemisphere of the original networks a random amount around the x , y and z axes on the spherical expansion of the cortical surface 62 . This procedure randomly relocated each network while maintaining networks’ sizes, shapes and relative positions to each other. Random rotation followed by computation of network-average FC change score was repeated 1,000 times to generate null distributions of FC change scores. Vertices rotated into the medial wall were not included in the calculation. Actual psilocybin FC change was then compared to null rotation permutations to generate a P value for the 12 networks that were consistently present across every participant’s Infomap parcellation. For bar graph visualization (Fig. 1 and Supplementary Fig. 1b ), networks with greater change ( P  < 0.05 based on null rotation permutations) are shown in their respective colour and other networks are shown in grey.

We used an approach previously validated to assess spatial complexity (termed entropy) or neural signals 61 . Temporal principal component analysis was conducted on the full BOLD dense timeseries, which yielded m principal components ( m roughly 80 K surface vertices and subcortical voxels) and associated eigenvalues. The normalized eigenvalue of the i th principal component was calculated as

where m is the number of principal components, and λ i and λ ′ i represent the eigenvalue and the normalized eigenvalue of the i th principal component, respectively. Last, the NGSC, defined as the normalized entropy of normalized eigenvalues, was computed using the equation:

The NGSC computed above attains values from the interval 0 to 1. The lowest value NGSC = 0 would mean the brain-wide BOLD signal consisted of exactly one principal component or spatial mode, and there is maximum global FC between all vertices. The highest value NGSC = 1 would mean the total data variance is uniformly distributed across all m principal components, and a maximum spatial complexity or a lowest FC is found.

NGSC was additionally calculated at the ‘parcel level’. To respect areal boundaries, this was done by first generating a set of individual-specific parcels in every participant (on all available resting fMRI sessions concatenated) using procedures described oreviously 39 , 62 .

NGSC maps were compared to PET-based 5-HT 2A receptor binding maps published in ref. 33 . Similarity was assessed by computing the bivariate correlation between NGSC values and 5-HT 2A binding across 324 cortical parcels from the Gordon–Laumann parcellation.

Persistent effects analysis

To assess the persistent effects of psilocybin, we compared FC changes 1–21 days postpsilocybin to predrug baseline. The FC change analysis (described above) indicated that connectivity at the whole-brain level did not change following psilocybin (Supplementary Fig. 1 ). A screen was conducted with P  < 0.05 threshold to identify brain networks or areas showing persistent effects. This analysis identified the anterior hippocampus as a candidate region of interest for persistent FC change (section ‘Baseline/after psilocybin FC change analysis’ in  Supplementary Methods ).

We assessed change in anterior hippocampus ‘FC change’ pre- versus postpsilocybin using the LME model described previously. In this model, all sessions before psilocybin (irrespective or cross-over order) were labelled as prepsilocybin and all sessions within 21 days after psilocybin were labelled as postpsilocybin.

As a control, we tested anterior hippocampus FC change pre- versus post-MTP using both the LME model, and an equivalence test. To control for potential persistent psilocybin effects, only the block of scans immediately before and after MTP were used (for example, if a participant took MTP as drug 1, then all baseline scans were labelled as ‘pre-MTP’ and all scans between drugs 1 and 2 were labelled ‘post-MTP’).

Equivalence testing (to conclude no change in anterior hippocampus after MTP) was accomplished by setting δ  = 0.5 standard deviation of FC change across pre-MTP sessions. We computed the 90% CI of change in FC change between pre- and post-MTP sessions. If the bounds of the 90% CI were within ± δ , then equivalence was determined 123 .

Other datasets

Raw fMRI and structural data published previously 55 , 56 were run through our in-house registration and processing pipeline described above. These datasets were used for replication, external validation and generalization to another classic psychedelic (that is, LSD) for the measures described above (for example, NGSC and the MDS-derived psilocybin FC dimension, dimension 1).

Using the data from ref. 55 : n  = 15 healthy adults (five women, mean age 34.1 years, s.d. 8.2) completed two scanning sessions (psilocybin and saline) that included an eyes-closed resting-state BOLD scan for 6 min before and following i.v. infusion of drug. fMRI data were acquired using a gradient-echo-planar imaging sequence, TR and TE of 3,000 and 35 ms, field-of-view 192 mm, 64 × 64 acquisition matrix, parallel acceleration factor of 2 and 90° flip angle.

Using the data from ref. 56 : healthy adults completed two scanning sessions (LSD and saline), which included an eyes-closed resting-state BOLD scan acquired for 22 min following i.v. drug infusion lasting 12 min. n  = 20 participants completed the protocol, but data were used for n  = 15 (four women; mean age 30.5, standard deviation 8.0) deemed suitable for BOLD analyses. fMRI data were acquired using a gradient-echo-planar imaging sequence, TR and TE of 2,000 and 35 ms, field-of-view 220 mm, 64 × 64 acquisition matrix, parallel acceleration factor of 2, 90° flip angle and 3.4 mm isotropic voxels.

The ABCD database resting-state functional MRI 59 (annual release v.2.0, https://doi.org/10.15154/1503209 ) was used to replicate the effects of stimulant use on FC. Preprocessing included framewise censoring with a criterion of frame displacement less than or equal to 0.2 mm in addition the standard predefined preprocessing procedures 124 . Participants with fewer than 600 frames (equivalent to 8 min of data after censoring) were excluded from the analysis. Parcel-wise group-averaged FC matrices were constructed for each participant as described above for 385 regions on inter-test in the brain.

Use of a stimulant (for example, MTP, amphetamine salts, lisdexamfetamine) in the last 24 h was assessed by parental report. Participants with missing data were excluded. Regression analysis was used to assess the relationship between FC (edges) and stimulant use in the last 24 h. Framewise displacement (averaged over frames remaining after censoring) was used as a covariate to account for motion-related effects. The t -values that reflect the relationship between stimulant use and FC were visualized on a colour scale from −5 to +5 to provide a qualitative information about effect of stimulant use on FC.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data from individual participants P1–P7 are available at https://wustl.box.com/v/PsilocybinPFM , with a password available on completion of a data use agreement. The ABCD data used in this report came from ABCD the Annual Release 2.0, https://doi.org/10.15154/1503209 . The ABCD data repository grows and changes over time ( https://nda.nih.gov/abcd ). The Imperial College London psilocybin and LSD datasets are available upon request.

Code availability

Data processing code for the psilocybin precision functional mapping data can be found at https://wustl.box.com/s/dmj5s3h9pxt9bcw9mm3ai9c15y756o79 . Code specific to analyses can be found at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/ . Data processing code for the ABCD data can be found at https://github.com/DCAN-Labs/abcd-hcp-pipeline . Matching task stimuli are available at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/-/blob/main/image_task_clean.zip . Software packages incorporated into the above pipelines for data analysis included: MATLAB R2019b, https://www.mathworks.com/ (including Psychtoolbox v.2.0 and Statistics and Machine Learning Toolbox v.11.6); Connectome Workbench v.1.5; http://www.humanconnectome.org/software/connectome-workbench.html ; Freesurfer v.6.2, https://surfer.nmr.mgh.harvard.edu/ ; FSL v.6.0, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ; 4dfp tools, https://4dfp.readthedocs.io/en/latest/ ; Infomap, https://www.mapequation.org ; Cifti MATLAB utilities (including spin test): https://github.com/MidnightScanClub/SCAN and 4dfp tools, https://4dfp.readthedocs.io/en/latest/ . MRI pulse sequences used to acquire the data are provided at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/-/blob/main/NP1161_MRI_sequence.pdf .

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Acknowledgements

This work was supported by the Taylor Family Institute Fund for Innovative Psychiatric Research (J. S. Siegel, T.O.L., G.E.N.); the McDonnell Center for Systems Neuroscience (J. S. Siegel, G.E.N.); the Institute of Clinical and Translational Science (G.E.N.); National Institutes of Health (NIH) grants MH112473 (J. S. Siegel, S.S., D.A.B., C.H., G.E.N.), T32 DA007261 (J. S. Siegel), NS123345 (B.P.K.), MH129616 (T.O.L.), MH121276 (N.U.F.D., E.M.G., D.A.F.), MH118370 (C.G.), NS124738 (C.G.), MH096773 (D.A.F., N.U.F.D.), MH122066 (D.A.F., E.M.G., N.U.F.D.), MH124567 (D.A.F., E.M.G., N.U.F.D.), NS129521 (E.M.G., D.A.F., N.U.F.D.) and NS088590 (N.U.F.D.); the National Spasmodic Dysphonia Association (E.M.G.); the Ralph Metzner Professorship and the Tianqiao and Chrissy Chen Institute (R.C.-H.); the Intellectual and Developmental Disabilities Research Center (N.U.F.D.); by the Kiwanis Foundation (N.U.F.D.); the Washington University Hope Center for Neurological Disorders (E.M.G., N.U.F.D.) and by Mallinckrodt Institute of Radiology pilot funding (E.M.G., N.U.F.D.). Furthermore, this study used data from the ABCD study, supported by National Institutes of Health grant no. U01DA041120. We give a special thanks to our study participants, who completed a demanding protocol with grace for the benefit of scientific inquiry. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/ . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Author information

These authors contributed equally: Ginger E. Nicol, Nico U. F. Dosenbach

Authors and Affiliations

Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

Joshua S. Siegel, Demetrius Perry, Timothy O. Laumann, Julie A. Schweiger, David A. Bender, Eric J. Lenze & Ginger E. Nicol

Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA

Subha Subramanian

Department of Neurology, Washington University School of Medicine, St Louis, MO, USA

Benjamin P. Kay, Nicholas V. Metcalf, Samuel R. Krimmel, Kristen M. Scheidter, Forrest I. Whiting, Marcus E. Raichle, Abraham Z. Snyder & Nico U. F. Dosenbach

Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA

Evan M. Gordon, T. Rick Reneau, Joshua S. Shimony, Dean F. Wong, Marcus E. Raichle, Abraham Z. Snyder & Nico U. F. Dosenbach

Department of Emergency Medicine, Advocate Christ Health Care, Oak Lawn, IL, USA

Ravi V. Chacko

Department of Psychology, Florida State University, Tallahassee, FL, USA

Caterina Gratton

Miami VA Medical Center, Miami, FL, USA

Christine Horan

Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA

Jonah A. Padawer-Curry, Marcus E. Raichle & Nico U. F. Dosenbach

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA

Russell T. Shinohara

Penn Statistics in Imaging and Visualization Endeavor, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Russell T. Shinohara & Yong Chen

Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA

Julia Moser, Steven M. Nelson & Damien A. Fair

Institute of Child Development, University of Minnesota, Minneapolis, MN, USA

Julia Moser & Damien A. Fair

Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA

Essa Yacoub, Luca Vizioli & Damien A. Fair

Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA

Steven M. Nelson & Damien A. Fair

Department of Neurology, University of California, San Francisco, CA, USA

Robin Carhart-Harris

Centre for Psychedelic Research, Imperial College London, London, UK

Usona Institute, Fitchburg, WI, USA

Charles L. Raison

Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA

Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA

Marcus E. Raichle & Nico U. F. Dosenbach

Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA

Marcus E. Raichle

Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA

Nico U. F. Dosenbach

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Contributions

The concept came from J. S. Siegel and G.E.N. The study was designed by J. S. Siegel, S.S., T.O.L., C.L.R., E.J.L., A.Z.S. and G.E.N. Data acquisition and processing were done by J. S. Siegel, S.S., T.R.R., D.P., C.H., J. S. Shimony, J.A.S., D.A.B., K.M.S., F.I.W., J.M., E.Y., S.M.N., L.V., D.A.F. and A.Z.S.. Data analysis and interpretation were carried out by J. S. Siegel, B.P.K., E.M.G., T.O.L., N.V.M., C.G., R.V.C., S.R.K., D.F.W., J.A.P.-C., R.T.S., Y.C., R.C.-H., M.E.R., G.E.N. and N.U.F.D. The paper was written and revised by J. S. Siegel, S.S., M.E.R., A.Z.S., G.E.N. and N.U.F.D. Participant 7 was author N.U.F.D.

Corresponding author

Correspondence to Joshua S. Siegel .

Ethics declarations

Competing interests.

Within the past year, J. S. Siegel has been an employee of Sumitomo Pharma America and received consulting fees from Longitude Capital. J. S. Siegel, N.U.F.D., T.O.L. and E.M.G. have submitted a provisional patent (patent no. 020949/US 15060-1787) for the use of precision functional mapping for measuring target engagement by experimental therapeutics. R.T.S. has received consulting compensation from Octave Bioscience and compensation for reviewership duties from the American Medical Association. C.L.R. serves as a consultant to Usona Institute and Novartis and receives research support from the Tiny Blue Dot Foundation. G.E.N. has received research support from Usona Institute (drug only). She has served as a paid consultant for Carelon, Alkermes, Inc., Sunovion Pharmaceuticals, Inc. and Novartis Pharmaceuticals Corp. T.O.L. holds a patent for taskless mapping of brain activity licenced to Sora Neurosciences and a patent for optimizing targets for neuromodulation, implant localization and ablation is pending. J. S. Siegel is a consultant and received stock options in Sora Neuroscience, and company that focuses on resting-state analysis. D.A.F. and N.U.F.D. are cofounders of Turing Medical Inc, have financial interest, may benefit financially if the company is successful in marketing FIRMM motion monitoring software products, may receive royalty income based on FIRMM technology developed at WUSOM and licenced to Turing Medical Inc. S.M.N., E.M.G. and T.O.L. have received consulting fees from Turing Medical Inc. D.F.W. is a consultant for Engrail Therapeutics and receives contract funds for WUSOM research studies from Eisai, Anavex and Roche. These potential conflicts of interest have been reviewed and are managed by WUSOM. The other authors declare no competing interests. All authors report no financial interest in psychedelics companies.

Peer review

Peer review information.

Nature thanks Charles Lynch, Petros Petridis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 quantifying psilocybin effects with precision functional mapping: design..

a) Schematic illustrating the study protocol of the individual-specific precision functional mapping study of acute and persistent effects of psilocybin (single dose: 25 mg). Repeated longitudinal study visits enabled high-fidelity individual brain mapping, measurement of day-to-day variance, and acclimation to the scanner. The open label replication protocol 6-12 months later included one or two scans each of baseline, psilocybin, and after drug. b) Timeline of imaging visits for 7 participants. c) Head motion comparisons across psychedelics studies 55 , 56 . Average head motion (FD, framewise displacement, in mm) off and on drug compared between our dataset and prior psychedelic fMRI studies. Unpaired two-sided t -test: n PSIL 2012  = 15, n LSD,2016  = 20, n PSIL(PFM)  = 7; off drug PSIL2012-PSIL(PFM) t (274)  = −4.57, P uncorr  = 7.33 × 10 −5 ; off drug LSD2016-PSIL(PFM) t (286)  = −4.03, P uncorr  = 7.34 × 10 −6 ; on drug PSIL2012-PSIL(PFM) t (46)  = −1.80, P uncorr  = 0.079; on drug LSD2016-PSIL(PFM) t (88)  = −0.73, P uncorr  = 0.46. Dotted line at FD of 0.2 mm. Dark gray bars indicate quartiles, light gray violins indicate distribution. * P uncorr  < 0.05, + P  < 0.1. d) Timeline for an example participant (P1). e) Participants reported significantly higher scores on all dimensions of the mystical experience questionnaire during psilocybin (red) than placebo (40 mg methylphenidate; blue). Paired two-tailed t -test, n  = 7; Mystical t (6)  = −3.64, P uncorr  = 0.011; Positive Mood t (6)  = −5.44, P uncorr  = 0.0016; Transcendence t (6)  = −4.98, P uncorr  = 0.0025; Ineffability t (6)  = −2.54, P uncorr  = 0.044. Error bars indicate SEM.

Extended Data Fig. 2 Unthresholded vertex-wise FC change maps.

T-statistic maps, resulting from the linear mixed effects (LME) model based on vertex-wise FC change (Euclidean distance from baseline scans) across the cortex and subcortical structures for every scan. Higher t values indicate a larger change from baseline (pre-drug) scans. Effects of drug condition (baseline, psilocybin, methylphenidate, post-psilocybin, post-methylphenidate), were modeled as fixed effects. For example, if drug 1 was psilocybin and drug 2 was methylphenidate, then scans between drug visits were labeled post-psilocybin and scans after drug 2 were labeled post-methylphenidate.

Extended Data Fig. 3 Functional connectivity (FC) distance and condition matrices for all fMRI scans.

Following Gratton et al. 38 , we compared FC matrices between rs-fMRI sessions to quantify contributors to variability in whole-brain FC. Under this approach, the effects of group, individual, session, and drug (as well as their interactions) are examined by first calculating the Euclidean distance among every pair of FC matrices (i.e., distance among the linearized upper triangles). a) In the resulting second-order distance matrix, each row and column show whole-brain FC from a single study visit. The colours in the matrix indicate distance between functional networks for a pair of visits (i.e., Euclidean distance between the linearized upper triangles of two FC matrices). Panels b and c demonstrate how the distance matrix was subdivided to compare different conditions. b) Black triangles represent distinct individuals. Replication protocol visits are listed at the end. c ) Task and rest scans are shown in white and orange, respectively. Note that psilocybin scans (black arrows pointing to P1 psilocybin scans in panel a are very dissimilar to no-drug scans from the same individual (left arrow; in a ) but have heightened similarity to psilocybin scans from other individuals (right arrow in a ).

Extended Data Fig. 4 Participant-specific FC change maps for drug sessions.

Individual participant methylphenidate (MTP) and psilocybin (PSIL) FC change maps. Left most column shows individuals’ functional networks. Right 3 columns show FC change maps, generated by calculating Euclidean distance from baseline seedmaps for each vertex. For each session the total score on the Mystical Experience Questionnaire (MEQ30: out of a maximum of 150) is given in the upper right corner. *P5 had an episode of emesis 30 minutes after drug ingestion during PSIL2.

Extended Data Fig. 5 Multi-dimensional scaling, dimension edge weights.

a) Group parcellation (324 cortical and 61 subcortical parcels) 31 b) Weights from the first 4 dimensions generated by multi-dimensional scaling of the full dataset. The color of each pixel in the plot represents the weight of a given edge. Dimension 1 captures the loss of network integration (on diagonal boxes) and segregation (off diagonal boxes) of psilocybin. Dimensions 2 and 3 primarily explain individual differences and do not show network patterns as clearly. Dimension 4 captures shared effects of psilocybin (PSIL) and methylphenidate (MTP) on sensorimotor systems (suspected arousal effects).

Extended Data Fig. 6 Average functional connectivity (FC) matrices by condition.

a) Group parcellation (324 cortical and 61 subcortical parcels) 31 . b) Average FC matrices and condition differences. Top left shows the group average FC adjacency matrix. Bottom left shows the effect of psilocybin, e.g. increased correlation between dorsal attention, fronto-parietal, and default mode network to each other and to other cortical, limbic, and cerebellar systems. Top right shows effect of methylphenidate. For comparison and validation, we compared methylphenidate to the main effect of stimulant use within the last 24 hours (bottom right, n  = 487 yes, n  = 7992 no) in ABCD rs-fMRI data (bottom right).

Extended Data Fig. 7 Correlations with mystical experience scores.

Comparison of MEQ30 score (y-axes) to global desynchronization (top left; NGSC change, drug minus baseline), head motion (bottom left; framewise displacement (FD) in mm), heart rate change (top right; drug minus baseline), and respiratory rate change (bottom right; drug minus baseline), for all drug sessions. Statistics ( rho , P ) are based on bivariate correlation, two-sided, uncorrected. In the case of Δ NGSC, statistics are reported before and after the removal of an outlier point (> 2 SD lower than mean, indicated by the gray arrow).

Extended Data Fig. 8 Auditory-visual matching fMRI task.

a) Schematic of auditory/visual matching task design. b) Comparison of performance (‘No Drug’ and psilocybin conditions are at ceiling). Lines indicate means and standard deviation across sessions. Number of task sessions are indicated in Supplementary Table 1 . c) Comparison of reaction time (RT). Lines indicate mean and standard deviation across all trials (48 trials per session). d) Task fMRI activation maps (beta weights) and e) contrasts (simple subtraction) using the canonical hemodynamic response function (HRF). f) Eight a priori regions of interest for timecourse analyses. g) Average timecourses from the regions of interest shown in panel f , calculated using finite impulse response model over 13 TR x 1.761 s/TR = 22.89 seconds, for all trials. Shaded area around each line indicates SEM. ANOVAN of Condition x HRF Beta (Main effect of all trials) magnitude testing effect of drug, two-sided: Left V1, F (2,40)  = 3.91, P  = 0.030; Right V1, F (2,40)  = 4.40, P  = 0.020; Left M1 hand, F (2,40)  = 0.40, P  = 0.68; Left Auditory A1, F (2,40)  = 0.22, P  = 0.81; Right Auditory A1, F (2,40)  = 0.77, P  = 0.47; Left Language, F (2,40)  = 0.025, P  = 0.98; Left DMN, F (2,40)  = 1.15, P  = 0.33; Right DMN, F (2,40)  = 0.14, P  = 0.87. * P  < 0.05. P-values are uncorrected for multiple comparisons.

Supplementary information

Supplementary information.

This file contains Supplementary Table 1, Figs. 1–7 and Methods.

Reporting Summary

Peer review file, supplementary video 1.

Quality control plots for every fMRI scan. For each participant (P1, P3–P7, concatenated) the quality control plots are concatenated in the order that the scans were acquired (Extended Data Fig. 1). The top plot shows head position (frame-by-frame, relative to frame 1) separated into x , y , z translation and x , y , z rotation (six parameters). The second plot from the top shows DVARS, which index the rate of change of fMRI signal across the entire brain at each frame of data. The D refers to the temporal derivative of time courses, and VARS refers to the root-mean-square variance over voxels. The third plot shows head motion measured as FD (framewise displacement) in mm. Underneath in the fourth row, the time course for the whole-brain grayordinates (cortex on top, subcortex on the bottom) are shown before preprocessing (known as ‘grayplot’ or ‘carpet plot’). The fifth row shows the same grayordinates, but after preprocessing (bandpass filtering, removal of nuisance signals by regression, and smoothing at 4 mm full-width at half-maximum). The vertical black lines or bars in the grayplots indicate these data frames that were censored due to excessive head motion. At the end, quality control plots are compared to physiology (heart rate, respiratory rate) plots for every session in which physiological monitoring data were acquired.

Supplementary Video 2

Time series of fully preprocessed resting-state fMRI (rs-fMRI) data (roughly 9 min), taken from the first resting scan of the MRI session. Frame-by-frame rs-fMRI data, excluding high head motion frames (FD > 0.3), are shown for the drug scans (psilocybin, MTP) for each participant (P1, P3–P7).

Supplementary Video 3

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The economic potential of generative AI: The next productivity frontier

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.

About the authors

This article is a collaborative effort by Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel , representing views from QuantumBlack, AI by McKinsey; McKinsey Digital; the McKinsey Technology Council; the McKinsey Global Institute; and McKinsey’s Growth, Marketing & Sales Practice.

The speed at which generative AI technology is developing isn’t making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. 1 “Introducing ChatGPT,” OpenAI, November 30, 2022; “GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses,” OpenAI, accessed June 1, 2023. Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. 2 “Introducing Claude,” Anthropic PBC, March 14, 2023; “Introducing 100K Context Windows,” Anthropic PBC, May 11, 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products. 3 Emma Roth, “The nine biggest announcements from Google I/O 2023,” The Verge , May 10, 2023.

To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.

Future frontiers: Navigating the next wave of tech innovations

Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond.

Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks.

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. The following sections share our initial findings.

For the full version of this report, download the PDF .

Key insights

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.

About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks.

Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.

Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working. 4 “ Harnessing automation for a future that works ,” McKinsey Global Institute, January 12, 2017. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.

The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.

Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.

The era of generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.

Where business value lies

Generative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1).

The first lens scans use cases for generative AI that organizations could adopt. We define a “use case” as a targeted application of generative AI to a specific business challenge, resulting in one or more measurable outcomes. For example, a use case in marketing is the application of generative AI to generate creative content such as personalized emails, the measurable outcomes of which potentially include reductions in the cost of generating such content and increases in revenue from the enhanced effectiveness of higher-quality content at scale. We identified 63 generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries.

That would add 15 to 40 percent to the $11 trillion to $17.7 trillion of economic value that we now estimate nongenerative artificial intelligence and analytics could unlock. (Our previous estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.)

Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce.

Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. Netting out this overlap, the total economic benefits of generative AI —including the major use cases we explored and the myriad increases in productivity that are likely to materialize when the technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to $7.9 trillion annually (Exhibit 2).

How we estimated the value potential of generative AI use cases

To assess the potential value of generative AI, we updated a proprietary McKinsey database of potential AI use cases and drew on the experience of more than 100 experts in industries and their business functions. 1 ” Notes from the AI frontier: Applications and value of deep learning ,” McKinsey Global Institute, April 17, 2018.

Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies.

We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis.

We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures.

Our estimates are based on the structure of the global economy in 2022 and do not consider the value generative AI could create if it produced entirely new product or service categories.

While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”).

In this section, we highlight the value potential of generative AI across business functions.

Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases.

Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower. 5 Pitchbook. This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies.

In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task.

In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks.

Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator.

Customer operations: Improving customer and agent experiences

Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14 percent an hour and reduced the time spent handling an issue by 9 percent. 1 Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, Generative AI at work , National Bureau of Economic Research working paper number 31161, April 2023. It also reduced agent attrition and requests to speak to a manager by 25 percent. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts.

The following are examples of the operational improvements generative AI can have for specific use cases:

• Customer self-service. Generative AI–fueled chatbots can give immediate and personalized responses to complex customer inquiries regardless of the language or location of the customer. By improving the quality and effectiveness of interactions via automated channels, generative AI could automate responses to a higher percentage of customer inquiries, enabling customer care teams to take on inquiries that can only be resolved by a human agent. Our research found that roughly half of customer contacts made by banking, telecommunications, and utilities companies in North America are already handled by machines, including but not exclusively AI. We estimate that generative AI could further reduce the volume of human-serviced contacts by up to 50 percent, depending on a company’s existing level of automation.
• Resolution during initial contact. Generative AI can instantly retrieve data a company has on a specific customer, which can help a human customer service representative more successfully answer questions and resolve issues during an initial interaction.
• Reduced response time. Generative AI can cut the time a human sales representative spends responding to a customer by providing assistance in real time and recommending next steps.
• Increased sales. Because of its ability to rapidly process data on customers and their browsing histories, the technology can identify product suggestions and deals tailored to customer preferences. Additionally, generative AI can enhance quality assurance and coaching by gathering insights from customer conversations, determining what could be done better, and coaching agents.

We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs.

Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience, including better understanding of the customer’s context that can assist human agents in providing more personalized help and recommendations.

Marketing and sales: Boosting personalization, content creation, and sales productivity

Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalized messages tailored to individual customer interests, preferences, and behaviors, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.

Introducing generative AI to marketing functions requires careful consideration. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs.

Potential operational benefits from using generative AI for marketing include the following:

• Efficient and effective content creation. Generative AI could significantly reduce the time required for ideation and content drafting, saving valuable time and effort. It can also facilitate consistency across different pieces of content, ensuring a uniform brand voice, writing style, and format. Team members can collaborate via generative AI, which can integrate their ideas into a single cohesive piece. This would allow teams to significantly enhance personalization of marketing messages aimed at different customer segments, geographies, and demographics. Mass email campaigns can be instantly translated into as many languages as needed, with different imagery and messaging depending on the audience. Generative AI’s ability to produce content with varying specifications could increase customer value, attraction, conversion, and retention over a lifetime and at a scale beyond what is currently possible through traditional techniques.
• Enhanced use of data. Generative AI could help marketing functions overcome the challenges of unstructured, inconsistent, and disconnected data—for example, from different databases—by interpreting abstract data sources such as text, image, and varying structures. It can help marketers better use data such as territory performance, synthesized customer feedback, and customer behavior to generate data-informed marketing strategies such as targeted customer profiles and channel recommendations. Such tools could identify and synthesize trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.
• SEO optimization. Generative AI can help marketers achieve higher conversion and lower cost through search engine optimization (SEO) for marketing and sales technical components such as page titles, image tags, and URLs. It can synthesize key SEO tokens, support specialists in SEO digital content creation, and distribute targeted content to customers.
• Product discovery and search personalization. With generative AI, product discovery and search can be personalized with multimodal inputs from text, images, and speech, and a deep understanding of customer profiles. For example, technology can leverage individual user preferences, behavior, and purchase history to help customers discover the most relevant products and generate personalized product descriptions. This would allow CPG, travel, and retail companies to improve their e-commerce sales by achieving higher website conversion rates.

We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending.

Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies.

Generative AI could also change the way both B2B and B2C companies approach sales. The following are two use cases for sales:

• Increase probability of sale. Generative AI could identify and prioritize sales leads by creating comprehensive consumer profiles from structured and unstructured data and suggesting actions to staff to improve client engagement at every point of contact. For example, generative AI could provide better information about client preferences, potentially improving close rates.
• Improve lead development. Generative AI could help sales representatives nurture leads by synthesizing relevant product sales information and customer profiles and creating discussion scripts to facilitate customer conversation, including up- and cross-selling talking points. It could also automate sales follow-ups and passively nurture leads until clients are ready for direct interaction with a human sales agent.

Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures.

This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success.

Software engineering: Speeding developer work as a coding assistant

Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do.

Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.

According to our analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. 1 Peter Cihon et al., The impact of AI on developer productivity: Evidence from GitHub Copilot , Cornell University arXiv software engineering working paper, arXiv:2302.06590, February 13, 2023. An internal McKinsey empirical study of software engineering teams found those who were trained to use generative AI tools rapidly reduced the time needed to generate and refactor code—and engineers also reported a better work experience, citing improvements in happiness, flow, and fulfillment.

Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain. However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce.

Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders. 2 Michael Nuñez, “Google and Replit join forces to challenge Microsoft in coding tools,” VentureBeat, March 28, 2023.

Product R&D: Reducing research and design time, improving simulation and testing

Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs.

For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others.

While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries.

In addition to the productivity gains that result from being able to quickly produce candidate designs, generative design can also enable improvements in the designs themselves, as in the following examples of the operational improvements generative AI could bring:

• Enhanced design. Generative AI can help product designers reduce costs by selecting and using materials more efficiently. It can also optimize designs for manufacturing, which can lead to cost reductions in logistics and production.
• Improved product testing and quality. Using generative AI in generative design can produce a higher-quality product, resulting in increased attractiveness and market appeal. Generative AI can help to reduce testing time of complex systems and accelerate trial phases involving customer testing through its ability to draft scenarios and profile testing candidates.

We also identified a new R&D use case for nongenerative AI: deep learning surrogates, the use of which has grown since our earlier research, can be paired with generative AI to produce even greater benefits. To be sure, integration will require the development of specific solutions, but the value could be significant because deep learning surrogates have the potential to accelerate the testing of designs proposed by generative AI.

While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall.

Industry impacts

Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4).

For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5).

In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development.

We share our detailed analysis of these industries below.

Generative AI supports key value drivers in retail and consumer packaged goods

The technology could generate value for the retail and consumer packaged goods (CPG) industry by increasing productivity by 1.2 to 2.0 percent of annual revenues, or an additional $400 billion to $660 billion. 1 Vehicular retail is included as part of our overall retail analysis. To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise.

Generative AI at work in retail and CPG

Reinvention of the customer interaction pattern.

Consumers increasingly seek customization in everything from clothing and cosmetics to curated shopping experiences, personalized outreach, and food—and generative AI can improve that experience. Generative AI can aggregate market data to test concepts, ideas, and models. Stitch Fix, which uses algorithms to suggest style choices to its customers, has experimented with DALL·E to visualize products based on customer preferences regarding color, fabric, and style. Using text-to-image generation, the company’s stylists can visualize an article of clothing based on a consumer’s preferences and then identify a similar article among Stitch Fix’s inventory.

Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI.

Accelerating the creation of value in key areas

Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates.

Rapid resolution and enhanced insights in customer care

The growth of e-commerce also elevates the importance of effective consumer interactions. Retailers can combine existing AI tools with generative AI to enhance the capabilities of chatbots, enabling them to better mimic the interaction style of human agents—for example, by responding directly to a customer’s query, tracking or canceling an order, offering discounts, and upselling. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information.

Disruptive and creative innovation

Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. This technology is developing rapidly and has the potential to add text-to-video generation.

Factors for retail and CPG organizations to consider

As retail and CPG executives explore how to integrate generative AI in their operations, they should keep in mind several factors that could affect their ability to capture value from the technology:

• External inference. Generative AI has increased the need to understand whether generated content is based on fact or inference, requiring a new level of quality control.
• Adversarial attacks. Foundation models are a prime target for attack by hackers and other bad actors, increasing the variety of potential security vulnerabilities and privacy risks.

To address these concerns, retail and CPG companies will need to strategically keep humans in the loop and ensure security and privacy are top considerations for any implementation. Companies will need to institute new quality checks for processes previously handled by humans, such as emails written by customer reps, and perform more-detailed quality checks on AI-assisted processes such as product design.

Why banks could realize significant value

Generative AI could have a significant impact on the banking industry , generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk.

Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations. 1 “ Building the AI bank of the future ,” McKinsey, May 2021. Generative AI applications could deliver additional benefits, especially because text modalities are prevalent in areas such as regulations and programming language, and the industry is customer facing, with many B2C and small-business customers. 2 McKinsey’s Global Banking Annual Review , December 1, 2022.

Several characteristics position the industry for the integration of generative AI applications:

• Sustained digitization efforts along with legacy IT systems. Banks have been investing in technology for decades, accumulating a significant amount of technical debt along with a siloed and complex IT architecture. 3 Akhil Babbar, Raghavan Janardhanan, Remy Paternoster, and Henning Soller, “ Why most digital banking transformations fail—and how to flip the odds ,” McKinsey, April 11, 2023.
• Large customer-facing workforces. Banking relies on a large number of service representatives such as call-center agents and wealth management financial advisers.
• A stringent regulatory environment. As a heavily regulated industry, banking has a substantial number of risk, compliance, and legal needs.
• White-collar industry. Generative AI’s impact could span the organization, assisting all employees in writing emails, creating business presentations, and other tasks.

Generative AI at work in banking

Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications. Three uses demonstrate its value potential to the industry.

A virtual expert to augment employee performance

A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources. For example, Morgan Stanley is building an AI assistant using GPT-4, with the aim of helping tens of thousands of wealth managers quickly find and synthesize answers from a massive internal knowledge base. 4 Hugh Son, “Morgan Stanley is testing an OpenAI-powered chatbot for its 16,000 financial advisors,” CNBC, March 14, 2023. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment.

One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information. The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.

Generative AI could reduce the significant costs associated with back-office operations. Such customer-facing chatbots could assess user requests and select the best service expert to address them based on characteristics such as topic, level of difficulty, and type of customer. Through generative AI assistants, service professionals could rapidly access all relevant information such as product guides and policies to instantaneously address customer requests.

Code acceleration to reduce tech debt and deliver software faster

Generative AI tools are useful for software development in four broad categories. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools. Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools can review code to identify defects and inefficiencies in computing. The result is more robust, effective code.

Production of tailored content at scale

Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts.

Factors for banks to consider

When exploring how to integrate generative AI into operations, banks can be mindful of a number of factors:

• The level of regulation for different processes. These vary from unregulated processes such as customer service to heavily regulated processes such as credit risk scoring.
• Type of end user. End users vary widely in their expectations and familiarity with generative AI—for example, employees compared with high-net-worth clients.
• Intended level of work automation. AI agents integrated through APIs could act nearly autonomously or as copilots, giving real-time suggestions to agents during customer interactions.
• Data constraints. While public data such as annual reports could be made widely available, there would need to be limits on identifiable details for customers and other internal data.

Pharmaceuticals and medical products could see benefits across the entire value chain

Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D, 1 Research and development in the pharmaceutical industry , Congressional Budget Office, April 2021. and the development of a new drug takes an average of ten to 15 years. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks.

Generative AI at work in pharmaceuticals and medical products

Drug discovery involves narrowing the universe of possible compounds to those that could effectively treat specific conditions. Generative AI’s ability to process massive amounts of data and model options can accelerate output across several use cases:

Improve automation of preliminary screening

In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets. To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization.

Enhance indication finding

An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications.

Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups.

Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process.

Factors for pharmaceuticals and medical products organizations to consider

Before integrating generative AI into operations, pharma executives should be aware of some factors that could limit their ability to capture its benefits:

• The need for a human in the loop. Companies may need to implement new quality checks on processes that shift from humans to generative AI, such as representative-generated emails, or more detailed quality checks on AI-assisted processes, such as drug discovery. The increasing need to verify whether generated content is based on fact or inference elevates the need for a new level of quality control.
• Explainability. A lack of transparency into the origins of generated content and traceability of root data could make it difficult to update models and scan them for potential risks; for instance, a generative AI solution for synthesizing scientific literature may not be able to point to the specific articles or quotes that led it to infer that a new treatment is very popular among physicians. The technology can also “hallucinate,” or generate responses that are obviously incorrect or inappropriate for the context. Systems need to be designed to point to specific articles or data sources, and then do human-in-the-loop checking.
• Privacy considerations. Generative AI’s use of clinical images and medical records could increase the risk that protected health information will leak, potentially violating regulations that require pharma companies to protect patient privacy.

Work and productivity implications

Technology has been changing the anatomy of work for decades. Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually.

These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. At a conceptual level, the application of generative AI may follow the same pattern in the modern workplace, although as we show later in this chapter, the types of activities that generative AI could affect, and the types of occupations with activities that could change, will likely be different as a result of this technology than for older technologies.

The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy.

Technology adoption at scale does not occur overnight. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time.

About the research

This analysis builds on the methodology we established in 2017. We began by examining the US Bureau of Labor Statistics O*Net breakdown of about 850 occupations into roughly 2,100 detailed work activities. For each of these activities, we scored the level of capability necessary to successfully perform the activity against a set of 18 capabilities that have the potential for automation.

We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts.

Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. We modeled the beginning of adoption for a specific detailed work activity in a particular occupation in a country (for 47 countries, accounting for more than 80 percent of the global workforce) when the cost of the automation technology reaches parity with the cost of human labor in that occupation.

Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms.

The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. The reality is likely to fall somewhere between the two.

The analyses in this paper incorporate the potential impact of generative AI on today’s work activities. The new capabilities of generative AI, combined with previous technologies and integrated into corporate operations around the world, could accelerate the potential for technical automation of individual activities and the adoption of technologies that augment the capabilities of the workforce. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”).

Automation potential has accelerated, but adoption to lag

Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023.

As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities.

Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7).

Our analysis of adoption scenarios accounts for the time required to integrate technological capabilities into solutions that can automate individual work activities; the cost of these technologies compared with that of human labor in different occupations and countries around the world; and the time it has taken for technologies to diffuse across the economy. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8).

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance.

Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. Our updated adoption scenarios, which account for developments in generative AI, models the time spent on 2023 work activities reaching 50 percent automation between 2030 and 2060, with a midpoint of 2045—an acceleration of roughly a decade compared with the previous estimate. 6 The comparison is not exact because the composition of work activities between 2016 and 2023 has changed; for example, some automation has occurred during that time period.

Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9).

Generative AI’s potential impact on knowledge work

Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks.

As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023.

Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language.

As a result, many of the work activities that involve communication, supervision, documentation, and interacting with people in general have the potential to be automated by generative AI, accelerating the transformation of work in occupations such as education and technology, for which automation potential was previously expected to emerge later (Exhibit 11).

Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12).

Another way to interpret this result is that generative AI will challenge the attainment of multiyear degree credentials as an indicator of skills, and others have advocated for taking a more skills-based approach to workforce development in order to create more equitable, efficient workforce training and matching systems. 7 A more skills-based approach to workforce development predates the emergence of generative AI. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do.

Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles.

However, generative AI’s impact is likely to most transform the work of higher-wage knowledge workers because of advances in the technical automation potential of their activities, which were previously considered to be relatively immune from automation (Exhibit 13).

Generative AI could propel higher productivity growth

Global economic growth was slower from 2012 to 2022 than in the two preceding decades. 8 Global economic prospects , World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth.

Declining employment is among those obstacles. Compound annual growth in the total number of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8 percent in 2012–22, largely because of aging. In many large countries, the size of the workforce is already declining. 9 Yaron Shamir, “Three factors contributing to fewer people in the workforce,” Forbes , April 7, 2022. Productivity, which measures output relative to input, or the value of goods and services produced divided by the amount of labor, capital, and other resources required to produce them, was the main engine of economic growth in the three decades from 1992 to 2022 (Exhibit 14). However, since then, productivity growth has slowed in tandem with slowing employment growth, confounding economists and policy makers. 10 “The U.S. productivity slowdown: an economy-wide and industry-level analysis,” Monthly Labor Review, US Bureau of Labor Statistics, April 2021; Kweilin Ellingrud, “ Turning around the productivity slowdown ,” McKinsey Global Institute, September 13, 2022.

The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. Based on our estimates, the automation of individual work activities enabled by these technologies could provide the global economy with an annual productivity boost of 0.5 to 3.4 percent from 2023 to 2040, depending on the rate of automation adoption—with generative AI contributing 0.1 to 0.6 percentage points of that growth—but only if individuals affected by the technology were to shift to other work activities that at least match their 2022 productivity levels (Exhibit 15). In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations.

Considerations for business and society

History has shown that new technologies have the potential to reshape societies. Artificial intelligence has already changed the way we live and work—for example, it can help our phones (mostly) understand what we say, or draft emails. Mostly, however, AI has remained behind the scenes, optimizing business processes or making recommendations about the next product to buy. The rapid development of generative AI is likely to significantly augment the impact of AI overall, generating trillions of dollars of additional value each year and transforming the nature of work.

But the technology could also deliver new and significant challenges. Stakeholders must act—and quickly, given the pace at which generative AI could be adopted—to prepare to address both the opportunities and the risks. Risks have already surfaced, including concerns about the content that generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism” in the training data used to create foundation models? Will the answers that LLMs produce when questioned be accurate, and can they be explained? Will the content generative AI creates be fair or biased in ways that users do not want by, say, producing content that reflects harmful stereotypes?

Using generative AI responsibly

Generative AI poses a variety of risks. Stakeholders will want to address these risks from the start.

Fairness: Models may generate algorithmic bias due to imperfect training data or decisions made by the engineers developing the models.

Intellectual property (IP): Training data and model outputs can generate significant IP risks, including infringing on copyrighted, trademarked, patented, or otherwise legally protected materials. Even when using a provider’s generative AI tool, organizations will need to understand what data went into training and how it’s used in tool outputs.

Privacy: Privacy concerns could arise if users input information that later ends up in model outputs in a form that makes individuals identifiable. Generative AI could also be used to create and disseminate malicious content such as disinformation, deepfakes, and hate speech.

Security: Generative AI may be used by bad actors to accelerate the sophistication and speed of cyberattacks. It also can be manipulated to provide malicious outputs. For example, through a technique called prompt injection, a third party gives a model new instructions that trick the model into delivering an output unintended by the model producer and end user.

Explainability: Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given answer is produced.

Reliability: Models can produce different answers to the same prompts, impeding the user’s ability to assess the accuracy and reliability of outputs.

Organizational impact: Generative AI may significantly affect the workforce, and the impact on specific groups and local communities could be disproportionately negative.

Social and environmental impact: The development and training of foundation models may lead to detrimental social and environmental consequences, including an increase in carbon emissions (for example, training one large language model can emit about 315 tons of carbon dioxide). 1 Ananya Ganesh, Andrew McCallum, and Emma Strubell, “Energy and policy considerations for deep learning in NLP,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , June 5, 2019.

There are economic challenges too: the scale and the scope of the workforce transitions described in this report are considerable. In the midpoint adoption scenario, about a quarter to a third of work activities could change in the coming decade. The task before us is to manage the potential positives and negatives of the technology simultaneously (see sidebar “Using generative AI responsibly”). Here are some of the critical questions we will need to address while balancing our enthusiasm for the potential benefits of the technology with the new challenges it can introduce.

Companies and business leaders

How can companies move quickly to capture the potential value at stake highlighted in this report, while managing the risks that generative AI presents?

How will the mix of occupations and skills needed across a company’s workforce be transformed by generative AI and other artificial intelligence over the coming years? How will a company enable these transitions in its hiring plans, retraining programs, and other aspects of human resources?

Do companies have a role to play in ensuring the technology is not deployed in “negative use cases” that could harm society?

How can businesses transparently share their experiences with scaling the use of generative AI within and across industries—and also with governments and society?

Policy makers

What will the future of work look like at the level of an economy in terms of occupations and skills? What does this mean for workforce planning?

How can workers be supported as their activities shift over time? What retraining programs can be put in place? What incentives are needed to support private companies as they invest in human capital? Are there earn-while-you-learn programs such as apprenticeships that could enable people to retrain while continuing to support themselves and their families?

What steps can policy makers take to prevent generative AI from being used in ways that harm society or vulnerable populations?

Can new policies be developed and existing policies amended to ensure human-centric AI development and deployment that includes human oversight and diverse perspectives and accounts for societal values?

Individuals as workers, consumers, and citizens

How concerned should individuals be about the advent of generative AI? While companies can assess how the technology will affect their bottom lines, where can citizens turn for accurate, unbiased information about how it will affect their lives and livelihoods?

How can individuals as workers and consumers balance the conveniences generative AI delivers with its impact in their workplaces?

Can citizens have a voice in the decisions that will shape the deployment and integration of generative AI into the fabric of their lives?

Technological innovation can inspire equal parts awe and concern. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it.

All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. It is important to properly understand this phenomenon and anticipate its impact. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great.

These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. They are capable of that most human of abilities, language, which is a fundamental requirement of most work activities linked to expertise and knowledge as well as a skill that can be used to hurt feelings, create misunderstandings, obscure truth, and incite violence and even wars.

We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. The time to act is now. 11 The research, analysis, and writing in this report was entirely done by humans.

Michael Chui is a partner in McKinsey’s Bay Area office, where Roger Roberts is a partner and Lareina Yee is a senior partner; Eric Hazan is a senior partner in McKinsey’s Paris office; Alex Singla is a senior partner in the Chicago office; Kate Smaje and Alex Sukharevsky are senior partners in the London office; and Rodney Zemmel is a senior partner in the New York office.

The authors wish to thank Pedro Abreu, Rohit Agarwal, Steven Aronowitz, Arun Arora, Charles Atkins, Elia Berteletti, Onno Boer, Albert Bollard, Xavier Bosquet, Benjamin Braverman, Charles Carcenac, Sebastien Chaigne, Peter Crispeels, Santiago Comella-Dorda, Eleonore Depardon, Kweilin Ellingrud, Thierry Ethevenin, Dmitry Gafarov, Neel Gandhi, Eric Goldberg, Liz Grennan, Shivani Gupta, Vinay Gupta, Dan Hababou, Bryan Hancock, Lisa Harkness, Leila Harouchi, Jake Hart, Heiko Heimes, Jeff Jacobs, Begum Karaci Deniz, Tarun Khurana, Malgorzata Kmicinska, Jan-Christoph Köstring, Andreas Kremer, Kathryn Kuhn, Jessica Lamb, Maxim Lampe, John Larson, Swan Leroi, Damian Lewandowski, Richard Li, Sonja Lindberg, Kerin Lo, Guillaume Lurenbaum, Matej Macak, Dana Maor, Julien Mauhourat, Marco Piccitto, Carolyn Pierce, Olivier Plantefeve, Alexandre Pons, Kathryn Rathje, Emily Reasor, Werner Rehm, Steve Reis, Kelsey Robinson, Martin Rosendahl, Christoph Sandler, Saurab Sanghvi, Boudhayan Sen, Joanna Si, Alok Singh, Gurneet Singh Dandona, François Soubien, Eli Stein, Stephanie Strom, Michele Tam, Robert Tas, Maribel Tejada, Wilbur Wang, Georg Winkler, Jane Wong, and Romain Zilahi for their contributions to this report.

For the full list of acknowledgments, see the downloadable PDF .

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