writing conclusions in essays

How to Write a Conclusion for an Essay - Tips and Examples

The conclusion of your essay is like the grand finale of a fireworks display. It's the last impression you leave on your reader, the moment that ties everything together and leaves them with a lasting impact. 

But for many writers, crafting a conclusion can feel like an afterthought, a hurdle to jump after the excitement of developing the main body of their work. Fear not! This article will equip you with the tools and techniques regarding how to write a conclusion for an essay that effectively summarizes your main points, strengthens your argument, and leaves your reader feeling satisfied and engaged.

What Is a Conclusion

In an essay, the conclusion acts as your final curtain call. It's where you revisit your initial claim (thesis), condense your main supporting arguments, and leave the reader with a lasting takeaway. 

Imagine it as the bridge that connects your ideas to a broader significance. A well-crafted conclusion does more than simply summarize; it elevates your points and offers a sense of closure, ensuring the reader leaves with a clear understanding of your argument's impact. In the next section, you will find conclusion ideas that you could use for your essay.

Please note that our online paper writing service can provide you not only with a stand-alone conclusion but with a fully new composition as well!

Types of Conclusion

Here's a breakdown of various conclusion types, each serving a distinct purpose:

How Long Should a Conclusion Be

The ideal length of a conclusion depends on the overall length of your essay, but there are some general guidelines:

• Shorter Essays (500-750 words): Aim for 3-5 sentences. This ensures you effectively wrap up your points without adding unnecessary content.
• Medium Essays (750-1200 words): Here, you can expand to 5-8 sentences. This provides more space to elaborate on your concluding thought or call to action.
• Longer Essays (1200+ words): For these, you can have a conclusion of 8-10 sentences. This allows for a more comprehensive summary or a more nuanced exploration of the future implications or broader significance of your topic.

Here are some additional factors to consider:

• The complexity of your argument: If your essay explores a multifaceted topic, your conclusion might need to be slightly longer to address all the points adequately.
• Type of conclusion: A call to action or a hypothetical scenario might require a few extra sentences for elaboration compared to a simple summary.

Remember: The most important aspect is ensuring your conclusion effectively summarizes your main points, leaves a lasting impression, and doesn't feel rushed or tacked on.

Here's a helpful rule of thumb:

• Keep it proportional: Your conclusion should be roughly 5-10% of your total essay length.

How many sentences should a conclusion be?

Conclusion Transition Words

Transition words for conclusion act like signposts for your reader. They smoothly guide them from the main body of your essay to your closing thoughts, ensuring a clear and logical flow of ideas. Here are some transition words specifically suited for concluding your essay:

Remember, the best transition word will depend on the specific type of conclusion you're aiming for.

How to Write a Conclusion

Every essay or dissertation writer knows that the toughest part of working on a conclusion can be striking the right balance. You want to effectively summarize your main points without redundancy, leaving a lasting impression that feels fresh and impactful, all within a concise and focused section. Here’s a step-by-step guide to help you write a stunning essay conclusion:

Restate Your Thesis

Briefly remind your reader of your essay's central claim. This doesn't have to be a word-for-word repetition but a concise restatement that refreshes their memory.

Summarize Key Points

In a few sentences, revisit the main arguments you used to support your thesis. When writing a conclusion, don't get bogged down in details, but offer a high-level overview that reinforces your essay's focus.

Leave a Lasting Impression

This is where your knowledge of how to write a good conclusion can shine! Consider a thought-provoking question, a call to action, or a connection to a broader truth—something that lingers in the reader's mind and resonates beyond the final sentence.

Avoid Introducing New Information

The conclusion paragraph shouldn't introduce entirely new ideas. Stick to wrapping up your existing arguments and leaving a final thought.

Ensure Flow and Readability

Transition smoothly from the main body of your essay to the conclusion. Use transition words like "in conclusion," "finally," or "as a result," and ensure your closing sentences feel natural and well-connected to the rest of your work.

Note that you can simply buy essay at any time and focus on other more important assignments or just enjoy your free time.

Conclusion Paragraph Outline

Here's an outline to help you better understand how to write a conclusion paragraph:

• Tailor the length of your conclusion to your essay's overall length (shorter essays: 3-5 sentences, longer essays: 8-10 sentences).
• Ensure a smooth transition from the main body using transition words.
• Avoid introducing new information; focus on wrapping up your existing points.
• Proofread for clarity and ensure your conclusion ties everything together and delivers a final impactful statement.

Read more: Persuasive essay outline . 

Do’s and Don’ts of Essay Conclusion Writing

According to professional term paper writers , a strong conclusion is essential for leaving a lasting impression on your reader. Here's a list of action items you should and shouldn’t do when writing an essay conclusion:

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Conclusion Examples

A strong conclusion isn't just an afterthought – it's the capstone of your essay. Here are five examples of conclusion paragraphs for essays showcasing different techniques to craft a powerful closing to make your essay stand out.

1. Call to Action: (Essay About the Importance of Recycling)

In conclusion, the environmental impact of our waste is undeniable. We all have a responsibility to adopt sustainable practices. We can collectively make a significant difference by incorporating simple changes like recycling into our daily routines. Join the movement – choose to reuse, reduce, and recycle.

2. Provocative Question: (Essay Exploring the Potential Consequences of Artificial Intelligence)

As artificial intelligence rapidly evolves, it's crucial to consider its impact on humanity. While AI holds immense potential for progress, will it remain a tool for good, or will it eventually surpass human control? This question demands our collective attention, as the decisions we make today will shape the future of AI and its impact on our world.

3. Universal Insight: (Essay Analyzing a Historical Event)

The study of history offers valuable lessons that transcend time. The events of the [insert historical event] remind us that even small acts of defiance can have a ripple effect, inspiring change and ultimately leading to a brighter future. Every voice has the power to make a difference, and courage can be contagious.

4. Future Implications: (Essay Discussing the Rise of Social Media)

Social media's explosive growth has transformed how we connect and consume information. While these platforms offer undeniable benefits, their long-term effects on social interaction, mental health, and political discourse require careful consideration. As social media continues to evolve, we must remain vigilant and ensure it remains a tool for positive connection and not a source of division.

5. Hypothetical Scenario: (Essay Arguing for the Importance of Space Exploration)

Imagine a world where our understanding of the universe is limited to Earth. We miss out on the potential for groundbreaking discoveries in physics, medicine, and our place in the cosmos. By continuing to venture beyond our planet, we push the boundaries of human knowledge and inspire future generations to reach for the stars.

Recommended for reading: Nursing essay examples .

Difference Between Good and Weak Conclusions

Not all conclusions are created equal. A weak ending can leave your reader feeling stranded, unsure of where your essay has taken them. Conversely, writing a conclusion that is strong acts as a landing pad, summarizing your key points and leaving a lasting impression.

Nailed that essay? Don't blow it with a lame ending! A good conclusion is like the mic drop at the end of a rap song. It reminds the reader of your main points but in a cool new way. Throw in a thought-provoking question, a call to action, or a connection to something bigger, and you'll leave them thinking long after they turn the page.

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How To Write A Conclusion For An Essay?

How to write a good conclusion, how to write a conclusion for a college essay.

Daniel Parker

is a seasoned educational writer focusing on scholarship guidance, research papers, and various forms of academic essays including reflective and narrative essays. His expertise also extends to detailed case studies. A scholar with a background in English Literature and Education, Daniel’s work on EssayPro blog aims to support students in achieving academic excellence and securing scholarships. His hobbies include reading classic literature and participating in academic forums.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

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• Essay Conclusions | UMGC. (n.d.). University of Maryland Global Campus. https://www.umgc.edu/current-students/learning-resources/writing-center/writing-resources/writing/essay-conclusions
• How to Write a Conclusion for an Essay | BestColleges. (n.d.). BestColleges.com. https://www.bestcolleges.com/blog/how-to-write-a-conclusion/
• Ending the Essay: Conclusions | Harvard College Writing Center. (n.d.). https://writingcenter.fas.harvard.edu/pages/ending-essay-conclusions

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How to Write an Essay Conclusion

4-minute read

• 1st October 2022

Regardless of what you’re studying, writing essays is probably a significant part of your work as a student . Taking the time to understand how to write each section of an essay (i.e., introduction, body, and conclusion) can make the entire process easier and ensure that you’ll be successful.

Once you’ve put in the hard work of writing a coherent and compelling essay, it can be tempting to quickly throw together a conclusion without the same attention to detail. However, you won’t leave an impactful final impression on your readers without a strong conclusion.

We’ve compiled a few easy steps to help you write a great conclusion for your next essay . Watch our video, or check out our guide below to learn more!

1. Return to Your Thesis

Similar to how an introduction should capture your reader’s interest and present your argument, a conclusion should show why your argument matters and leave the reader with further curiosity about the topic.

To do this, you should begin by reminding the reader of your thesis statement. While you can use similar language and keywords when referring to your thesis, avoid copying it from the introduction and pasting it into your conclusion.

Try varying your vocabulary and sentence structure and presenting your thesis in a way that demonstrates how your argument has evolved throughout your essay.

2. Review Your Main Points

In addition to revisiting your thesis statement, you should review the main points you presented in your essay to support your argument.

However, a conclusion isn’t simply a summary of your essay . Rather, you should further examine your main points and demonstrate how each is connected.

Try to discuss these points concisely, in just a few sentences, in preparation for demonstrating how they fit in to the bigger picture of the topic.

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3. Show the Significance of Your Essay

Next, it’s time to think about the topic of your essay beyond the scope of your argument. It’s helpful to keep the question “so what?” in mind when you’re doing this. The goal is to demonstrate why your argument matters.

If you need some ideas about what to discuss to show the significance of your essay, consider the following:

• What do your findings contribute to the current understanding of the topic?
• Did your findings raise new questions that would benefit from future research?
• Can you offer practical suggestions for future research or make predictions about the future of the field/topic?
• Are there other contexts, topics, or a broader debate that your ideas can be applied to?

While writing your essay, it can be helpful to keep a list of ideas or insights that you develop about the implications of your work so that you can refer back to it when you write the conclusion.

Making these kinds of connections will leave a memorable impression on the reader and inspire their interest in the topic you’ve written about.

4. Avoid Some Common Mistakes

To ensure you’ve written a strong conclusion that doesn’t leave your reader confused or lacking confidence in your work, avoid:

• Presenting new evidence: Don’t introduce new information or a new argument, as it can distract from your main topic, confuse your reader, and suggest that your essay isn’t organized.
• Undermining your argument: Don’t use statements such as “I’m not an expert,” “I feel,” or “I think,” as lacking confidence in your work will weaken your argument.
• Using generic statements: Don’t use generic concluding statements such as “In summary,” “To sum up,” or “In conclusion,” which are redundant since the reader will be able to see that they’ve reached the end of your essay.

Finally, don’t make the mistake of forgetting to proofread your essay ! Mistakes can be difficult to catch in your own writing, but they can detract from your writing.

Our expert editors can ensure that your essay is clear, concise, and free of spelling and grammar errors. Find out more by submitting a free trial document today!

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How to Conclude an Essay (with Examples)

Last Updated: May 24, 2024 Fact Checked

Writing a Strong Conclusion

What to avoid, brainstorming tricks.

This article was co-authored by Jake Adams and by wikiHow staff writer, Aly Rusciano . Jake Adams is an academic tutor and the owner of Simplifi EDU, a Santa Monica, California based online tutoring business offering learning resources and online tutors for academic subjects K-College, SAT & ACT prep, and college admissions applications. With over 14 years of professional tutoring experience, Jake is dedicated to providing his clients the very best online tutoring experience and access to a network of excellent undergraduate and graduate-level tutors from top colleges all over the nation. Jake holds a BS in International Business and Marketing from Pepperdine University. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 3,211,370 times.

So, you’ve written an outstanding essay and couldn’t be more proud. But now you have to write the final paragraph. The conclusion simply summarizes what you’ve already written, right? Well, not exactly. Your essay’s conclusion should be a bit more finessed than that. Luckily, you’ve come to the perfect place to learn how to write a conclusion. We’ve put together this guide to fill you in on everything you should and shouldn’t do when ending an essay. Follow our advice, and you’ll have a stellar conclusion worthy of an A+ in no time.

Tips for Ending an Essay

• Rephrase your thesis to include in your final paragraph to bring the essay full circle.
• End your essay with a call to action, warning, or image to make your argument meaningful.
• Keep your conclusion concise and to the point, so you don’t lose a reader’s attention.
• Do your best to avoid adding new information to your conclusion and only emphasize points you’ve already made in your essay.

• “All in all”
• “Ultimately”
• “Furthermore”
• “As a consequence”
• “As a result”

• Make sure to write your main points in a new and unique way to avoid repetition.

• Let’s say this is your original thesis statement: “Allowing students to visit the library during lunch improves campus life and supports academic achievement.”
• Restating your thesis for your conclusion could look like this: “Evidence shows students who have access to their school’s library during lunch check out more books and are more likely to complete their homework.”
• The restated thesis has the same sentiment as the original while also summarizing other points of the essay.

• “When you use plastic water bottles, you pollute the ocean. Switch to using a glass or metal water bottle instead. The planet and sea turtles will thank you.”
• “The average person spends roughly 7 hours on their phone a day, so there’s no wonder cybersickness is plaguing all generations.”
• “Imagine walking on the beach, except the soft sand is made up of cigarette butts. They burn your feet but keep washing in with the tide. If we don’t clean up the ocean, this will be our reality.”
• “ Lost is not only a show that changed the course of television, but it’s also a reflection of humanity as a whole.”
• “If action isn’t taken to end climate change today, the global temperature will dangerously rise from 4.5 to 8 °F (−15.3 to −13.3 °C) by 2100.”

• Focus on your essay's most prevalent or important parts. What key points do you want readers to take away or remember about your essay?

• For instance, instead of writing, “That’s why I think that Abraham Lincoln was the best American President,” write, “That’s why Abraham Lincoln was the best American President.”
• There’s no room for ifs, ands, or buts—your opinion matters and doesn’t need to be apologized for!

• For instance, words like “firstly,” “secondly,” and “thirdly” may be great transition statements for body paragraphs but are unnecessary in a conclusion.

• For instance, say you began your essay with the idea that humanity’s small sense of sense stems from space’s vast size. Try returning to this idea in the conclusion by emphasizing that as human knowledge grows, space becomes smaller.

• For example, you could extend an essay on the television show Orange is the New Black by bringing up the culture of imprisonment in America.

Community Q&A

• Always review your essay after writing it for proper grammar, spelling, and punctuation, and don’t be afraid to revise. Thanks Helpful 0 Not Helpful 0

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• Have somebody else proofread your essay before turning it in. The other person will often be able to see errors you may have missed!

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• ↑ https://www.uts.edu.au/current-students/support/helps/self-help-resources/grammar/transition-signals
• ↑ https://owl.purdue.edu/owl/general_writing/common_writing_assignments/argument_papers/conclusions.html
• ↑ http://writing2.richmond.edu/writing/wweb/conclude.html
• ↑ https://writingcenter.fas.harvard.edu/pages/ending-essay-conclusions
• ↑ https://www.pittsfordschools.org/site/handlers/filedownload.ashx?moduleinstanceid=542&dataid=4677&FileName=conclusions1.pdf
• ↑ https://www.cuyamaca.edu/student-support/tutoring-center/files/student-resources/how-to-write-a-good-conclusion.pdf
• ↑ https://library.sacredheart.edu/c.php?g=29803&p=185935

About This Article

To end an essay, start your conclusion with a phrase that makes it clear your essay is coming to a close, like "In summary," or "All things considered." Then, use a few sentences to briefly summarize the main points of your essay by rephrasing the topic sentences of your body paragraphs. Finally, end your conclusion with a call to action that encourages your readers to do something or learn more about your topic. In general, try to keep your conclusion between 5 and 7 sentences long. For more tips from our English co-author, like how to avoid common pitfalls when writing an essay conclusion, scroll down! Did this summary help you? Yes No

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Need editing and proofreading services, how to write a conclusion for an essay (examples included).

• Tags: Essay , Essay Writing

Condensing a 1,000-plus-word essay into a neat little bundle may seem like a Herculean task. You must summarize all your findings and justify their importance within a single paragraph. 

But, when you discover the formula for writing a conclusion paragraph, things get much simpler! 

But, how to write a conclusion paragraph for an essay, and more importantly, how to make it impactful enough? Through this article, we will walk you through the process of constructing a powerful conclusion that leaves a lingering impression on readers’ minds. We will also acquaint you with essay conclusion examples for different types of essays. 

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Let’s start from the beginning: How can you write a conclusion for an essay?

How to write a conclusion for an essay

In order to write an effective conclusion, you must first understand what is a conclusion in an essay. It is not just the summary of the main points of your essay. A well-written conclusion effectively ties together the main ideas of your essay and also pays heed to their broader implications. The objectives of your concluding paragraph are as follows:

• Highlight the significance of your essay topic
• Tie together the key points of your essay
• Leave the reader with something to ponder about

A good essay conclusion begins with a modified thesis statement that is altered on the basis of the information stated throughout the essay. It then ties together all the main points of the essay and ends with a clincher that highlights the broader implications of your thesis statement. 

Now that we’ve understood the basics of how to conclude an essay, let’s understand the key aspects of a good conclusion paragraph. 

1. Restating your thesis statement

If you want to understand how to start a conclusion, you must realize that involves more than just restating the thesis statement word for word. Your thesis statement needs to be updated and expanded upon as per the information provided in your essay. 

There are many ways to start a conclusion. One such method could be to start with the revised version of your thesis statement that hints to the significance of your argument. After this, your conclusion paragraph can organically move on to your arguments in the essay. 

Let’s take a look at an effective way of writing a conclusion for an essay:

If the following claim is your thesis statement:

Virtual reality (VR) is undeniably altering the perception of reality by revolutionizing various industries, reshaping human experiences, and challenging traditional notions of what is real.

The restated thesis statement will be as follows: 

Our analysis has substantiated the claim that virtual reality (VR) is significantly transforming the way we perceive reality. It has revolutionized industries, reshaped human experiences, and challenged traditional notions of reality.

2. Tying together the main points

Tying together all the main points of your essay does not mean simply summarizing them in an arbitrary manner. The key is to link each of your main essay points in a coherent structure. One point should follow the other in a logical format.

The goal is to establish how each of these points connects to the message of your essay as a whole. You can also take the help of powerful quotes or impactful reviews to shed a unique light on your essay. 

Let’s take a look at an example:

VR presents a new paradigm where the distinction between the real and the virtual becomes increasingly blurred. As users dive into immersive virtual worlds, they are confronted with questions about the nature of reality, perception, and the boundaries of human consciousness. 

3. Constructing an impactful conclusion

Most of us are confused about how to end an essay with a bang. The answer is quite simple! The final line of your essay should be impactful enough to create a lasting impression on the reader. More importantly, it should also highlight the significance of your essay topic. This could mean the broader implications of your topic, either in your field of study or in general.

Optionally, you could also try to end your essay on an optimistic note that motivates or encourages the reader. If your essay is about eradicating a problem in society, highlight the positive effects achieved by the eradication of that problem. 

Here’s an example of how to end an essay:

In a world where virtual boundaries dissolve, VR is the catalyst that reshapes our perception of reality, forever altering the landscape of the human experience.

Here’s a combined version of all three aspects:

Our analysis has substantiated the claim that Virtual Reality (VR) is significantly transforming how we perceive reality. It has revolutionized industries, reshaped human experiences, and challenged traditional notions of reality. It presents a new paradigm where the distinction between the real and the virtual becomes increasingly blurred. As users dive into immersive virtual worlds, they are confronted with questions about the nature of reality, perception, and the boundaries of human consciousness. In a world where virtual boundaries dissolve, it is the catalyst that reshapes our perception of reality, forever altering the landscape of the human experience.

Now that we’ve understood the structure of a concluding paragraph, let’s look at what to avoid while writing a conclusion. 

What to avoid in your conclusion paragraph

When learning how to write a conclusion for an essay, you must also know what to avoid. You want to strengthen your argument with the help of a compelling conclusion paragraph, and not undermine it by confusing the reader. 

Let’s take a look at a few strategies to avoid in your essay conclusion:

1. Avoid including new evidence

The conclusion should not introduce new information but rather strengthen the arguments that are already made. If you come across any unique piece of information regarding your essay topic, accommodate it into your body paragraphs rather than stuffing it into your conclusion.

Including new, contradictory information in the concluding paragraph not only confuses the reader but also weakens your argument. You may include a powerful quote that strengthens the message of your essay, or an example that sheds light on the importance of your argument. However, this does not include introducing a completely new argument or making a unique point.

2. Avoid the use of concluding phrases

Your conclusion should hint towards your essay coming to an end, instead of blatantly stating the obvious. Blatant concluding statements undermine the quality of your essay, making it clumsy and amateurish. They also significantly diminish the quality of your arguments. 

It is a good idea to avoid the following statements while concluding your essay:

• In conclusion,
• In summary,

While using these statements may not be incorrect per se, hinting towards a conclusion creates a better impression on the reader rather than blatantly stating it. 

Here are more effective statements you could use:

• Let this essay serve as a catalyst for…
• As we navigate the intricacies of this multifaceted topic, remember…
• As I bid farewell to this subject…

3. Don’t undermine your argument

Although there might be several points of view regarding your essay topic, it is crucial that you stick to your own. You may have stated and refuted other points of view in your body paragraphs. 

However, your conclusion is simply meant to strengthen your main argument. Mentioning other points of view in your essay conclusion, not only weakens your argument but also creates a poor impression of your essay.

Here are a few phrases you should avoid in your essay conclusion:

• There are several methods to approach this topic.
• There are plenty of good points for both sides of the argument.
• There is no clear solution to this problem.

Examples of essay conclusions

Different types of essays make use of different forms of conclusions. The critical question of “how to start a conclusion paragraph” has many different answers. To help you further, we’ve provided a few good conclusions for essays that are based on the four main essay types.

1. Narrative essay conclusion

The following essay conclusion example elaborates on the narrator’s unique experience with homeschooling.

• Restated thesis statement
• Body paragraph summary
• Closing statement

My experience with homeschooling has been a journey that has shaped me in profound ways. Through the challenges and triumphs, I have come to appreciate the unique advantages and personal growth that homeschooling can offer. As I reflect on my journey, I am reminded of the transformative power of this alternative education approach. It has empowered me to take ownership of my education, nurture my passions, and develop skills that extend far beyond the confines of academic achievement. Whether in traditional classrooms or homeschooling environments, it is through embracing and nurturing the unique potential within each of us that we can truly thrive and make a lasting impact on the world.

2. Descriptive essay conclusion

The following essay conclusion example elaborates on the narrator’s bond with their cat.

The enchanting presence that my cat has cannot be ignored, captivating my heart with her grace, charm, and unconditional love. Through the moments of playfulness, companionship, and affection, she has become an irreplaceable member of my family. As I continue to cherish the memories and lessons learned from her, I am reminded of the extraordinary power of the human-animal bond. In their company, we find solace, companionship, and a love that transcends words. In a world that can be challenging and tumultuous, never underestimate the profound impact that animals can have on our lives. In their presence, not only do we find love but also a profound sense of connection.

3. Argumentative essay conclusion

Here’s an essay conclusion example that elaborates on the marginalization of, and acute intolerance towards, LGBTQ+ individuals. 

The journey toward equality for LGBTQ+ individuals is an ongoing battle that demands our unwavering commitment to justice and inclusion. It is evident that while progress has been made, the journey toward equality for these individuals is far from complete. It demands our continued advocacy, activism, and support for legislative change, societal acceptance, and the creation of inclusive environments. The struggle for LGBTQ+ equality is a fight for the very essence of human dignity and the recognition of our shared humanity. It is a battle that requires our collective efforts, determination, and an unyielding belief in the fundamental principles of equality and justice.

4. Expository essay conclusion

This example of an essay conclusion revolves around a psychological phenomenon named the bandwagon effect and examines its potential ill effects on society:

The bandwagon effect in psychology is a fascinating phenomenon that sheds light on the powerful influence of social conformity on individual behavior and decision-making processes. This effect serves as a reminder of the inherently social nature of human beings and the power of social influence in shaping our thoughts, attitudes, and actions. It underscores the importance of critical thinking, individual autonomy, and the ability to resist the pressure of conformity. By understanding its mechanisms and implications, we can guard against its potential pitfalls and actively foster independent thought and decision-making, also contributing to a more enlightened and progressive society.

Now that you’ve taken a closer look at different conclusions for essays, it’s time to put this knowledge to good use. If you need to take your essay up a notch and score high, professional essay editing services are your best bet.

Happy writing!

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Essay writing: Conclusions

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“Pay adequate attention to the conclusion.” Kathleen McMillan & Jonathan Weyers,  How to Write Essays & Assignments

Conclusions are often overlooked, cursory and written last minute. If this sounds familiar then it's time to change and give your conclusions some much needed attention. Your conclusion is the whole point of your essay. All the other parts of the essay should have been leading your reader on an inevitable journey towards your conclusion. So make it count and finish your essay in style.

Know where you are going

Too many students focus their essays on content rather than argument. This means they pay too much attention to the main body without considering where it is leading. It can be a good idea to write a draft conclusion before  you write your main body. It is a lot easier to plan a journey when you know your destination! 

It should only be a draft however, as quite often the writing process itself can help you develop your argument and you may feel your conclusion needs adapting accordingly.

What it should include

A great conclusion should include:

A clear link back to the question . This is usually the first thing you do in a conclusion and it shows that you have (hopefully) answered it.

A sentence or two that summarise(s) your main argument but in a bit more detail than you gave in your introduction.

A series of supporting sentences that basically reiterate the main point of each of your paragraphs but show how they relate to each other and lead you to the position you have taken. Constantly ask yourself "So what?" "Why should anyone care?" and answer these questions for each of the points you make in your conclusion.

A final sentence that states why your ideas are important to the wider subject area . Where the introduction goes from general to specific, the conclusion needs to go from specific back out to general.

What it should not  include

Try to avoid including the following in your conclusion. Remember your conclusion should be entirely predictable. The reader wants no surprises.

Any new ideas . If an idea is worth including, put it in the main body. You do not need to include citations in your conclusion if you have already used them earlier and are just reiterating your point.

A change of style i.e. being more emotional or sentimental than the rest of the essay. Keep it straightforward, explanatory and clear.

Overused phrases like: “in conclusion”; “in summary”; “as shown in this essay”. Consign these to the rubbish bin!

Here are some alternatives, there are many more:

• The x main points presented here emphasise the importance of...
• The [insert something relevant] outlined above indicate that ...
• By showing the connections between x, y and z, it has been argued here that ...

Maximise marks

Remember, your conclusion is the last thing your reader (marker!) will read. Spending a little care on it will leave her/him absolutely sure that you have answered the question and you will definitely receive a higher mark than if your conclusion was a quickly written afterthought.

Your conclusion should be around 10% of your word count. There is never a situation where sacrificing words in your conclusion will benefit your essay.

The 5Cs conclusion method: (spot the typo on this video)

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Writing a Paper: Conclusions

Writing a conclusion.

A conclusion is an important part of the paper; it provides closure for the reader while reminding the reader of the contents and importance of the paper. It accomplishes this by stepping back from the specifics in order to view the bigger picture of the document. In other words, it is reminding the reader of the main argument. For most course papers, it is usually one paragraph that simply and succinctly restates the main ideas and arguments, pulling everything together to help clarify the thesis of the paper. A conclusion does not introduce new ideas; instead, it should clarify the intent and importance of the paper. It can also suggest possible future research on the topic.

An Easy Checklist for Writing a Conclusion

It is important to remind the reader of the thesis of the paper so he is reminded of the argument and solutions you proposed.

Think of the main points as puzzle pieces, and the conclusion is where they all fit together to create a bigger picture. The reader should walk away with the bigger picture in mind.

Make sure that the paper places its findings in the context of real social change.

Make sure the reader has a distinct sense that the paper has come to an end. It is important to not leave the reader hanging. (You don’t want her to have flip-the-page syndrome, where the reader turns the page, expecting the paper to continue. The paper should naturally come to an end.)

No new ideas should be introduced in the conclusion. It is simply a review of the material that is already present in the paper. The only new idea would be the suggesting of a direction for future research.

Conclusion Example

As addressed in my analysis of recent research, the advantages of a later starting time for high school students significantly outweigh the disadvantages. A later starting time would allow teens more time to sleep--something that is important for their physical and mental health--and ultimately improve their academic performance and behavior. The added transportation costs that result from this change can be absorbed through energy savings. The beneficial effects on the students’ academic performance and behavior validate this decision, but its effect on student motivation is still unknown. I would encourage an in-depth look at the reactions of students to such a change. This sort of study would help determine the actual effects of a later start time on the time management and sleep habits of students.

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Conclusion How to end an essay

While getting started can be very difficult, finishing an essay is usually quite straightforward. By the time you reach the end you will already know what the main points of the essay are, so it will be easy for you to write a summary of the essay and finish with some kind of final comment , which are the two components of a good conclusion. An example essay has been given below to help you understand both of these, and there is a checklist at the end which you can use for editing your conclusion.

In short, the concluding paragraph consists of the following two parts:

• a summary of the main points;
• your final comment on the subject.

It is important, at the end of the essay, to summarise the main points. If your thesis statement is detailed enough, then your summary can just be a restatement of your thesis using different words. The summary should include all the main points of the essay, and should begin with a suitable transition signal . You should not add any new information at this point.

The following is an example of a summary for a short essay on cars ( given below ):

In conclusion, while the car is advantageous for its convenience, it has some important disadvantages, in particular the pollution it causes and the rise of traffic jams.

Although this summary is only one sentence long, it contains the main (controlling) ideas from all three paragraphs in the main body. It also has a clear transition signal ('In conclusion') to show that this is the end of the essay.

Final comment

Once the essay is finished and the writer has given a summary, there should be some kind of final comment about the topic. This should be related to the ideas in the main body . Your final comment might:

• offer solutions to any problems mentioned in the body;
• offer recommendations for future action;
• give suggestions for future research.

Here is an example of a final comment for the essay on cars :

If countries can invest in the development of technology for green fuels, and if car owners can think of alternatives such as car sharing, then some of these problems can be lessened.

This final comment offers solutions, and is related to the ideas in the main body. One of the disadvantages in the body was pollution, so the writer suggests developing 'green fuels' to help tackle this problem. The second disadvantage was traffic congestion, and the writer again suggests a solution, 'car sharing'. By giving these suggestions related to the ideas in the main body, the writer has brought the essay to a successful close.

Example essay

Below is a discussion essay which looks at the advantages and disadvantages of car ownership. This essay is used throughout the essay writing section to help you understand different aspects of essay writing. Here it focuses on the summary and final comment of the conclusion (mentioned on this page), the thesis statement and general statements of the introduction, and topic sentences and controlling ideas. Click on the different areas (in the shaded boxes to the right) to highlight the different structural aspects in this essay.

Although they were invented almost a hundred years ago, for decades cars were only owned by the rich. Since the 60s and 70s they have become increasingly affordable, and now most families in developed nations, and a growing number in developing countries, own a car. While cars have undoubted advantages, of which their convenience is the most apparent, they have significant drawbacks, most notably pollution and traffic problems . The most striking advantage of the car is its convenience. When travelling long distance, there may be only one choice of bus or train per day, which may be at an unsuitable time. The car, however, allows people to travel at any time they wish, and to almost any destination they choose. Despite this advantage, cars have many significant disadvantages, the most important of which is the pollution they cause. Almost all cars run either on petrol or diesel fuel, both of which are fossil fuels. Burning these fuels causes the car to emit serious pollutants, such as carbon dioxide, carbon monoxide, and nitrous oxide. Not only are these gases harmful for health, causing respiratory disease and other illnesses, they also contribute to global warming, an increasing problem in the modern world. According to the Union of Concerned Scientists (2013), transportation in the US accounts for 30% of all carbon dioxide production in that country, with 60% of these emissions coming from cars and small trucks. In short, pollution is a major drawback of cars. A further disadvantage is the traffic problems that they cause in many cities and towns of the world. While car ownership is increasing in almost all countries of the world, especially in developing countries, the amount of available roadway in cities is not increasing at an equal pace. This can lead to traffic congestion, in particular during the morning and evening rush hour. In some cities, this congestion can be severe, and delays of several hours can be a common occurrence. Such congestion can also affect those people who travel out of cities at the weekend. Spending hours sitting in an idle car means that this form of transport can in fact be less convenient than trains or aeroplanes or other forms of public transport. In conclusion, while the car is advantageous for its convenience , it has some important disadvantages, in particular the pollution it causes and the rise of traffic jams . If countries can invest in the development of technology for green fuels, and if car owners can think of alternatives such as car sharing, then some of these problems can be lessened.

Union of Concerned Scientists (2013). Car Emissions and Global Warming. www.ucsusa.org/clean vehicles/why-clean-cars/global-warming/ (Access date: 8 August, 2013)

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Below is a checklist for an essay conclusion. Use it to check your own writing, or get a peer (another student) to help you.

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Compare & contrast essays examine the similarities of two or more objects, and the differences.

Cause & effect essays consider the reasons (or causes) for something, then discuss the results (or effects).

Discussion essays require you to examine both sides of a situation and to conclude by saying which side you favour.

Problem-solution essays are a sub-type of SPSE essays (Situation, Problem, Solution, Evaluation).

Transition signals are useful in achieving good cohesion and coherence in your writing.

Reporting verbs are used to link your in-text citations to the information cited.

Table of Contents

Ai, ethics & human agency, collaboration, information literacy, writing process, conclusions – how to write compelling conclusions.

• © 2023 by Jennifer Janechek - IBM Quantum

Conclusions generally address these issues:

• How can you restate your ideas concisely and in a new way?
• What have you left your reader to think about at the end of your paper?
• How does your paper answer the “so what?” question?

As the last part of the paper, conclusions often get the short shrift. We instructors know (not that we condone it)—many students devote a lot less attention to the writing of the conclusion. Some students might even finish their conclusion thirty minutes before they have to turn in their papers. But even if you’re practicing desperation writing, don’t neglect your conclusion; it’s a very integral part of your paper.

Think about it: Why would you spend so much time writing your introductory material and your body paragraphs and then kill the paper by leaving your reader with a dud for a conclusion? Rather than simply trailing off at the end, it’s important to learn to construct a compelling conclusion—one that both reiterates your ideas and leaves your reader with something to think about.

How do I reiterate my main points?

In the first part of the conclusion, you should spend a brief amount of time summarizing what you’ve covered in your paper. This reiteration should not merely be a restatement of your thesis or a collection of your topic sentences but should be a condensed version of your argument, topic, and/or purpose.

Let’s take a look at an example reiteration from a paper about offshore drilling:

Ideally, a ban on all offshore drilling is the answer to the devastating and culminating environmental concerns that result when oil spills occur. Given the catastrophic history of three major oil spills, the environmental and economic consequences of offshore drilling should now be obvious.

Now, let’s return to the thesis statement in this paper so we can see if it differs from the conclusion:

As a nation, we should reevaluate all forms of offshore drilling, but deep water offshore oil drilling, specifically, should be banned until the technology to stop and clean up oil spills catches up with our drilling technology. Though some may argue that offshore drilling provides economic advantages and would lessen our dependence on foreign oil, the environmental and economic consequences of an oil spill are so drastic that they far outweigh the advantages.

The author has already discussed environmental/economic concerns with oil drilling. In the above example, the author provides an overview of the paper in the second sentence of the conclusion, recapping the main points and reminding the readers that they should now be willing to acknowledge this position as viable.

Though you may not always want to take this aggressive of an approach (i.e., saying something should be obvious to the reader), the key is to summarize your main ideas without “plagiarizing” by repeating yourself word for word. Instead, you may take the approach of saying, “The readers can now see, given the catastrophic history of three major oil spills, the environmental and economic consequences of oil drilling.”

Can you give me a real-life example of a conclusion?

Think of conclusions this way: You are watching a movie, which has just reached the critical plot point (the murderer will be revealed, the couple will finally kiss, the victim will be rescued, etc.), when someone else enters the room. This person has no idea what is happening in the movie. They might lean over to ask, “What’s going on?” You now have to condense the entire plot in a way that makes sense, so the person will not have to ask any other questions, but quickly, so that you don’t miss any more of the movie.

Your conclusion in a paper works in a similar way. When you write your conclusion, imagine that a person has just showed up in time to hear the last paragraph. What does that reader need to know in order to get the gist of your paper? You cannot go over the entire argument again because the rest of your readers have actually been present and listening the whole time. They don’t need to hear the details again. Writing a compelling conclusion usually relies on the balance between two needs: give enough detail to cover your point, but be brief enough to make it obvious that this is the end of the paper.

Remember that reiteration is not restatement. Summarize your paper in one to two sentences (or even three or four, depending on the length of the paper), and then move on to answering the “So what?” question.

How can I answer the “So what?” question?

The bulk of your conclusion should answer the “So what?” question. Have you ever had an instructor write “So what?” at the end of your paper? This is not meant to offend but rather to remind you to show readers the significance of your argument. Readers do not need or want an entire paragraph of summary, so you should craft some new tidbit of interesting information that serves as an extension of your original ideas.

There are a variety of ways that you can answer the “So what?” question. The following are just a few types of such “endnotes”:

The Call to Action

The call to action can be used at the end of a variety of papers, but it works best for persuasive papers. Persuasive papers include social action papers and Rogerian argument essays, which begin with a problem and move toward a solution that serves as the author’s thesis. Any time your purpose in writing is to change your readers’ minds or you want to get your readers to do something, the call to action is the way to go. The call to action asks your readers, after having progressed through a compelling and coherent argument, to do something or believe a certain way.

Following the reiteration of the essay’s argument, here is an example call to action:

We have advanced technology that allows deepwater offshore drilling, but we lack the similarly advanced technology that would manage these spills effectively. As such, until cleanup and prevention technology are available, we gatekeepers of our coastal shores and defenders of marine wildlife should ban offshore drilling, or, at the very least, demand a moratorium on all offshore oil drilling.

This call to action requests that the readers consider a ban on offshore drilling. Remember, you need to identify your audience before you begin writing. Whether the author wants readers to actually enact the ban or just to come to this side of the argument, the conclusion asks readers to do or believe something new based upon the information they just received.

The Contextualization

The contextualization places the author’s local argument, topic, or purpose in a more global context so that readers can see the larger purpose for the piece or where the piece fits into a larger conversation. Writers do research for papers in part so they can enter into specific conversations, and they provide their readers with a contextualization in the conclusion to acknowledge the broader dialogue that contains that smaller conversation.

For instance, if we were to return to the paper on offshore drilling, rather than proposing a ban (a call to action), we might provide the reader with a contextualization:

We have advanced technology that allows deepwater offshore drilling, but we lack the advanced technology that would manage these spills effectively. Thus, one can see the need to place environmental concerns at the forefront of the political arena. Many politicians have already done so, including Senator Doe and Congresswoman Smith.

Rather than asking readers to do or believe something, this conclusion answers the “So what?” question by showing why this specific conversation about offshore drilling matters in the larger conversation about politics and environmentalism.

The twist leaves readers with a contrasting idea to consider. For instance, to continue the offshore drilling paper, the author might provide readers with a twist in the last few lines of the conclusion:

While offshore drilling is certainly an important issue today, it is only a small part of the greater problem of environmental abuse. Until we are ready to address global issues, even a moratorium on offshore drilling will only delay the inevitable destruction of the environment.

While this contrasting idea does not negate the writer’s original argument, it does present an alternative contrasting idea to weigh against the original argument. The twist is similar to a cliffhanger, as it is intended to leave readers saying, “Hmm…”

Suggest Possibilities for Future Research

This approach to answering “So what?” is best for projects that might be developed into larger, ongoing projects later or to suggest possibilities for future research someone else who might be interested in that topic could explore. This approach involves pinpointing various directions which your research might take if someone were to extend the ideas included in your paper. Research is a conversation, so it’s important to consider how your piece fits into this conversation and how others might use it in their own conversations.

For example, to suggest possibilities for future research based on the paper on offshore drilling, the conclusion might end with something like this:

I have just explored the economic and environmental repercussions of offshore drilling based on the examples we have of three major oil spills over the past thirty years. Future research might uncover more economic and environmental consequences of offshore drilling, consequences that will become clearer as the effects of the BP oil spill become more pronounced and as more time passes.

Suggesting opportunities for future research involves the reader in the paper, just like the call to action. Readers may be inspired by your brilliant ideas to use your piece as a jumping-off point!

Whether you use a call to action, a twist, a contextualization, or a suggestion of future possibilities for research, it’s important to answer the “So what?” question to keep readers interested in your topic until the very end of the paper. And, perhaps more importantly, leaving your readers with something to consider makes it more likely that they will remember your piece of writing.

Revise your own argument by using the following questions to guide you:

• What do you want readers to take away from your discussion?
• What are the main points you made, why should readers care, and what ideas should they take away?

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Writing conclusions.

• GENERAL LANGUAGE FUNCTIONS
• Being cautious
• Being critical
• Classifying and listing
• Compare and contrast
• Defining terms
• Describing trends
• Describing quantities
• Explaining causality
• Giving examples
• Signalling transition
• Writing about the past

Writing conclusions

Conclusions are shorter sections of academic texts which usually serve two functions. The first is to summarise and bring together the main areas covered in the writing, which might be called ‘looking back’; and the second is to give a final comment or judgement on this. The final comment may also include making suggestions for improvement and speculating on future directions.

In dissertations and research papers, conclusions tend to be more complex and will also include sections on the significance of the findings and recommendations for future work. Conclusions may be optional in research articles where consolidation of the study and general implications are covered in the Discussion section. However, they are usually expected in dissertations and essays.

Restating the aims of the study

This study set out to … This paper has argued that … This essay has discussed the reasons for … In this investigation, the aim was to assess … The aim of the present research was to examine … The purpose of the current study was to determine … The main goal of the current study was to determine … This project was undertaken to design … and evaluate … The present study was designed to determine the effect of … The second aim of this study was to investigate the effects of …

Summarising main research findings

This study has identified … The research has also shown that … The second major finding was that … These experiments confirmed that … X made no significant difference to … This study has found that generally … The investigation of X has shown that … The results of this investigation show that … X, Y and Z emerged as reliable predictors of … The most obvious finding to emerge from this study is that … The relevance of X is clearly supported by the current findings. One of the more significant findings to emerge from this study is that …

Suggesting implications for the field of knowledge

The results of this study indicate that … These findings suggest that in general … The findings of this study suggest that … Taken together, these results suggest that … An implication of this is the possibility that … The evidence from this study suggests that … Overall, this study strengthens the idea that … The current data highlight the importance of … The findings of this research provide insights for …

The results of this research support the idea that … These data suggest that X can be achieved through … The theoretical implications of these findings are unclear. The principal theoretical implication of this study is that … This study has raised important questions about the nature of … Taken together, these findings suggest a role for X in promoting Y. The findings of this investigation complement those of earlier studies. These findings have significant implications for the understanding of how … Although this study focuses on X, the findings may well have a bearing on …

Explaining the significance of the findings or contribution of the study

The findings will be of interest to … This thesis has provided a deeper insight into … The findings reported here shed new light on … The study contributes to our understanding of … These results add to the rapidly expanding field of … The contribution of this study has been to confirm … Before this study, evidence of X was purely anecdotal. This project is the first comprehensive investigation of … The insights gained from this study may be of assistance to … This work contributes to existing knowledge of X by providing …

Prior to this study it was difficult to make predictions about how … The analysis of X undertaken here, has extended our knowledge of … The empirical findings in this study provide a new understanding of … This paper contributes to recent historiographical debates concerning … This approach will prove useful in expanding our understanding of how … This new understanding should help to improve predictions of the impact of … The methods used for this X may be applied to other Xs elsewhere in the world. The X that we have identified therefore assists in our understanding of the role of … This is the first study of substantial duration which examines associations between … The findings from this study make several contributions to the current literature. First,…

Recognising the limitations of the current study

A limitation of this study is that … Being limited to X, this study lacks … The small sample size did not allow … The major limitation of this study is the … This study was limited by the absence of … X makes these findings less generalisable to … Thirdly, the study did not evaluate the use of … It is unfortunate that the study did not include … The scope of this study was limited in terms of …

The study is limited by the lack of information on … The most important limitation lies in the fact that … The main weakness of this study was the paucity of … Since the study was limited to X, it was not possible to .. An additional uncontrolled factor is the possibility that … It was not possible to assess X; therefore, it is unknown if … An issue that was not addressed in this study was whether… The generalisability of these results is subject to certain limitations. For instance, … One source of weakness in this study which could have affected the measurements of X was …

Acknowledging limitation(s) whilst stating a finding or contribution

Notwithstanding these limitations, the study suggests that … Whilst this study did not confirm X, it did partially substantiate … Despite its exploratory nature, this study offers some insight into … In spite of its limitations, the study certainly adds to our understanding of the … Notwithstanding the relatively limited sample, this work offers valuable insights into … Although the current study is based on a small sample of participants, the findings suggest …

Making recommendations for further research work

Future studies should… The question raised by this study is … The study should be repeated using … This would be a fruitful area for further work. Several questions still remain to be answered. A natural progression of this work is to analyse … More research using controlled trials is needed to … More broadly, research is also needed to determine … A further study could assess the long-term effects of … What is now needed is a cross-national study involving …

Considerably more work will need to be done to determine … The precise mechanism of X in plants remains to be elucidated. These findings provide the following insights for future research: … Large randomised controlled trials could provide more definitive evidence. This research has thrown up many questions in need of further investigation. A greater focus on X could produce interesting findings that account more for … The issue of X is an intriguing one which could be usefully explored in further research. If the debate is to be moved forward, a better understanding of X needs to be developed. I suggest that before X is introduced, a study similar to this one should be carried out on … More information on X would help us to establish a greater degree of accuracy on this matter.

Setting out recommendations for practice or policy

Other types of X could include: a), b). … There is, therefore, a definite need for … Greater efforts are needed to ensure … Provision of X will enhance Y and reduce Z. Another important practical implication is that … Moreover, more X should be made available to … The challenge now is to fabricate Xs that contain … Unless governments adopt X, Y will not be attained. These findings suggest several courses of action for … A reasonable approach to tackle this issue could be to …

Continued efforts are needed to make X more accessible to … The findings of this study have a number of practical implications. There are a number of important changes which need to be made. Management to enhance bumble-bee populations might involve … This study suggests that X should be avoided by people who are prone to … A key policy priority should therefore be to plan for the long-term care of … This information can be used to develop targetted interventions aimed at … Taken together, these findings do not support strong recommendations to … Ensuring appropriate systems, services and support for X should be a priority for … The findings of this study have a number of important implications for future practice.

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How to Write a Good Conclusion (With Examples) 

• Smodin Editorial Team
• Published: May 31, 2024

Students often spend a great deal of time crafting essay introductions while leaving the conclusion as an afterthought. While the introduction is one of the most vital aspects of an essay, a good conclusion can have just as much of an impact on its effectiveness. Knowing how to write a good conclusion is crucial, as it encapsulates your main points and leaves a lasting impression on the reader.

A well-crafted conclusion should serve as the final pitch for your arguments. Your reader should walk away with a clear understanding of what they just read and how it applies to the core of your thesis. With the right approach, your conclusion can transform a good essay into a great one, making it both memorable and impactful.

This article will guide you through four simple steps of writing compelling conclusions. Each step is designed to help you reinforce your thesis and articulate your final thoughts in a way that will resonate with your teacher or professor. With a bit of practice, you can learn how to stick the landing and give every essay the finale it deserves.

What Is the Purpose of the Conclusion Paragraph?

Understanding the purpose of the conclusion paragraph is essential for effective essay writing. The conclusion paragraph should be more than just a summary of your essay. It should consolidate all your arguments and tie them back to your thesis.

Remember, all good writing inspires emotion. Whether to inspire, provoke, or engage is up to you, but the conclusion should always leave a lasting impression.

If in doubt, Smodin’s AI Chat tool can be handy for gauging the emotional impact of your conclusion.

By mastering the art of writing a powerful conclusion, you equip yourself with the tools to ensure your essays stand out. Whether it’s the first or last essay you’re writing for the class, it’s your chance to leave a definitive mark on your reader.

How to Write a Good Conclusion

This approach ensures your conclusion adds value and reinforces your arguments’ coherence. Here are three simple and effective practices to help you craft a solid conclusion.

Restating Your Thesis

Restating your thesis in the conclusion is a common practice in essay writing, and for good reason. It helps underscore how your understanding has deepened or shifted based on the evidence you provided.

Just understand that a restatement of your original thesis doesn’t mean a complete word-for-word repeat. You should rephrase your original thesis so that it elucidates the insights you touched on throughout the essay. Smodin’s AI Rewriter can help refine your restatement to ensure it is fresh and impactful.

Here are a few tips to effectively restate your thesis

• Show Complexity : If your essay added layers or nuances to the original statement, be sure to articulate that clearly.
• Integrate Key Findings : Incorporate the main findings of your essay to reinforce how they supported or refined your thesis.
• Keep It Fresh : Again, you want to avoid repeating the same things twice. Use different wording that reflects a nuanced perspective.

Finally, always ensure that the restated thesis connects seamlessly with the rest of your essay. Always try to showcase the coherence of your writing to provide the reader with a strong sense of closure.

Using AI tools like Smodin’s Outliner and Essay Writer can ensure your writing flows smoothly and is easy to follow.

Providing an Effective Synthesis

Providing an effective synthesis should enhance your original thesis. All good arguments should evolve and shift throughout the essay. Rather than simply summarizing these findings, you should integrate critical insights and evidence to demonstrate a deeper or more nuanced understanding.

Draw connections between the main points discussed and show how they collectively support your thesis. Also, reflect on the implications of these insights for the broader context of your subject. And once again, always use fresh and engaging language to maintain the reader’s interest.

The last thing you want is for your reader to view your essay as a collection of individual points. A good essay should read as a unified whole, with all the pieces tying together naturally. You affirm your argument’s significance when you tie all the pieces together in your conclusion.

Providing New Insights

Also, think of this step as your opportunity to propose future research directions based on your findings. What could a student or researcher study next? What unanswered questions remain? If you’re having trouble answering these questions, consider using Smodin’s research tools to expand your knowledge of the topic.

That isn’t to say you can leave open-ended or unanswered questions about your own thesis. On the contrary, your conclusion should firmly establish the validity of your argument. That said, any deep and insightful analysis naturally leads to further exploration. Draw attention to these potential areas of inquiry.

(Optional) Form a Personal Connection With the Reader

Forming a connection with the reader in the conclusion can personalize and strengthen the impact of your essay. This technique can be powerful if implemented correctly, making your writing more relatable, human, and memorable.

That said, slime academics discourage using “I” in formal essays. It’s always best to clarify your teacher’s or professor’s stance before submitting your final draft.

If it is allowed, consider sharing a brief personal reflection or anecdote that ties back to the main themes of your essay. A personal touch can go a long way toward humanizing your arguments and creating a connection with the reader.

Whatever you choose, remember that your conclusion should always complement the analytical findings of your essay. Never say anything that detracts from your thesis or the findings you presented.

Examples of Good Conclusions

Let’s explore some examples to illustrate what a well-crafted conclusion looks and sounds like. The following are two hypothetical thesis essays from the fields of science and literature.

• Thesis Topic: The Impact of Climate Change on Coral Reefs
• Introduction: “Coral reefs act as the guardians of the ocean’s biodiversity. These underwater ecosystems are among the most vibrant and essential on the entire planet. However, the escalating impact of climate change poses a severe threat to their health and survival. This essay aims to dissect specific environmental changes contributing to coral degradation while proposing measures for mitigation.”
• Conclusion: “This investigation into the impact of climate change on coral reefs has revealed a disturbing acceleration of coral bleaching events and a significant decline of reef biodiversity. The findings presented in this study establish a clear link between increased sea temperatures and coral reef mortality. Future research should focus on the resilience mechanisms of coral species that could influence conservation strategies. The fate of the coral reefs depends on humanity’s immediate and concentrated action to curb global emissions and preserve these vital ecosystems for future generations.”

Notice how the conclusion doesn’t simply restate the thesis. Instead, it highlights the definitive connection between climate change and coral health. It also reiterates the issue’s urgency and extends a call of action for ongoing intervention. The last sentence is direct, to the point, and leaves a lasting impression on the reader.

If you’re struggling with your closing sentence (or any sentence, for that matter), Smodin’s Rewriter can create hundreds of different sentences in seconds. Then, choose the sentences and phrases that resonate the most and use them to craft a compelling conclusion.

• Thesis Topic: The Evolution of the American Dream in 20th-Century American Literature
• Introduction: “The American Dream was once defined by prosperity and success. However, throughout the 20th century, the representation of the American Dream in popular literature has undergone significant changes. Are these representations indicative of a far-reaching sentiment that lay dormant among the American public? Or were these works simply the result of disillusioned writers responding to the evolving challenges of the times?”
• Conclusion: “Works by F. Scott Fitzgerald, John Steinbeck, and Toni Morrison illustrate the American Dream’s evolution from unbridled optimism to a more critical examination of the American ethos. Throughout modernist and post-modernist literature, the American Dream is often at odds with core American values. These novels reflect broader societal shifts that continue to shape the national consciousness. Further research into contemporary literature could provide greater insight into the complexities of this concept.”

You will know exactly what this essay covers by reading the introduction and conclusion alone. It summarizes the evolution of the American Dream by examining the works of three unique authors. It then analyzes these works to demonstrate how they reflect broader societal shifts. The conclusion works as both a capstone and a bridge to set the stage for future inquiries.

Write Better Conclusions With Smodin

Always remember the human element behind the grading process when crafting your essay. Your teachers or professors are human and have likely spent countless hours reviewing essays on similar topics. The grading process can be long and exhaustive. Your conclusion should aim to make their task easier, not harder.

A well-crafted conclusion serves as the final piece to your argument. It should recap the critical insights discussed above while shedding new light on the topic. By including innovative elements and insightful observations, your conclusion will help your essay stand out from the crowd.

Make sure your essay ends on a high note to maximize your chances of getting a better grade now and in the future. Smodin’s comprehensive suite of AI tools can help you enhance every aspect of your essay writing. From initial research to structuring, these tools can streamline the process and improve the quality of your essays.

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When selecting a category, use the webpages and websites category only when a work does not fit better within another category. For example, a report from a government website would use the reports category, whereas a page on a government website that is not a report or other work would use the webpages and websites category.

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

Prevent plagiarism. Run a free check.

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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• Open access
• Published: 03 June 2024

Applying large language models for automated essay scoring for non-native Japanese

• Wenchao Li 1 &
• Haitao Liu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  723 ( 2024 ) Cite this article

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• Language and linguistics

Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

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Conventional machine learning technology in aes.

AES has experienced significant growth with the advancement of machine learning technologies in recent decades. In the earlier stages of AES development, conventional machine learning-based approaches were commonly used. These approaches involved the following procedures: a) feeding the machine with a dataset. In this step, a dataset of essays is provided to the machine learning system. The dataset serves as the basis for training the model and establishing patterns and correlations between linguistic features and human ratings. b) the machine learning model is trained using linguistic features that best represent human ratings and can effectively discriminate learners’ writing proficiency. These features include lexical richness (Lu, 2012 ; Kyle and Crossley, 2015 ; Kyle et al. 2021 ), syntactic complexity (Lu, 2010 ; Liu, 2008 ), text cohesion (Crossley and McNamara, 2016 ), and among others. Conventional machine learning approaches in AES require human intervention, such as manual correction and annotation of essays. This human involvement was necessary to create a labeled dataset for training the model. Several AES systems have been developed using conventional machine learning technologies. These include the Intelligent Essay Assessor (Landauer et al. 2003 ), the e-rater engine by Educational Testing Service (Attali and Burstein, 2006 ; Burstein, 2003 ), MyAccess with the InterlliMetric scoring engine by Vantage Learning (Elliot, 2003 ), and the Bayesian Essay Test Scoring system (Rudner and Liang, 2002 ). These systems have played a significant role in automating the essay scoring process and providing quick and consistent feedback to learners. However, as touched upon earlier, conventional machine learning approaches rely on predetermined linguistic features and often require manual intervention, making them less flexible and potentially limiting their generalizability to different contexts.

In the context of the Japanese language, conventional machine learning-incorporated AES tools include Jess (Ishioka and Kameda, 2006 ) and JWriter (Lee and Hasebe, 2017 ). Jess assesses essays by deducting points from the perfect score, utilizing the Mainichi Daily News newspaper as a database. The evaluation criteria employed by Jess encompass various aspects, such as rhetorical elements (e.g., reading comprehension, vocabulary diversity, percentage of complex words, and percentage of passive sentences), organizational structures (e.g., forward and reverse connection structures), and content analysis (e.g., latent semantic indexing). JWriter employs linear regression analysis to assign weights to various measurement indices, such as average sentence length and total number of characters. These weights are then combined to derive the overall score. A pilot study involving the Jess model was conducted on 1320 essays at different proficiency levels, including primary, intermediate, and advanced. However, the results indicated that the Jess model failed to significantly distinguish between these essay levels. Out of the 16 measures used, four measures, namely median sentence length, median clause length, median number of phrases, and maximum number of phrases, did not show statistically significant differences between the levels. Additionally, two measures exhibited between-level differences but lacked linear progression: the number of attributives declined words and the Kanji/kana ratio. On the other hand, the remaining measures, including maximum sentence length, maximum clause length, number of attributive conjugated words, maximum number of consecutive infinitive forms, maximum number of conjunctive-particle clauses, k characteristic value, percentage of big words, and percentage of passive sentences, demonstrated statistically significant between-level differences and displayed linear progression.

Both Jess and JWriter exhibit notable limitations, including the manual selection of feature parameters and weights, which can introduce biases into the scoring process. The reliance on human annotators to label non-native language essays also introduces potential noise and variability in the scoring. Furthermore, an important concern is the possibility of system manipulation and cheating by learners who are aware of the regression equation utilized by the models (Hirao et al. 2020 ). These limitations emphasize the need for further advancements in AES systems to address these challenges.

Deep learning technology in AES

Deep learning has emerged as one of the approaches for improving the accuracy and effectiveness of AES. Deep learning-based AES methods utilize artificial neural networks that mimic the human brain’s functioning through layered algorithms and computational units. Unlike conventional machine learning, deep learning autonomously learns from the environment and past errors without human intervention. This enables deep learning models to establish nonlinear correlations, resulting in higher accuracy. Recent advancements in deep learning have led to the development of transformers, which are particularly effective in learning text representations. Noteworthy examples include bidirectional encoder representations from transformers (BERT) (Devlin et al. 2019 ) and the generative pretrained transformer (GPT) (OpenAI).

BERT is a linguistic representation model that utilizes a transformer architecture and is trained on two tasks: masked linguistic modeling and next-sentence prediction (Hirao et al. 2020 ; Vaswani et al. 2017 ). In the context of AES, BERT follows specific procedures, as illustrated in Fig. 1 : (a) the tokenized prompts and essays are taken as input; (b) special tokens, such as [CLS] and [SEP], are added to mark the beginning and separation of prompts and essays; (c) the transformer encoder processes the prompt and essay sequences, resulting in hidden layer sequences; (d) the hidden layers corresponding to the [CLS] tokens (T[CLS]) represent distributed representations of the prompts and essays; and (e) a multilayer perceptron uses these distributed representations as input to obtain the final score (Hirao et al. 2020 ).

AES system with BERT (Hirao et al. 2020 ).

The training of BERT using a substantial amount of sentence data through the Masked Language Model (MLM) allows it to capture contextual information within the hidden layers. Consequently, BERT is expected to be capable of identifying artificial essays as invalid and assigning them lower scores (Mizumoto and Eguchi, 2023 ). In the context of AES for nonnative Japanese learners, Hirao et al. ( 2020 ) combined the long short-term memory (LSTM) model proposed by Hochreiter and Schmidhuber ( 1997 ) with BERT to develop a tailored automated Essay Scoring System. The findings of their study revealed that the BERT model outperformed both the conventional machine learning approach utilizing character-type features such as “kanji” and “hiragana”, as well as the standalone LSTM model. Takeuchi et al. ( 2021 ) presented an approach to Japanese AES that eliminates the requirement for pre-scored essays by relying solely on reference texts or a model answer for the essay task. They investigated multiple similarity evaluation methods, including frequency of morphemes, idf values calculated on Wikipedia, LSI, LDA, word-embedding vectors, and document vectors produced by BERT. The experimental findings revealed that the method utilizing the frequency of morphemes with idf values exhibited the strongest correlation with human-annotated scores across different essay tasks. The utilization of BERT in AES encounters several limitations. Firstly, essays often exceed the model’s maximum length limit. Second, only score labels are available for training, which restricts access to additional information.

Mizumoto and Eguchi ( 2023 ) were pioneers in employing the GPT model for AES in non-native English writing. Their study focused on evaluating the accuracy and reliability of AES using the GPT-3 text-davinci-003 model, analyzing a dataset of 12,100 essays from the corpus of nonnative written English (TOEFL11). The findings indicated that AES utilizing the GPT-3 model exhibited a certain degree of accuracy and reliability. They suggest that GPT-3-based AES systems hold the potential to provide support for human ratings. However, applying GPT model to AES presents a unique natural language processing (NLP) task that involves considerations such as nonnative language proficiency, the influence of the learner’s first language on the output in the target language, and identifying linguistic features that best indicate writing quality in a specific language. These linguistic features may differ morphologically or syntactically from those present in the learners’ first language, as observed in (1)–(3).

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

1 sg .-give. past- him-one .cl- book

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

Kare-ni-hon-o-age-mashi-ta

3 sg .- dat -hon- acc- give.honorification. past

Inflectional

give, give-s, gave, given, giving

Additionally, the morphological agglutination and subject-object-verb (SOV) order in Japanese, along with its idiomatic expressions, pose additional challenges for applying language models in AES tasks (4).

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

“My leg became like a stick (I am extremely tired).”

The example sentence provided demonstrates the morpho-syntactic structure of Japanese and the presence of an idiomatic expression. In this sentence, the verb “なる” (naru), meaning “to become”, appears at the end of the sentence. The verb stem “なり” (nari) is attached with morphemes indicating honorification (“ます” - mashu) and tense (“た” - ta), showcasing agglutination. While the sentence can be literally translated as “my leg became like a stick”, it carries an idiomatic interpretation that implies “I am extremely tired”.

To overcome this issue, CyberAgent Inc. ( 2023 ) has developed the Open-Calm series of language models specifically designed for Japanese. Open-Calm consists of pre-trained models available in various sizes, such as Small, Medium, Large, and 7b. Figure 2 depicts the fundamental structure of the Open-Calm model. A key feature of this architecture is the incorporation of the Lora Adapter and GPT-NeoX frameworks, which can enhance its language processing capabilities.

GPT-NeoX Model Architecture (Okgetheng and Takeuchi 2024 ).

In a recent study conducted by Okgetheng and Takeuchi ( 2024 ), they assessed the efficacy of Open-Calm language models in grading Japanese essays. The research utilized a dataset of approximately 300 essays, which were annotated by native Japanese educators. The findings of the study demonstrate the considerable potential of Open-Calm language models in automated Japanese essay scoring. Specifically, among the Open-Calm family, the Open-Calm Large model (referred to as OCLL) exhibited the highest performance. However, it is important to note that, as of the current date, the Open-Calm Large model does not offer public access to its server. Consequently, users are required to independently deploy and operate the environment for OCLL. In order to utilize OCLL, users must have a PC equipped with an NVIDIA GeForce RTX 3060 (8 or 12 GB VRAM).

In summary, while the potential of LLMs in automated scoring of nonnative Japanese essays has been demonstrated in two studies—BERT-driven AES (Hirao et al. 2020 ) and OCLL-based AES (Okgetheng and Takeuchi, 2024 )—the number of research efforts in this area remains limited.

Another significant challenge in applying LLMs to AES lies in prompt engineering and ensuring its reliability and effectiveness (Brown et al. 2020 ; Rae et al. 2021 ; Zhang et al. 2021 ). Various prompting strategies have been proposed, such as the zero-shot chain of thought (CoT) approach (Kojima et al. 2022 ), which involves manually crafting diverse and effective examples. However, manual efforts can lead to mistakes. To address this, Zhang et al. ( 2021 ) introduced an automatic CoT prompting method called Auto-CoT, which demonstrates matching or superior performance compared to the CoT paradigm. Another prompt framework is trees of thoughts, enabling a model to self-evaluate its progress at intermediate stages of problem-solving through deliberate reasoning (Yao et al. 2023 ).

Beyond linguistic studies, there has been a noticeable increase in the number of foreign workers in Japan and Japanese learners worldwide (Ministry of Health, Labor, and Welfare of Japan, 2022 ; Japan Foundation, 2021 ). However, existing assessment methods, such as the Japanese Language Proficiency Test (JLPT), J-CAT, and TTBJ Footnote 1 , primarily focus on reading, listening, vocabulary, and grammar skills, neglecting the evaluation of writing proficiency. As the number of workers and language learners continues to grow, there is a rising demand for an efficient AES system that can reduce costs and time for raters and be utilized for employment, examinations, and self-study purposes.

This study aims to explore the potential of LLM-based AES by comparing the effectiveness of five models: two LLMs (GPT Footnote 2 and BERT), one Japanese local LLM (OCLL), and two conventional machine learning-based methods (linguistic feature-based scoring tools - Jess and JWriter).

The research questions addressed in this study are as follows:

To what extent do the LLM-driven AES and linguistic feature-based AES, when used as automated tools to support human rating, accurately reflect test takers’ actual performance?

What influence does the prompt have on the accuracy and performance of LLM-based AES methods?

The subsequent sections of the manuscript cover the methodology, including the assessment measures for nonnative Japanese writing proficiency, criteria for prompts, and the dataset. The evaluation section focuses on the analysis of annotations and rating scores generated by LLM-driven and linguistic feature-based AES methods.

Methodology

The dataset utilized in this study was obtained from the International Corpus of Japanese as a Second Language (I-JAS) Footnote 3 . This corpus consisted of 1000 participants who represented 12 different first languages. For the study, the participants were given a story-writing task on a personal computer. They were required to write two stories based on the 4-panel illustrations titled “Picnic” and “The key” (see Appendix A). Background information for the participants was provided by the corpus, including their Japanese language proficiency levels assessed through two online tests: J-CAT and SPOT. These tests evaluated their reading, listening, vocabulary, and grammar abilities. The learners’ proficiency levels were categorized into six levels aligned with the Common European Framework of Reference for Languages (CEFR) and the Reference Framework for Japanese Language Education (RFJLE): A1, A2, B1, B2, C1, and C2. According to Lee et al. ( 2015 ), there is a high level of agreement (r = 0.86) between the J-CAT and SPOT assessments, indicating that the proficiency certifications provided by J-CAT are consistent with those of SPOT. However, it is important to note that the scores of J-CAT and SPOT do not have a one-to-one correspondence. In this study, the J-CAT scores were used as a benchmark to differentiate learners of different proficiency levels. A total of 1400 essays were utilized, representing the beginner (aligned with A1), A2, B1, B2, C1, and C2 levels based on the J-CAT scores. Table 1 provides information about the learners’ proficiency levels and their corresponding J-CAT and SPOT scores.

A dataset comprising a total of 1400 essays from the story writing tasks was collected. Among these, 714 essays were utilized to evaluate the reliability of the LLM-based AES method, while the remaining 686 essays were designated as development data to assess the LLM-based AES’s capability to distinguish participants with varying proficiency levels. The GPT 4 API was used in this study. A detailed explanation of the prompt-assessment criteria is provided in Section Prompt . All essays were sent to the model for measurement and scoring.

Measures of writing proficiency for nonnative Japanese

Japanese exhibits a morphologically agglutinative structure where morphemes are attached to the word stem to convey grammatical functions such as tense, aspect, voice, and honorifics, e.g. (5).

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

[eat (stem)-causative-passive voice-honorification-tense. past-question marker]

Japanese employs nine case particles to indicate grammatical functions: the nominative case particle が (ga), the accusative case particle を (o), the genitive case particle の (no), the dative case particle に (ni), the locative/instrumental case particle で (de), the ablative case particle から (kara), the directional case particle へ (e), and the comitative case particle と (to). The agglutinative nature of the language, combined with the case particle system, provides an efficient means of distinguishing between active and passive voice, either through morphemes or case particles, e.g. 食べる taberu “eat concusive . ” (active voice); 食べられる taberareru “eat concusive . ” (passive voice). In the active voice, “パン を 食べる” (pan o taberu) translates to “to eat bread”. On the other hand, in the passive voice, it becomes “パン が 食べられた” (pan ga taberareta), which means “(the) bread was eaten”. Additionally, it is important to note that different conjugations of the same lemma are considered as one type in order to ensure a comprehensive assessment of the language features. For example, e.g., 食べる taberu “eat concusive . ”; 食べている tabeteiru “eat progress .”; 食べた tabeta “eat past . ” as one type.

To incorporate these features, previous research (Suzuki, 1999 ; Watanabe et al. 1988 ; Ishioka, 2001 ; Ishioka and Kameda, 2006 ; Hirao et al. 2020 ) has identified complexity, fluency, and accuracy as crucial factors for evaluating writing quality. These criteria are assessed through various aspects, including lexical richness (lexical density, diversity, and sophistication), syntactic complexity, and cohesion (Kyle et al. 2021 ; Mizumoto and Eguchi, 2023 ; Ure, 1971 ; Halliday, 1985 ; Barkaoui and Hadidi, 2020 ; Zenker and Kyle, 2021 ; Kim et al. 2018 ; Lu, 2017 ; Ortega, 2015 ). Therefore, this study proposes five scoring categories: lexical richness, syntactic complexity, cohesion, content elaboration, and grammatical accuracy. A total of 16 measures were employed to capture these categories. The calculation process and specific details of these measures can be found in Table 2 .

T-unit, first introduced by Hunt ( 1966 ), is a measure used for evaluating speech and composition. It serves as an indicator of syntactic development and represents the shortest units into which a piece of discourse can be divided without leaving any sentence fragments. In the context of Japanese language assessment, Sakoda and Hosoi ( 2020 ) utilized T-unit as the basic unit to assess the accuracy and complexity of Japanese learners’ speaking and storytelling. The calculation of T-units in Japanese follows the following principles:

A single main clause constitutes 1 T-unit, regardless of the presence or absence of dependent clauses, e.g. (6).

ケンとマリはピクニックに行きました (main clause): 1 T-unit.

If a sentence contains a main clause along with subclauses, each subclause is considered part of the same T-unit, e.g. (7).

天気が良かった の で (subclause)、ケンとマリはピクニックに行きました (main clause): 1 T-unit.

In the case of coordinate clauses, where multiple clauses are connected, each coordinated clause is counted separately. Thus, a sentence with coordinate clauses may have 2 T-units or more, e.g. (8).

ケンは地図で場所を探して (coordinate clause)、マリはサンドイッチを作りました (coordinate clause): 2 T-units.

Lexical diversity refers to the range of words used within a text (Engber, 1995 ; Kyle et al. 2021 ) and is considered a useful measure of the breadth of vocabulary in L n production (Jarvis, 2013a , 2013b ).

The type/token ratio (TTR) is widely recognized as a straightforward measure for calculating lexical diversity and has been employed in numerous studies. These studies have demonstrated a strong correlation between TTR and other methods of measuring lexical diversity (e.g., Bentz et al. 2016 ; Čech and Miroslav, 2018 ; Çöltekin and Taraka, 2018 ). TTR is computed by considering both the number of unique words (types) and the total number of words (tokens) in a given text. Given that the length of learners’ writing texts can vary, this study employs the moving average type-token ratio (MATTR) to mitigate the influence of text length. MATTR is calculated using a 50-word moving window. Initially, a TTR is determined for words 1–50 in an essay, followed by words 2–51, 3–52, and so on until the end of the essay is reached (Díez-Ortega and Kyle, 2023 ). The final MATTR scores were obtained by averaging the TTR scores for all 50-word windows. The following formula was employed to derive MATTR:

\({\rm{MATTR}}({\rm{W}})=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{N}}-{\rm{W}}+1}{{\rm{F}}}_{{\rm{i}}}}{{\rm{W}}({\rm{N}}-{\rm{W}}+1)}\)

Here, N refers to the number of tokens in the corpus. W is the randomly selected token size (W < N). \({F}_{i}\) is the number of types in each window. The \({\rm{MATTR}}({\rm{W}})\) is the mean of a series of type-token ratios (TTRs) based on the word form for all windows. It is expected that individuals with higher language proficiency will produce texts with greater lexical diversity, as indicated by higher MATTR scores.

Lexical density was captured by the ratio of the number of lexical words to the total number of words (Lu, 2012 ). Lexical sophistication refers to the utilization of advanced vocabulary, often evaluated through word frequency indices (Crossley et al. 2013 ; Haberman, 2008 ; Kyle and Crossley, 2015 ; Laufer and Nation, 1995 ; Lu, 2012 ; Read, 2000 ). In line of writing, lexical sophistication can be interpreted as vocabulary breadth, which entails the appropriate usage of vocabulary items across various lexicon-grammatical contexts and registers (Garner et al. 2019 ; Kim et al. 2018 ; Kyle et al. 2018 ). In Japanese specifically, words are considered lexically sophisticated if they are not included in the “Japanese Education Vocabulary List Ver 1.0”. Footnote 4 Consequently, lexical sophistication was calculated by determining the number of sophisticated word types relative to the total number of words per essay. Furthermore, it has been suggested that, in Japanese writing, sentences should ideally have a length of no more than 40 to 50 characters, as this promotes readability. Therefore, the median and maximum sentence length can be considered as useful indices for assessment (Ishioka and Kameda, 2006 ).

Syntactic complexity was assessed based on several measures, including the mean length of clauses, verb phrases per T-unit, clauses per T-unit, dependent clauses per T-unit, complex nominals per clause, adverbial clauses per clause, coordinate phrases per clause, and mean dependency distance (MDD). The MDD reflects the distance between the governor and dependent positions in a sentence. A larger dependency distance indicates a higher cognitive load and greater complexity in syntactic processing (Liu, 2008 ; Liu et al. 2017 ). The MDD has been established as an efficient metric for measuring syntactic complexity (Jiang, Quyang, and Liu, 2019 ; Li and Yan, 2021 ). To calculate the MDD, the position numbers of the governor and dependent are subtracted, assuming that words in a sentence are assigned in a linear order, such as W1 … Wi … Wn. In any dependency relationship between words Wa and Wb, Wa is the governor and Wb is the dependent. The MDD of the entire sentence was obtained by taking the absolute value of governor – dependent:

MDD = \(\frac{1}{n}{\sum }_{i=1}^{n}|{\rm{D}}{{\rm{D}}}_{i}|\)

In this formula, \(n\) represents the number of words in the sentence, and \({DD}i\) is the dependency distance of the \({i}^{{th}}\) dependency relationship of a sentence. Building on this, the annotation of sentence ‘Mary-ga-John-ni-keshigomu-o-watashita was [Mary- top -John- dat -eraser- acc -give- past] ’. The sentence’s MDD would be 2. Table 3 provides the CSV file as a prompt for GPT 4.

Cohesion (semantic similarity) and content elaboration aim to capture the ideas presented in test taker’s essays. Cohesion was assessed using three measures: Synonym overlap/paragraph (topic), Synonym overlap/paragraph (keywords), and word2vec cosine similarity. Content elaboration and development were measured as the number of metadiscourse markers (type)/number of words. To capture content closely, this study proposed a novel-distance based representation, by encoding the cosine distance between the essay (by learner) and essay task’s (topic and keyword) i -vectors. The learner’s essay is decoded into a word sequence, and aligned to the essay task’ topic and keyword for log-likelihood measurement. The cosine distance reveals the content elaboration score in the leaners’ essay. The mathematical equation of cosine similarity between target-reference vectors is shown in (11), assuming there are i essays and ( L i , …. L n ) and ( N i , …. N n ) are the vectors representing the learner and task’s topic and keyword respectively. The content elaboration distance between L i and N i was calculated as follows:

\(\cos \left(\theta \right)=\frac{{\rm{L}}\,\cdot\, {\rm{N}}}{\left|{\rm{L}}\right|{\rm{|N|}}}=\frac{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}{N}_{i}}{\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}^{2}}\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{N}_{i}^{2}}}\)

A high similarity value indicates a low difference between the two recognition outcomes, which in turn suggests a high level of proficiency in content elaboration.

To evaluate the effectiveness of the proposed measures in distinguishing different proficiency levels among nonnative Japanese speakers’ writing, we conducted a multi-faceted Rasch measurement analysis (Linacre, 1994 ). This approach applies measurement models to thoroughly analyze various factors that can influence test outcomes, including test takers’ proficiency, item difficulty, and rater severity, among others. The underlying principles and functionality of multi-faceted Rasch measurement are illustrated in (12).

\(\log \left(\frac{{P}_{{nijk}}}{{P}_{{nij}(k-1)}}\right)={B}_{n}-{D}_{i}-{C}_{j}-{F}_{k}\)

(12) defines the logarithmic transformation of the probability ratio ( P nijk /P nij(k-1) )) as a function of multiple parameters. Here, n represents the test taker, i denotes a writing proficiency measure, j corresponds to the human rater, and k represents the proficiency score. The parameter B n signifies the proficiency level of test taker n (where n ranges from 1 to N). D j represents the difficulty parameter of test item i (where i ranges from 1 to L), while C j represents the severity of rater j (where j ranges from 1 to J). Additionally, F k represents the step difficulty for a test taker to move from score ‘k-1’ to k . P nijk refers to the probability of rater j assigning score k to test taker n for test item i . P nij(k-1) represents the likelihood of test taker n being assigned score ‘k-1’ by rater j for test item i . Each facet within the test is treated as an independent parameter and estimated within the same reference framework. To evaluate the consistency of scores obtained through both human and computer analysis, we utilized the Infit mean-square statistic. This statistic is a chi-square measure divided by the degrees of freedom and is weighted with information. It demonstrates higher sensitivity to unexpected patterns in responses to items near a person’s proficiency level (Linacre, 2002 ). Fit statistics are assessed based on predefined thresholds for acceptable fit. For the Infit MNSQ, which has a mean of 1.00, different thresholds have been suggested. Some propose stricter thresholds ranging from 0.7 to 1.3 (Bond et al. 2021 ), while others suggest more lenient thresholds ranging from 0.5 to 1.5 (Eckes, 2009 ). In this study, we adopted the criterion of 0.70–1.30 for the Infit MNSQ.

Moving forward, we can now proceed to assess the effectiveness of the 16 proposed measures based on five criteria for accurately distinguishing various levels of writing proficiency among non-native Japanese speakers. To conduct this evaluation, we utilized the development dataset from the I-JAS corpus, as described in Section Dataset . Table 4 provides a measurement report that presents the performance details of the 14 metrics under consideration. The measure separation was found to be 4.02, indicating a clear differentiation among the measures. The reliability index for the measure separation was 0.891, suggesting consistency in the measurement. Similarly, the person separation reliability index was 0.802, indicating the accuracy of the assessment in distinguishing between individuals. All 16 measures demonstrated Infit mean squares within a reasonable range, ranging from 0.76 to 1.28. The Synonym overlap/paragraph (topic) measure exhibited a relatively high outfit mean square of 1.46, although the Infit mean square falls within an acceptable range. The standard error for the measures ranged from 0.13 to 0.28, indicating the precision of the estimates.

Table 5 further illustrated the weights assigned to different linguistic measures for score prediction, with higher weights indicating stronger correlations between those measures and higher scores. Specifically, the following measures exhibited higher weights compared to others: moving average type token ratio per essay has a weight of 0.0391. Mean dependency distance had a weight of 0.0388. Mean length of clause, calculated by dividing the number of words by the number of clauses, had a weight of 0.0374. Complex nominals per T-unit, calculated by dividing the number of complex nominals by the number of T-units, had a weight of 0.0379. Coordinate phrases rate, calculated by dividing the number of coordinate phrases by the number of clauses, had a weight of 0.0325. Grammatical error rate, representing the number of errors per essay, had a weight of 0.0322.

Criteria (output indicator)

The criteria used to evaluate the writing ability in this study were based on CEFR, which follows a six-point scale ranging from A1 to C2. To assess the quality of Japanese writing, the scoring criteria from Table 6 were utilized. These criteria were derived from the IELTS writing standards and served as assessment guidelines and prompts for the written output.

A prompt is a question or detailed instruction that is provided to the model to obtain a proper response. After several pilot experiments, we decided to provide the measures (Section Measures of writing proficiency for nonnative Japanese ) as the input prompt and use the criteria (Section Criteria (output indicator) ) as the output indicator. Regarding the prompt language, considering that the LLM was tasked with rating Japanese essays, would prompt in Japanese works better Footnote 5 ? We conducted experiments comparing the performance of GPT-4 using both English and Japanese prompts. Additionally, we utilized the Japanese local model OCLL with Japanese prompts. Multiple trials were conducted using the same sample. Regardless of the prompt language used, we consistently obtained the same grading results with GPT-4, which assigned a grade of B1 to the writing sample. This suggested that GPT-4 is reliable and capable of producing consistent ratings regardless of the prompt language. On the other hand, when we used Japanese prompts with the Japanese local model “OCLL”, we encountered inconsistent grading results. Out of 10 attempts with OCLL, only 6 yielded consistent grading results (B1), while the remaining 4 showed different outcomes, including A1 and B2 grades. These findings indicated that the language of the prompt was not the determining factor for reliable AES. Instead, the size of the training data and the model parameters played crucial roles in achieving consistent and reliable AES results for the language model.

The following is the utilized prompt, which details all measures and requires the LLM to score the essays using holistic and trait scores.

Please evaluate Japanese essays written by Japanese learners and assign a score to each essay on a six-point scale, ranging from A1, A2, B1, B2, C1 to C2. Additionally, please provide trait scores and display the calculation process for each trait score. The scoring should be based on the following criteria:

Moving average type-token ratio.

Number of lexical words (token) divided by the total number of words per essay.

Number of sophisticated word types divided by the total number of words per essay.

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

Number of metadiscourse markers (types) divided by the total number of words.

Number of errors per essay.

Japanese essay text

出かける前に二人が地図を見ている間に、サンドイッチを入れたバスケットに犬が入ってしまいました。それに気づかずに二人は楽しそうに出かけて行きました。やがて突然犬がバスケットから飛び出し、二人は驚きました。バスケット の 中を見ると、食べ物はすべて犬に食べられていて、二人は困ってしまいました。(ID_JJJ01_SW1)

The score of the example above was B1. Figure 3 provides an example of holistic and trait scores provided by GPT-4 (with a prompt indicating all measures) via Bing Footnote 6 .

Example of GPT-4 AES and feedback (with a prompt indicating all measures).

Statistical analysis

The aim of this study is to investigate the potential use of LLM for nonnative Japanese AES. It seeks to compare the scoring outcomes obtained from feature-based AES tools, which rely on conventional machine learning technology (i.e. Jess, JWriter), with those generated by AI-driven AES tools utilizing deep learning technology (BERT, GPT, OCLL). To assess the reliability of a computer-assisted annotation tool, the study initially established human-human agreement as the benchmark measure. Subsequently, the performance of the LLM-based method was evaluated by comparing it to human-human agreement.

To assess annotation agreement, the study employed standard measures such as precision, recall, and F-score (Brants 2000 ; Lu 2010 ), along with the quadratically weighted kappa (QWK) to evaluate the consistency and agreement in the annotation process. Assume A and B represent human annotators. When comparing the annotations of the two annotators, the following results are obtained. The evaluation of precision, recall, and F-score metrics was illustrated in equations (13) to (15).

\({\rm{Recall}}(A,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,A}\)

\({\rm{Precision}}(A,\,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,B}\)

The F-score is the harmonic mean of recall and precision:

\({\rm{F}}-{\rm{score}}=\frac{2* ({\rm{Precision}}* {\rm{Recall}})}{{\rm{Precision}}+{\rm{Recall}}}\)

The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.

In accordance with Taghipour and Ng ( 2016 ), the calculation of QWK involves two steps:

Step 1: Construct a weight matrix W as follows:

\({W}_{{ij}}=\frac{{(i-j)}^{2}}{{(N-1)}^{2}}\)

i represents the annotation made by the tool, while j represents the annotation made by a human rater. N denotes the total number of possible annotations. Matrix O is subsequently computed, where O_( i, j ) represents the count of data annotated by the tool ( i ) and the human annotator ( j ). On the other hand, E refers to the expected count matrix, which undergoes normalization to ensure that the sum of elements in E matches the sum of elements in O.

Step 2: With matrices O and E, the QWK is obtained as follows:

K = 1- \(\frac{\sum i,j{W}_{i,j}\,{O}_{i,j}}{\sum i,j{W}_{i,j}\,{E}_{i,j}}\)

The value of the quadratic weighted kappa increases as the level of agreement improves. Further, to assess the accuracy of LLM scoring, the proportional reductive mean square error (PRMSE) was employed. The PRMSE approach takes into account the variability observed in human ratings to estimate the rater error, which is then subtracted from the variance of the human labels. This calculation provides an overall measure of agreement between the automated scores and true scores (Haberman et al. 2015 ; Loukina et al. 2020 ; Taghipour and Ng, 2016 ). The computation of PRMSE involves the following steps:

Step 1: Calculate the mean squared errors (MSEs) for the scoring outcomes of the computer-assisted tool (MSE tool) and the human scoring outcomes (MSE human).

Step 2: Determine the PRMSE by comparing the MSE of the computer-assisted tool (MSE tool) with the MSE from human raters (MSE human), using the following formula:

\({\rm{PRMSE}}=1-\frac{({\rm{MSE}}\,{\rm{tool}})\,}{({\rm{MSE}}\,{\rm{human}})\,}=1-\,\frac{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-{\hat{{\rm{y}}}}_{{\rm{i}}})}^{2}}{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-\hat{{\rm{y}}})}^{2}}\)

In the numerator, ŷi represents the scoring outcome predicted by a specific LLM-driven AES system for a given sample. The term y i − ŷ i represents the difference between this predicted outcome and the mean value of all LLM-driven AES systems’ scoring outcomes. It quantifies the deviation of the specific LLM-driven AES system’s prediction from the average prediction of all LLM-driven AES systems. In the denominator, y i − ŷ represents the difference between the scoring outcome provided by a specific human rater for a given sample and the mean value of all human raters’ scoring outcomes. It measures the discrepancy between the specific human rater’s score and the average score given by all human raters. The PRMSE is then calculated by subtracting the ratio of the MSE tool to the MSE human from 1. PRMSE falls within the range of 0 to 1, with larger values indicating reduced errors in LLM’s scoring compared to those of human raters. In other words, a higher PRMSE implies that LLM’s scoring demonstrates greater accuracy in predicting the true scores (Loukina et al. 2020 ). The interpretation of kappa values, ranging from 0 to 1, is based on the work of Landis and Koch ( 1977 ). Specifically, the following categories are assigned to different ranges of kappa values: −1 indicates complete inconsistency, 0 indicates random agreement, 0.0 ~ 0.20 indicates extremely low level of agreement (slight), 0.21 ~ 0.40 indicates moderate level of agreement (fair), 0.41 ~ 0.60 indicates medium level of agreement (moderate), 0.61 ~ 0.80 indicates high level of agreement (substantial), 0.81 ~ 1 indicates almost perfect level of agreement. All statistical analyses were executed using Python script.

Results and discussion

Annotation reliability of the llm.

This section focuses on assessing the reliability of the LLM’s annotation and scoring capabilities. To evaluate the reliability, several tests were conducted simultaneously, aiming to achieve the following objectives:

Assess the LLM’s ability to differentiate between test takers with varying levels of oral proficiency.

Determine the level of agreement between the annotations and scoring performed by the LLM and those done by human raters.

The evaluation of the results encompassed several metrics, including: precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement.

Inter-annotator agreement (human–human annotator agreement)

We started with an agreement test of the two human annotators. Two trained annotators were recruited to determine the writing task data measures. A total of 714 scripts, as the test data, was utilized. Each analysis lasted 300–360 min. Inter-annotator agreement was evaluated using the standard measures of precision, recall, and F-score and QWK. Table 7 presents the inter-annotator agreement for the various indicators. As shown, the inter-annotator agreement was fairly high, with F-scores ranging from 1.0 for sentence and word number to 0.666 for grammatical errors.

The findings from the QWK analysis provided further confirmation of the inter-annotator agreement. The QWK values covered a range from 0.950 ( p  = 0.000) for sentence and word number to 0.695 for synonym overlap number (keyword) and grammatical errors ( p  = 0.001).

Agreement of annotation outcomes between human and LLM

To evaluate the consistency between human annotators and LLM annotators (BERT, GPT, OCLL) across the indices, the same test was conducted. The results of the inter-annotator agreement (F-score) between LLM and human annotation are provided in Appendix B-D. The F-scores ranged from 0.706 for Grammatical error # for OCLL-human to a perfect 1.000 for GPT-human, for sentences, clauses, T-units, and words. These findings were further supported by the QWK analysis, which showed agreement levels ranging from 0.807 ( p  = 0.001) for metadiscourse markers for OCLL-human to 0.962 for words ( p  = 0.000) for GPT-human. The findings demonstrated that the LLM annotation achieved a significant level of accuracy in identifying measurement units and counts.

Reliability of LLM-driven AES’s scoring and discriminating proficiency levels

This section examines the reliability of the LLM-driven AES scoring through a comparison of the scoring outcomes produced by human raters and the LLM ( Reliability of LLM-driven AES scoring ). It also assesses the effectiveness of the LLM-based AES system in differentiating participants with varying proficiency levels ( Reliability of LLM-driven AES discriminating proficiency levels ).

Reliability of LLM-driven AES scoring

Table 8 summarizes the QWK coefficient analysis between the scores computed by the human raters and the GPT-4 for the individual essays from I-JAS Footnote 7 . As shown, the QWK of all measures ranged from k  = 0.819 for lexical density (number of lexical words (tokens)/number of words per essay) to k  = 0.644 for word2vec cosine similarity. Table 9 further presents the Pearson correlations between the 16 writing proficiency measures scored by human raters and GPT 4 for the individual essays. The correlations ranged from 0.672 for syntactic complexity to 0.734 for grammatical accuracy. The correlations between the writing proficiency scores assigned by human raters and the BERT-based AES system were found to range from 0.661 for syntactic complexity to 0.713 for grammatical accuracy. The correlations between the writing proficiency scores given by human raters and the OCLL-based AES system ranged from 0.654 for cohesion to 0.721 for grammatical accuracy. These findings indicated an alignment between the assessments made by human raters and both the BERT-based and OCLL-based AES systems in terms of various aspects of writing proficiency.

Reliability of LLM-driven AES discriminating proficiency levels

After validating the reliability of the LLM’s annotation and scoring, the subsequent objective was to evaluate its ability to distinguish between various proficiency levels. For this analysis, a dataset of 686 individual essays was utilized. Table 10 presents a sample of the results, summarizing the means, standard deviations, and the outcomes of the one-way ANOVAs based on the measures assessed by the GPT-4 model. A post hoc multiple comparison test, specifically the Bonferroni test, was conducted to identify any potential differences between pairs of levels.

As the results reveal, seven measures presented linear upward or downward progress across the three proficiency levels. These were marked in bold in Table 10 and comprise one measure of lexical richness, i.e. MATTR (lexical diversity); four measures of syntactic complexity, i.e. MDD (mean dependency distance), MLC (mean length of clause), CNT (complex nominals per T-unit), CPC (coordinate phrases rate); one cohesion measure, i.e. word2vec cosine similarity and GER (grammatical error rate). Regarding the ability of the sixteen measures to distinguish adjacent proficiency levels, the Bonferroni tests indicated that statistically significant differences exist between the primary level and the intermediate level for MLC and GER. One measure of lexical richness, namely LD, along with three measures of syntactic complexity (VPT, CT, DCT, ACC), two measures of cohesion (SOPT, SOPK), and one measure of content elaboration (IMM), exhibited statistically significant differences between proficiency levels. However, these differences did not demonstrate a linear progression between adjacent proficiency levels. No significant difference was observed in lexical sophistication between proficiency levels.

To summarize, our study aimed to evaluate the reliability and differentiation capabilities of the LLM-driven AES method. For the first objective, we assessed the LLM’s ability to differentiate between test takers with varying levels of oral proficiency using precision, recall, F-Score, and quadratically-weighted kappa. Regarding the second objective, we compared the scoring outcomes generated by human raters and the LLM to determine the level of agreement. We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency measures for the individual essays. The results confirmed the feasibility of using the LLM for annotation and scoring in AES for nonnative Japanese. As a result, Research Question 1 has been addressed.

Comparison of BERT-, GPT-, OCLL-based AES, and linguistic-feature-based computation methods

This section aims to compare the effectiveness of five AES methods for nonnative Japanese writing, i.e. LLM-driven approaches utilizing BERT, GPT, and OCLL, linguistic feature-based approaches using Jess and JWriter. The comparison was conducted by comparing the ratings obtained from each approach with human ratings. All ratings were derived from the dataset introduced in Dataset . To facilitate the comparison, the agreement between the automated methods and human ratings was assessed using QWK and PRMSE. The performance of each approach was summarized in Table 11 .

The QWK coefficient values indicate that LLMs (GPT, BERT, OCLL) and human rating outcomes demonstrated higher agreement compared to feature-based AES methods (Jess and JWriter) in assessing writing proficiency criteria, including lexical richness, syntactic complexity, content, and grammatical accuracy. Among the LLMs, the GPT-4 driven AES and human rating outcomes showed the highest agreement in all criteria, except for syntactic complexity. The PRMSE values suggest that the GPT-based method outperformed linguistic feature-based methods and other LLM-based approaches. Moreover, an interesting finding emerged during the study: the agreement coefficient between GPT-4 and human scoring was even higher than the agreement between different human raters themselves. This discovery highlights the advantage of GPT-based AES over human rating. Ratings involve a series of processes, including reading the learners’ writing, evaluating the content and language, and assigning scores. Within this chain of processes, various biases can be introduced, stemming from factors such as rater biases, test design, and rating scales. These biases can impact the consistency and objectivity of human ratings. GPT-based AES may benefit from its ability to apply consistent and objective evaluation criteria. By prompting the GPT model with detailed writing scoring rubrics and linguistic features, potential biases in human ratings can be mitigated. The model follows a predefined set of guidelines and does not possess the same subjective biases that human raters may exhibit. This standardization in the evaluation process contributes to the higher agreement observed between GPT-4 and human scoring. Section Prompt strategy of the study delves further into the role of prompts in the application of LLMs to AES. It explores how the choice and implementation of prompts can impact the performance and reliability of LLM-based AES methods. Furthermore, it is important to acknowledge the strengths of the local model, i.e. the Japanese local model OCLL, which excels in processing certain idiomatic expressions. Nevertheless, our analysis indicated that GPT-4 surpasses local models in AES. This superior performance can be attributed to the larger parameter size of GPT-4, estimated to be between 500 billion and 1 trillion, which exceeds the sizes of both BERT and the local model OCLL.

Prompt strategy

In the context of prompt strategy, Mizumoto and Eguchi ( 2023 ) conducted a study where they applied the GPT-3 model to automatically score English essays in the TOEFL test. They found that the accuracy of the GPT model alone was moderate to fair. However, when they incorporated linguistic measures such as cohesion, syntactic complexity, and lexical features alongside the GPT model, the accuracy significantly improved. This highlights the importance of prompt engineering and providing the model with specific instructions to enhance its performance. In this study, a similar approach was taken to optimize the performance of LLMs. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. Model 1 was used as the baseline, representing GPT-4 without any additional prompting. Model 2, on the other hand, involved GPT-4 prompted with 16 measures that included scoring criteria, efficient linguistic features for writing assessment, and detailed measurement units and calculation formulas. The remaining models (Models 3 to 18) utilized GPT-4 prompted with individual measures. The performance of these 18 different models was assessed using the output indicators described in Section Criteria (output indicator) . By comparing the performances of these models, the study aimed to understand the impact of prompt engineering on the accuracy and effectiveness of GPT-4 in AES tasks.

Based on the PRMSE scores presented in Fig. 4 , it was observed that Model 1, representing GPT-4 without any additional prompting, achieved a fair level of performance. However, Model 2, which utilized GPT-4 prompted with all measures, outperformed all other models in terms of PRMSE score, achieving a score of 0.681. These results indicate that the inclusion of specific measures and prompts significantly enhanced the performance of GPT-4 in AES. Among the measures, syntactic complexity was found to play a particularly significant role in improving the accuracy of GPT-4 in assessing writing quality. Following that, lexical diversity emerged as another important factor contributing to the model’s effectiveness. The study suggests that a well-prompted GPT-4 can serve as a valuable tool to support human assessors in evaluating writing quality. By utilizing GPT-4 as an automated scoring tool, the evaluation biases associated with human raters can be minimized. This has the potential to empower teachers by allowing them to focus on designing writing tasks and guiding writing strategies, while leveraging the capabilities of GPT-4 for efficient and reliable scoring.

PRMSE scores of the 18 AES models.

This study aimed to investigate two main research questions: the feasibility of utilizing LLMs for AES and the impact of prompt engineering on the application of LLMs in AES.

To address the first objective, the study compared the effectiveness of five different models: GPT, BERT, the Japanese local LLM (OCLL), and two conventional machine learning-based AES tools (Jess and JWriter). The PRMSE values indicated that the GPT-4-based method outperformed other LLMs (BERT, OCLL) and linguistic feature-based computational methods (Jess and JWriter) across various writing proficiency criteria. Furthermore, the agreement coefficient between GPT-4 and human scoring surpassed the agreement among human raters themselves, highlighting the potential of using the GPT-4 tool to enhance AES by reducing biases and subjectivity, saving time, labor, and cost, and providing valuable feedback for self-study. Regarding the second goal, the role of prompt design was investigated by comparing 18 models, including a baseline model, a model prompted with all measures, and 16 models prompted with one measure at a time. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. The PRMSE scores of the models showed that GPT-4 prompted with all measures achieved the best performance, surpassing the baseline and other models.

In conclusion, this study has demonstrated the potential of LLMs in supporting human rating in assessments. By incorporating automation, we can save time and resources while reducing biases and subjectivity inherent in human rating processes. Automated language assessments offer the advantage of accessibility, providing equal opportunities and economic feasibility for individuals who lack access to traditional assessment centers or necessary resources. LLM-based language assessments provide valuable feedback and support to learners, aiding in the enhancement of their language proficiency and the achievement of their goals. This personalized feedback can cater to individual learner needs, facilitating a more tailored and effective language-learning experience.

There are three important areas that merit further exploration. First, prompt engineering requires attention to ensure optimal performance of LLM-based AES across different language types. This study revealed that GPT-4, when prompted with all measures, outperformed models prompted with fewer measures. Therefore, investigating and refining prompt strategies can enhance the effectiveness of LLMs in automated language assessments. Second, it is crucial to explore the application of LLMs in second-language assessment and learning for oral proficiency, as well as their potential in under-resourced languages. Recent advancements in self-supervised machine learning techniques have significantly improved automatic speech recognition (ASR) systems, opening up new possibilities for creating reliable ASR systems, particularly for under-resourced languages with limited data. However, challenges persist in the field of ASR. First, ASR assumes correct word pronunciation for automatic pronunciation evaluation, which proves challenging for learners in the early stages of language acquisition due to diverse accents influenced by their native languages. Accurately segmenting short words becomes problematic in such cases. Second, developing precise audio-text transcriptions for languages with non-native accented speech poses a formidable task. Last, assessing oral proficiency levels involves capturing various linguistic features, including fluency, pronunciation, accuracy, and complexity, which are not easily captured by current NLP technology.

Data availability

The dataset utilized was obtained from the International Corpus of Japanese as a Second Language (I-JAS). The data URLs: [ https://www2.ninjal.ac.jp/jll/lsaj/ihome2.html ].

J-CAT and TTBJ are two computerized adaptive tests used to assess Japanese language proficiency.

SPOT is a specific component of the TTBJ test.

J-CAT: https://www.j-cat2.org/html/ja/pages/interpret.html

SPOT: https://ttbj.cegloc.tsukuba.ac.jp/p1.html#SPOT .

The study utilized a prompt-based GPT-4 model, developed by OpenAI, which has an impressive architecture with 1.8 trillion parameters across 120 layers. GPT-4 was trained on a vast dataset of 13 trillion tokens, using two stages: initial training on internet text datasets to predict the next token, and subsequent fine-tuning through reinforcement learning from human feedback.

https://www2.ninjal.ac.jp/jll/lsaj/ihome2-en.html .

http://jhlee.sakura.ne.jp/JEV/ by Japanese Learning Dictionary Support Group 2015.

We express our sincere gratitude to the reviewer for bringing this matter to our attention.

On February 7, 2023, Microsoft began rolling out a major overhaul to Bing that included a new chatbot feature based on OpenAI’s GPT-4 (Bing.com).

Appendix E-F present the analysis results of the QWK coefficient between the scores computed by the human raters and the BERT, OCLL models.

Attali Y, Burstein J (2006) Automated essay scoring with e-rater® V.2. J. Technol., Learn. Assess., 4

Barkaoui K, Hadidi A (2020) Assessing Change in English Second Language Writing Performance (1st ed.). Routledge, New York. https://doi.org/10.4324/9781003092346

Bentz C, Tatyana R, Koplenig A, Tanja S (2016) A comparison between morphological complexity. measures: Typological data vs. language corpora. In Proceedings of the workshop on computational linguistics for linguistic complexity (CL4LC), 142–153. Osaka, Japan: The COLING 2016 Organizing Committee

Bond TG, Yan Z, Heene M (2021) Applying the Rasch model: Fundamental measurement in the human sciences (4th ed). Routledge

Brants T (2000) Inter-annotator agreement for a German newspaper corpus. Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00), Athens, Greece, 31 May-2 June, European Language Resources Association

Brown TB, Mann B, Ryder N, et al. (2020) Language models are few-shot learners. Advances in Neural Information Processing Systems, Online, 6–12 December, Curran Associates, Inc., Red Hook, NY

Burstein J (2003) The E-rater scoring engine: Automated essay scoring with natural language processing. In Shermis MD and Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Čech R, Miroslav K (2018) Morphological richness of text. In Masako F, Václav C (ed) Taming the corpus: From inflection and lexis to interpretation, 63–77. Cham, Switzerland: Springer Nature

Çöltekin Ç, Taraka, R (2018) Exploiting Universal Dependencies treebanks for measuring morphosyntactic complexity. In Aleksandrs B, Christian B (ed), Proceedings of first workshop on measuring language complexity, 1–7. Torun, Poland

Crossley SA, Cobb T, McNamara DS (2013) Comparing count-based and band-based indices of word frequency: Implications for active vocabulary research and pedagogical applications. System 41:965–981. https://doi.org/10.1016/j.system.2013.08.002

Article   Google Scholar  

Crossley SA, McNamara DS (2016) Say more and be more coherent: How text elaboration and cohesion can increase writing quality. J. Writ. Res. 7:351–370

CyberAgent Inc (2023) Open-Calm series of Japanese language models. Retrieved from: https://www.cyberagent.co.jp/news/detail/id=28817

Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota, 2–7 June, pp. 4171–4186. Association for Computational Linguistics

Diez-Ortega M, Kyle K (2023) Measuring the development of lexical richness of L2 Spanish: a longitudinal learner corpus study. Studies in Second Language Acquisition 1-31

Eckes T (2009) On common ground? How raters perceive scoring criteria in oral proficiency testing. In Brown A, Hill K (ed) Language testing and evaluation 13: Tasks and criteria in performance assessment (pp. 43–73). Peter Lang Publishing

Elliot S (2003) IntelliMetric: from here to validity. In: Shermis MD, Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Google Scholar  

Engber CA (1995) The relationship of lexical proficiency to the quality of ESL compositions. J. Second Lang. Writ. 4:139–155

Garner J, Crossley SA, Kyle K (2019) N-gram measures and L2 writing proficiency. System 80:176–187. https://doi.org/10.1016/j.system.2018.12.001

Haberman SJ (2008) When can subscores have value? J. Educat. Behav. Stat., 33:204–229

Haberman SJ, Yao L, Sinharay S (2015) Prediction of true test scores from observed item scores and ancillary data. Brit. J. Math. Stat. Psychol. 68:363–385

Halliday MAK (1985) Spoken and Written Language. Deakin University Press, Melbourne, Australia

Hirao R, Arai M, Shimanaka H et al. (2020) Automated essay scoring system for nonnative Japanese learners. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 1250–1257. European Language Resources Association

Hunt KW (1966) Recent Measures in Syntactic Development. Elementary English, 43(7), 732–739. http://www.jstor.org/stable/41386067

Ishioka T (2001) About e-rater, a computer-based automatic scoring system for essays [Konpyūta ni yoru essei no jidō saiten shisutemu e − rater ni tsuite]. University Entrance Examination. Forum [Daigaku nyūshi fōramu] 24:71–76

Hochreiter S, Schmidhuber J (1997) Long short- term memory. Neural Comput. 9(8):1735–1780

Article   CAS   PubMed   Google Scholar  

Ishioka T, Kameda M (2006) Automated Japanese essay scoring system based on articles written by experts. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 17–18 July 2006, pp. 233-240. Association for Computational Linguistics, USA

Japan Foundation (2021) Retrieved from: https://www.jpf.gp.jp/j/project/japanese/survey/result/dl/survey2021/all.pdf

Jarvis S (2013a) Defining and measuring lexical diversity. In Jarvis S, Daller M (ed) Vocabulary knowledge: Human ratings and automated measures (Vol. 47, pp. 13–44). John Benjamins. https://doi.org/10.1075/sibil.47.03ch1

Jarvis S (2013b) Capturing the diversity in lexical diversity. Lang. Learn. 63:87–106. https://doi.org/10.1111/j.1467-9922.2012.00739.x

Jiang J, Quyang J, Liu H (2019) Interlanguage: A perspective of quantitative linguistic typology. Lang. Sci. 74:85–97

Kim M, Crossley SA, Kyle K (2018) Lexical sophistication as a multidimensional phenomenon: Relations to second language lexical proficiency, development, and writing quality. Mod. Lang. J. 102(1):120–141. https://doi.org/10.1111/modl.12447

Kojima T, Gu S, Reid M et al. (2022) Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, New Orleans, LA, 29 November-1 December, Curran Associates, Inc., Red Hook, NY

Kyle K, Crossley SA (2015) Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Q 49:757–786

Kyle K, Crossley SA, Berger CM (2018) The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behav. Res. Methods 50:1030–1046. https://doi.org/10.3758/s13428-017-0924-4

Article   PubMed   Google Scholar  

Kyle K, Crossley SA, Jarvis S (2021) Assessing the validity of lexical diversity using direct judgements. Lang. Assess. Q. 18:154–170. https://doi.org/10.1080/15434303.2020.1844205

Landauer TK, Laham D, Foltz PW (2003) Automated essay scoring and annotation of essays with the Intelligent Essay Assessor. In Shermis MD, Burstein JC (ed), Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159–174

Laufer B, Nation P (1995) Vocabulary size and use: Lexical richness in L2 written production. Appl. Linguist. 16:307–322. https://doi.org/10.1093/applin/16.3.307

Lee J, Hasebe Y (2017) jWriter Learner Text Evaluator, URL: https://jreadability.net/jwriter/

Lee J, Kobayashi N, Sakai T, Sakota K (2015) A Comparison of SPOT and J-CAT Based on Test Analysis [Tesuto bunseki ni motozuku ‘SPOT’ to ‘J-CAT’ no hikaku]. Research on the Acquisition of Second Language Japanese [Dainigengo to shite no nihongo no shūtoku kenkyū] (18) 53–69

Li W, Yan J (2021) Probability distribution of dependency distance based on a Treebank of. Japanese EFL Learners’ Interlanguage. J. Quant. Linguist. 28(2):172–186. https://doi.org/10.1080/09296174.2020.1754611

Article   MathSciNet   Google Scholar  

Linacre JM (2002) Optimizing rating scale category effectiveness. J. Appl. Meas. 3(1):85–106

PubMed   Google Scholar  

Linacre JM (1994) Constructing measurement with a Many-Facet Rasch Model. In Wilson M (ed) Objective measurement: Theory into practice, Volume 2 (pp. 129–144). Norwood, NJ: Ablex

Liu H (2008) Dependency distance as a metric of language comprehension difficulty. J. Cognitive Sci. 9:159–191

Liu H, Xu C, Liang J (2017) Dependency distance: A new perspective on syntactic patterns in natural languages. Phys. Life Rev. 21. https://doi.org/10.1016/j.plrev.2017.03.002

Loukina A, Madnani N, Cahill A, et al. (2020) Using PRMSE to evaluate automated scoring systems in the presence of label noise. Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, Seattle, WA, USA → Online, 10 July, pp. 18–29. Association for Computational Linguistics

Lu X (2010) Automatic analysis of syntactic complexity in second language writing. Int. J. Corpus Linguist. 15:474–496

Lu X (2012) The relationship of lexical richness to the quality of ESL learners’ oral narratives. Mod. Lang. J. 96:190–208

Lu X (2017) Automated measurement of syntactic complexity in corpus-based L2 writing research and implications for writing assessment. Lang. Test. 34:493–511

Lu X, Hu R (2022) Sense-aware lexical sophistication indices and their relationship to second language writing quality. Behav. Res. Method. 54:1444–1460. https://doi.org/10.3758/s13428-021-01675-6

Ministry of Health, Labor, and Welfare of Japan (2022) Retrieved from: https://www.mhlw.go.jp/stf/newpage_30367.html

Mizumoto A, Eguchi M (2023) Exploring the potential of using an AI language model for automated essay scoring. Res. Methods Appl. Linguist. 3:100050

Okgetheng B, Takeuchi K (2024) Estimating Japanese Essay Grading Scores with Large Language Models. Proceedings of 30th Annual Conference of the Language Processing Society in Japan, March 2024

Ortega L (2015) Second language learning explained? SLA across 10 contemporary theories. In VanPatten B, Williams J (ed) Theories in Second Language Acquisition: An Introduction

Rae JW, Borgeaud S, Cai T, et al. (2021) Scaling Language Models: Methods, Analysis & Insights from Training Gopher. ArXiv, abs/2112.11446

Read J (2000) Assessing vocabulary. Cambridge University Press. https://doi.org/10.1017/CBO9780511732942

Rudner LM, Liang T (2002) Automated Essay Scoring Using Bayes’ Theorem. J. Technol., Learning and Assessment, 1 (2)

Sakoda K, Hosoi Y (2020) Accuracy and complexity of Japanese Language usage by SLA learners in different learning environments based on the analysis of I-JAS, a learners’ corpus of Japanese as L2. Math. Linguist. 32(7):403–418. https://doi.org/10.24701/mathling.32.7_403

Suzuki N (1999) Summary of survey results regarding comprehensive essay questions. Final report of “Joint Research on Comprehensive Examinations for the Aim of Evaluating Applicability to Each Specialized Field of Universities” for 1996-2000 [shōronbun sōgō mondai ni kansuru chōsa kekka no gaiyō. Heisei 8 - Heisei 12-nendo daigaku no kaku senmon bun’ya e no tekisei no hyōka o mokuteki to suru sōgō shiken no arikata ni kansuru kyōdō kenkyū’ saishū hōkoku-sho]. University Entrance Examination Section Center Research and Development Department [Daigaku nyūshi sentā kenkyū kaihatsubu], 21–32

Taghipour K, Ng HT (2016) A neural approach to automated essay scoring. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 1–5 November, pp. 1882–1891. Association for Computational Linguistics

Takeuchi K, Ohno M, Motojin K, Taguchi M, Inada Y, Iizuka M, Abo T, Ueda H (2021) Development of essay scoring methods based on reference texts with construction of research-available Japanese essay data. In IPSJ J 62(9):1586–1604

Ure J (1971) Lexical density: A computational technique and some findings. In Coultard M (ed) Talking about Text. English Language Research, University of Birmingham, Birmingham, England

Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. In Advances in Neural Information Processing Systems, Long Beach, CA, 4–7 December, pp. 5998–6008, Curran Associates, Inc., Red Hook, NY

Watanabe H, Taira Y, Inoue Y (1988) Analysis of essay evaluation data [Shōronbun hyōka dēta no kaiseki]. Bulletin of the Faculty of Education, University of Tokyo [Tōkyōdaigaku kyōiku gakubu kiyō], Vol. 28, 143–164

Yao S, Yu D, Zhao J, et al. (2023) Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36

Zenker F, Kyle K (2021) Investigating minimum text lengths for lexical diversity indices. Assess. Writ. 47:100505. https://doi.org/10.1016/j.asw.2020.100505

Zhang Y, Warstadt A, Li X, et al. (2021) When do you need billions of words of pretraining data? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online, pp. 1112-1125. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.90

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This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.

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Wenchao Li is in charge of conceptualization, validation, formal analysis, investigation, data curation, visualization and writing the draft. Haitao Liu is in charge of supervision.

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Li, W., Liu, H. Applying large language models for automated essay scoring for non-native Japanese. Humanit Soc Sci Commun 11 , 723 (2024). https://doi.org/10.1057/s41599-024-03209-9

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Received : 02 February 2024

Accepted : 16 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1057/s41599-024-03209-9

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