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Khan Academy Blog

Introducing Khanmigo’s New Academic Essay Feedback Tool

posted on November 29, 2023

By Sarah Robertson , senior product manager at Khan Academy

Khan Academy has always been about leveraging technology to deliver world-class educational experiences to students everywhere. We think the newest AI-powered feature in our Khanmigo pilot—our Academic Essay Feedback tool—is a groundbreaking step toward revolutionizing how students improve their writing skills.

The reality of writing instruction

Here’s a word problem for you: A ninth-grade English teacher assigns a two-page essay to 100 students. If she limits herself to spending 10 minutes per essay providing personalized, detailed feedback on each draft, how many hours will it take her to finish reviewing all 100 essays?

The answer is that it would take her nearly 17 hours —and that’s just for the first draft!

Research tells us that the most effective methods of improving student writing skills require feedback to be focused, actionable, aligned to clear objectives, and delivered often and in a timely manner . 

The unfortunate reality is that teachers are unable to provide this level of feedback to students as often as students need it—and they need it now more than ever. Only 25% of eighth and twelfth graders are proficient in writing, according to the most recent NAEP scores .

An AI writing tutor for every student

Developed by experts in English Language Arts (ELA) and writing instruction, the pilot Khanmigo Academic Essay Feedback tool uses AI to offer students specific, immediate, and actionable feedback on their argumentative, expository, or literary analysis essays. 

Unlike other AI-powered writing tools, the Academic Essay Feedback tool isn’t limited to giving feedback on sentence- or language-level issues alone, like grammar or spelling. Instead, it provides feedback on areas like essay structure and organization, how well students support their arguments, introduction and conclusion, and style and tone.

The tool also doesn’t just stop at providing feedback, it also guides students through the revision process. Students can view highlighted feedback, ask clarifying questions, see exemplar writing, make revisions, and ask for further review—without the AI doing any actual writing for them.

Unique features of Khanmigo pilot Academic Essay Feedback tool

• Immediate, personalized feedback: within seconds, students get detailed, actionable, grade-level-appropriate feedback (both praise and constructive) that is personalized to their specific writing assignment and tied directly to interactive highlights in their essay.
• Comprehensive approach: feedback covers a wide range of writing skills, from crafting an engaging yet focused introduction and thesis, to overall essay structure and organization, to style and tone, to alignment and use of evidence.
• Interactive revision process: students can interact with Khanmigo to ask questions about specific pieces of feedback, get examples of model writing, make immediate revisions based on the feedback, and see if their revisions addressed the suggestion.
• Support for various essay types: the tool is versatile and assists with multi-paragraph persuasive, argumentative, explanatory, and literary analysis essay assignments for grades 8-12 (and more, coming soon).
• Focus on instruction and growth: like all Khanmigo features, the Academic Essay Feedback tool will not do the work for the student. Teachers and parents can rest assured that Khanmigo is there to improve the students’ independent writing skills, not provide one-click suggested revisions.

How parents can use Khanmigo’s Academic Essay Feedback tool

Any student with Khanmigo access can find the feedback tool under the “Write” category on their AI Activities menu. 

For academic essays, students should simply paste their first draft into the essay field, select their grade level and essay type, and provide the essay instructions from the teacher.

Students then click “Submit” and feedback begins generating. Once Khanmigo is done generating feedback, students can work their way through the suggestions for each category, chat with Khanmigo for help, make revisions, and resolve feedback. They can then submit their second draft for another round of feedback, or copy the final draft to submit to their teacher.

Bringing Khanmigo to your classroom, school, or district

Teachers in Khan Academy Districts partnerships can begin using the Khanmigo Academic Essay Feedback tool with their students right away. Simply direct students to the feedback tool under the “Write” category on their AI Activities menu.

Like all other Khanmigo activities, students’ interactions are monitored and moderated for safety. Teachers or parents can view the student’s initial draft, AI-generated feedback, chat history, and final draft in the student’s chat history. If anything is flagged for moderation, teachers or parents will receive an email notification.

Looking ahead

With the Academic Essay Feedback tool in our Khanmigo pilot, teachers and parents can empower students to take charge of their writing.The tool helps facilitate a deeper understanding of effective writing techniques and encourages self-improvement. For teachers, we think this tool is a valuable ally, enabling them to provide more frequent, timely, detailed, and actionable feedback for students on multiple drafts.

In the coming months, we’ll be launching exciting improvements to the tool and even more writing resources for learners, parents, teachers, and administrators:

• The ability for teachers to create an essay-revision assignment for their students on Khan Academy
• More varied feedback areas and flexibility in what feedback is given
• Support for students in essay outlining and drafting
• Insights for teachers and parents into their students’ full writing process

Stay tuned!

Sarah Robertson is a senior product manager at Khan Academy. She has a M.Ed. in Curriculum and Instruction and over a decade of experience teaching English, developing curriculum, and creating software products that have helped tens of millions of students improve their reading and writing skills.

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The world’s leading AI platform for teachers to grade essays

EssayGrader is an AI powered grading assistant that gives high quality, specific and accurate writing feedback for essays. On average it takes a teacher 10 minutes to grade a single essay, with EssayGrader that time is cut down to 30 seconds. That's a 95% reduction in the time it takes to grade an essay, with the same results.

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EssayGrader analyzes essays with the power of AI. Our software is trained on massive amounts of diverse text data, including books, articles and websites. This gives us the ability to provide accurate and detailed writing feedback to students and save teachers loads of time. We are the perfect AI powered grading assistant.

EssayGrader analyzes essays for grammar, punctuation, spelling, coherence, clarity and writing style errors. We provide detailed reports of the errors found and suggestions on how to fix those errors. Our error reports help speed up grading times by quickly highlighting mistakes made in the essay.

Bulk uploading

Uploading a single essay at a time, then waiting for it to complete is a pain. Bulk uploading allows you to upload an entire class worth of essays at a single time. You can work on other important tasks, come back in a few minutes to see all the essays perfectly graded.

Custom rubrics

We don't assume how you want to grade your essays. Instead, we provide you with the ability to create the same rubrics you already use. Those rubrics are then used to grade essays with the same grading criteria you are already accustomed to.

Sometimes you don't want to read a 5000 word essay and you'd just like a quick summary. Or maybe you're a student that needs to provide a summary of your essay to your teacher. We can help with our summarizer feature. We can provide a concise summary including the most important information and unique phrases.

AI detector

Our AI detector feature allows teachers to identify if an essay was written by AI or if only parts of it were written by AI. AI is becoming very popular and teachers need to be able to detect if essays are being written by students or AI.

Create classes to neatly organize your students essays. This is an essential feature when you have multiple classes and need to be able to track down students essays quickly.

Our mission

At EssayGrader, our mission is crystal clear: we're transforming the grading experience for teachers and students alike. Picture a space where teachers can efficiently and accurately grade essays, lightening their workload, while empowering students to enhance their writing skills. Our software is a dynamic work in progress, a testament to our commitment to constant improvement. We're dedicated to refining and enhancing our platform continually. With each update, we strive to simplify the lives of both educators and learners, making the process of grading and writing essays smoother and more efficient.We recognize the immense challenges teachers face – the heavy burdens, the long hours, and the often underappreciated efforts. EssayGrader is our way of shouldering some of that load. We are here to support you, to make your tasks more manageable, and to give you the tools you need to excel in your teaching journey.

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PROOF POINTS: AI writing feedback ‘better than I thought,’ top researcher says

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This week I challenged my editor to face off against a machine. Barbara Kantrowitz gamely accepted, under one condition: “You have to file early.”  Ever since ChatGPT arrived in 2022, many journalists have made a public stunt out of asking the new generation of artificial intelligence to write their stories. Those AI stories were often bland and sprinkled with errors. I wanted to understand how well ChatGPT handled a different aspect of writing: giving feedback.

My curiosity was piqued by a new study , published in the June 2024 issue of the peer-reviewed journal Learning and Instruction, that evaluated the quality of ChatGPT’s feedback on students’ writing. A team of researchers compared AI with human feedback on 200 history essays written by students in grades 6 through 12 and they determined that human feedback was generally a bit better. Humans had a particular advantage in advising students on something to work on that would be appropriate for where they are in their development as a writer. 

But ChatGPT came close. On a five-point scale that the researchers used to rate feedback quality, with a 5 being the highest quality feedback, ChatGPT averaged a 3.6 compared with a 4.0 average from a team of 16 expert human evaluators. It was a tough challenge. Most of these humans had taught writing for more than 15 years or they had considerable experience in writing instruction. All received three hours of training for this exercise plus extra pay for providing the feedback.

ChatGPT even beat these experts in one aspect; it was slightly better at giving feedback on students’ reasoning, argumentation and use of evidence from source materials – the features that the researchers had wanted the writing evaluators to focus on.

“It was better than I thought it was going to be because I didn’t have a lot of hope that it was going to be that good,” said Steve Graham, a well-regarded expert on writing instruction at Arizona State University, and a member of the study’s research team. “It wasn’t always accurate. But sometimes it was right on the money. And I think we’ll learn how to make it better.”

Average ratings for the quality of ChatGPT and human feedback on 200 student essays

Exactly how ChatGPT is able to give good feedback is something of a black box even to the writing researchers who conducted this study. Artificial intelligence doesn’t comprehend things in the same way that humans do. But somehow, through the neural networks that ChatGPT’s programmers built, it is picking up on patterns from all the writing it has previously digested, and it is able to apply those patterns to a new text. 

The surprising “relatively high quality” of ChatGPT’s feedback is important because it means that the new artificial intelligence of large language models, also known as generative AI, could potentially help students improve their writing. One of the biggest problems in writing instruction in U.S. schools is that teachers assign too little writing, Graham said, often because teachers feel that they don’t have the time to give personalized feedback to each student. That leaves students without sufficient practice to become good writers. In theory, teachers might be willing to assign more writing or insist on revisions for each paper if students (or teachers) could use ChatGPT to provide feedback between drafts. 

Despite the potential, Graham isn’t an enthusiastic cheerleader for AI. “My biggest fear is that it becomes the writer,” he said. He worries that students will not limit their use of ChatGPT to helpful feedback, but ask it to do their thinking, analyzing and writing for them. That’s not good for learning. The research team also worries that writing instruction will suffer if teachers delegate too much feedback to ChatGPT. Seeing students’ incremental progress and common mistakes remain important for deciding what to teach next, the researchers said. For example, seeing loads of run-on sentences in your students’ papers might prompt a lesson on how to break them up. But if you don’t see them, you might not think to teach it. Another common concern among writing instructors is that AI feedback will steer everyone to write in the same homogenized way. A young writer’s unique voice could be flattened out before it even has the chance to develop.

There’s also the risk that students may not be interested in heeding AI feedback. Students often ignore the painstaking feedback that their teachers already give on their essays. Why should we think students will pay attention to feedback if they start getting more of it from a machine? 

Still, Graham and his research colleagues at the University of California, Irvine, are continuing to study how AI could be used effectively and whether it ultimately improves students’ writing. “You can’t ignore it,” said Graham. “We either learn to live with it in useful ways, or we’re going to be very unhappy with it.”

Right now, the researchers are studying how students might converse back-and-forth with ChatGPT like a writing coach in order to understand the feedback and decide which suggestions to use.

Example of feedback from a human and ChatGPT on the same essay

In the current study, the researchers didn’t track whether students understood or employed the feedback, but only sought to measure its quality. Judging the quality of feedback is a rather subjective exercise, just as feedback itself is a bundle of subjective judgment calls. Smart people can disagree on what good writing looks like and how to revise bad writing. 

In this case, the research team came up with its own criteria for what constitutes good feedback on a history essay. They instructed the humans to focus on the student’s reasoning and argumentation, rather than, say, grammar and punctuation.  They also told the human raters to adopt a “glow and grow strategy” for delivering the feedback by first finding something to praise, then identifying a particular area for improvement. 

The human raters provided this kind of feedback on hundreds of history essays from 2021 to 2023, as part of an unrelated study of an initiative to boost writing at school . The researchers randomly grabbed 200 of these essays and fed the raw student writing – without the human feedback – to version 3.5 of ChatGPT and asked it to give feedback , too . 

At first, the AI feedback was terrible, but as the researchers tinkered with the instructions, or the “prompt,” they typed into ChatGPT, the feedback improved. The researchers eventually settled upon this wording: “Pretend you are a secondary school teacher. Provide 2-3 pieces of specific, actionable feedback on each of the following essays…. Use a friendly and encouraging tone.” The researchers also fed the assignment that the students were given, for example, “Why did the Montgomery Bus Boycott succeed?” along with the reading source material that the students were provided. (More details about how the researchers prompted ChatGPT are explained in Appendix C of the study .)

The humans took about 20 to 25 minutes per essay. ChatGPT’s feedback came back instantly. The humans sometimes marked up sentences by, for example, showing a place where the student could have cited a source to buttress an argument. ChatGPT didn’t write any in-line comments and only wrote a note to the student. 

Researchers then read through both sets of feedback – human and machine – for each essay, comparing and rating them. (It was supposed to be a blind comparison test and the feedback raters were not told who authored each one. However, the language and tone of ChatGPT were distinct giveaways, and the in-line comments were a tell of human feedback.)

Humans appeared to have a clear edge with the very strongest and the very weakest writers, the researchers found. They were better at pushing a strong writer a little bit further, for example, by suggesting that the student consider and address a counterargument. ChatGPT struggled to come up with ideas for a student who was already meeting the objectives of a well-argued essay with evidence from the reading source materials. ChatGPT also struggled with the weakest writers. The researchers had to drop two of the essays from the study because they were so short that ChatGPT didn’t have any feedback for the student. The human rater was able to parse out some meaning from a brief, incomplete sentence and offer a suggestion. 

In one student essay about the Montgomery Bus Boycott, reprinted above, the human feedback seemed too generic to me: “Next time, I would love to see some evidence from the sources to help back up your claim.” ChatGPT, by contrast, specifically suggested that the student could have mentioned how much revenue the bus company lost during the boycott – an idea that was mentioned in the student’s essay. ChatGPT also suggested that the student could have mentioned specific actions that the NAACP and other organizations took. But the student had actually mentioned a few of these specific actions in his essay. That part of ChatGPT’s feedback was plainly inaccurate. 

In another student writing example, also reprinted below, the human straightforwardly pointed out that the student had gotten an historical fact wrong. ChatGPT appeared to affirm that the student’s mistaken version of events was correct.

Another example of feedback from a human and ChatGPT on the same essay

So how did ChatGPT’s review of my first draft stack up against my editor’s? One of the researchers on the study team suggested a prompt that I could paste into ChatGPT. After a few back and forth questions with the chatbot about my grade level and intended audience, it initially spit out some generic advice that had little connection to the ideas and words of my story. It seemed more interested in format and presentation, suggesting a summary at the top and subheads to organize the body. One suggestion would have made my piece too long-winded. Its advice to add examples of how AI feedback might be beneficial was something that I had already done. I then asked for specific things to change in my draft, and ChatGPT came back with some great subhead ideas. I plan to use them in my newsletter, which you can see if you sign up for it here . (And if you want to see my prompt and dialogue with ChatGPT, here is the link .) 

My human editor, Barbara, was the clear winner in this round. She tightened up my writing, fixed style errors and helped me brainstorm this ending. Barbara’s job is safe – for now. 

This story about AI feedback   was written by Jill Barshay and produced by  The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for  Proof Points   and other  Hechinger newsletters .

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One key point is that humans took 20-25 minutes per essay while AI was instant. I estimate I can read AI’s feedback, comment on it, and improve it in about five minutes per essay, meaning I could give four to five times as much feedback in the same amount of time. Likely, AI + me will create better feedback than either of us alone. Given the number of students I teach, I could only give the same amount of human attention that the humans in this experiment gave were my district to give me an entire free week to read and score. (120 students X 20 minutes = 2,400 minutes = 40 hours) That’s not likely, plus it would take time away from vital in-class connections.

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The Potential of AI Feedback to Improve Student Writing

This week I challenged my editor to face off against a machine. Barbara Kantrowitz gamely accepted, under one condition: “You have to file early.” Ever since ChatGPT arrived in 2022, many journalists have made a public stunt out of asking the new generation of artificial intelligence to write their stories. Those AI stories were often bland and sprinkled with errors. I wanted to understand how well ChatGPT handled a different aspect of writing: giving feedback

My curiosity was piqued by a new  study , published in the June 2024 issue of the peer-reviewed journal Learning and Instruction, that evaluated the quality of ChatGPT’s feedback on students’ writing. A team of researchers compared AI with human feedback on 200 history essays written by students in grades 6 through 12 and they determined that human feedback was generally a bit better. Humans had a particular advantage in advising students on something to work on that would be appropriate for where they are in their development as a writer.

But ChatGPT came close. On a five-point scale that the researchers used to rate feedback quality, with a 5 being the highest quality feedback, ChatGPT averaged a 3.6 compared with a 4.0 average from a team of 16 expert human evaluators. It was a tough challenge. Most of these humans had taught writing for more than 15 years or they had considerable experience in writing instruction. All received three hours of training for this exercise plus extra pay for providing the feedback.

ChatGPT even beat these experts in one aspect; it was slightly better at giving feedback on students’ reasoning, argumentation and use of evidence from source materials – the features that the researchers had wanted the writing evaluators to focus on.

“It was better than I thought it was going to be because I didn’t have a lot of hope that it was going to be that good,” said Steve Graham, a well-regarded expert on writing instruction at Arizona State University, and a member of the study’s research team. “It wasn’t always accurate. But sometimes it was right on the money. And I think we’ll learn how to make it better.”

Exactly how ChatGPT is able to give good feedback is something of a black box even to the writing researchers who conducted this study. Artificial intelligence doesn’t comprehend things in the same way that humans do. But somehow, through the  neural networks  that ChatGPT’s programmers built, it is picking up on patterns from all the writing it has previously digested, and it is able to apply those patterns to a new text.

The surprising “relatively high quality” of ChatGPT’s feedback is important because it means that the new artificial intelligence of large language models, also known as generative AI, could potentially help students improve their writing. One of the biggest problems in writing instruction in U.S. schools is that teachers assign too little writing, Graham said, often because teachers feel that they don’t have the time to give personalized feedback to each student. That leaves students without sufficient practice to become good writers. In theory, teachers might be willing to assign more writing or insist on revisions for each paper if students (or teachers) could use ChatGPT to provide feedback between drafts.

Despite the potential, Graham isn’t an enthusiastic cheerleader for AI. “My biggest fear is that it becomes the writer,” he said. He worries that students will not limit their use of ChatGPT to helpful feedback, but ask it to do their thinking, analyzing and writing for them. That’s not good for learning. The research team also worries that writing instruction will suffer if teachers delegate too much feedback to ChatGPT. Seeing students’ incremental progress and common mistakes remain important for deciding what to teach next, the researchers said. For example, seeing loads of run-on sentences in your students’ papers might prompt a lesson on how to break them up. But if you don’t see them, you might not think to teach it. Another common concern among writing instructors is that AI feedback will steer everyone to write in the same homogenized way. A young writer’s unique voice could be flattened out before it even has the chance to develop.

There’s also the risk that students may not be interested in heeding AI feedback. Students often ignore the painstaking feedback that their teachers already give on their essays. Why should we think students will pay attention to feedback if they start getting more of it from a machine?

Still, Graham and his research colleagues at the University of California, Irvine, are continuing to study how AI could be used effectively and whether it ultimately improves students’ writing. “You can’t ignore it,” said Graham. “We either learn to live with it in useful ways, or we’re going to be very unhappy with it.”

Right now, the researchers are studying how students might  converse back-and-forth with ChatGPT like a writing coach  in order to understand the feedback and decide which suggestions to use.

In the current study, the researchers didn’t track whether students understood or employed the feedback, but only sought to measure its quality. Judging the quality of feedback is a rather subjective exercise, just as feedback itself is a bundle of subjective judgment calls. Smart people can disagree on what good writing looks like and how to revise bad writing.

In this case, the research team came up with its own criteria for what constitutes good feedback on a history essay. They instructed the humans to focus on the student’s reasoning and argumentation, rather than, say, grammar and punctuation.  They also told the human raters to adopt a “glow and grow strategy” for delivering the feedback by first finding something to praise, then identifying a particular area for improvement.

The human raters provided this kind of feedback on hundreds of history essays from 2021 to 2023, as part of an unrelated study of an  initiative to boost writing at school . The researchers randomly grabbed 200 of these essays and fed the raw student writing – without the human feedback – to version 3.5 of ChatGPT and asked it to give feedback, too.

At first, the AI feedback was terrible, but as the researchers tinkered with the instructions, or the “prompt,” they typed into ChatGPT, the feedback improved. The researchers eventually settled upon this wording: “Pretend you are a secondary school teacher. Provide 2-3 pieces of specific, actionable feedback on each of the following essays…. Use a friendly and encouraging tone.” The researchers also fed the assignment that the students were given, for example, “Why did the Montgomery Bus Boycott succeed?” along with the reading source material that the students were provided. (More details about how the researchers prompted ChatGPT are explained in  Appendix C of the study .)

The humans took about 20 to 25 minutes per essay. ChatGPT’s feedback came back instantly. The humans sometimes marked up sentences by, for example, showing a place where the student could have cited a source to buttress an argument. ChatGPT didn’t write any in-line comments and only wrote a note to the student.

Researchers then read through both sets of feedback – human and machine – for each essay, comparing and rating them. (It was supposed to be a blind comparison test and the feedback raters were not told who authored each one. However, the language and tone of ChatGPT were distinct giveaways, and the in-line comments were a tell of human feedback.)

Humans appeared to have a clear edge with the very strongest and the very weakest writers, the researchers found. They were better at pushing a strong writer a little bit further, for example, by suggesting that the student consider and address a counterargument. ChatGPT struggled to come up with ideas for a student who was already meeting the objectives of a well-argued essay with evidence from the reading source materials. ChatGPT also struggled with the weakest writers. The researchers had to drop two of the essays from the study because they were so short that ChatGPT didn’t have any feedback for the student. The human rater was able to parse out some meaning from a brief, incomplete sentence and offer a suggestion.

In one student essay about the Montgomery Bus Boycott, reprinted above, the human feedback seemed too generic to me: “Next time, I would love to see some evidence from the sources to help back up your claim.” ChatGPT, by contrast, specifically suggested that the student could have mentioned how much revenue the bus company lost during the boycott – an idea that was mentioned in the student’s essay. ChatGPT also suggested that the student could have mentioned specific actions that the NAACP and other organizations took. But the student had actually mentioned a few of these specific actions in his essay. That part of ChatGPT’s feedback was plainly inaccurate.

In another student writing example, also reprinted below, the human straightforwardly pointed out that the student had gotten an historical fact wrong. ChatGPT appeared to affirm that the student’s mistaken version of events was correct.

So how did ChatGPT’s review of my first draft stack up against my editor’s? One of the researchers on the study team suggested a prompt that I could paste into ChatGPT. After a few back and forth questions with the chatbot about my grade level and intended audience, it initially spit out some generic advice that had little connection to the ideas and words of my story. It seemed more interested in format and presentation, suggesting a summary at the top and subheads to organize the body. One suggestion would have made my piece too long-winded. Its advice to add examples of how AI feedback might be beneficial was something that I had already done. I then asked for specific things to change in my draft, and ChatGPT came back with some great subhead ideas. I plan to use them in my newsletter, which you can see if you  sign up for it here . (And if you want to see my prompt and dialogue with ChatGPT, here is the  link .)

My human editor, Barbara, was the clear winner in this round. She tightened up my writing, fixed style errors and helped me brainstorm this ending. Barbara’s job is safe – for now.

Jill Barshay is a senior reporter at The Hechinger Report, where she writes the weekly “Proof Points” column about education research and data. This column was initially published by The Hechinger Report .

Published: June 3, 2024

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• Research article
• Open access
• Published: 27 October 2023

AI-generated feedback on writing: insights into efficacy and ENL student preference

• Juan Escalante   ORCID: orcid.org/0009-0009-8534-2504 1 ,
• Austin Pack 1 &
• Alex Barrett 2  

International Journal of Educational Technology in Higher Education volume  20 , Article number:  57 ( 2023 ) Cite this article

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The question of how generative AI tools, such as large language models and chatbots, can be leveraged ethically and effectively in education is ongoing. Given the critical role that writing plays in learning and assessment within educational institutions, it is of growing importance for educators to make thoughtful and informed decisions as to how and in what capacity generative AI tools should be leveraged to assist in the development of students’ writing skills. This paper reports on two longitudinal studies. Study 1 examined learning outcomes of 48 university English as a new language (ENL) learners in a six-week long repeated measures quasi experimental design where the experimental group received writing feedback generated from ChatGPT (GPT-4) and the control group received feedback from their human tutor. Study 2 analyzed the perceptions of a different group of 43 ENLs who received feedback from both ChatGPT and their tutor. Results of study 1 showed no difference in learning outcomes between the two groups. Study 2 results revealed a near even split in preference for AI-generated or human-generated feedback, with clear advantages to both forms of feedback apparent from the data. The main implication of these studies is that the use of AI-generated feedback can likely be incorporated into ENL essay evaluation without affecting learning outcomes, although we recommend a blended approach that utilizes the strengths of both forms of feedback. The main contribution of this paper is in addressing generative AI as an automatic essay evaluator while incorporating learner perspectives.

Introduction

Automated writing evaluation (AWE) systems such as Grammarly and Pigai assist learners and educators in the writing process by providing corrective feedback on learner writing. These systems, and older tools such as spelling and grammar checkers, rely on natural language processing to identify errors and infelicities in writing and suggest improvements. However, with the recent unleashing of highly sophisticated generative pretrained transformer (GPT) large language models (LLMs), such as GPT-4 by OpenAI and PaLM 2 by Google , AWE may be entering a new era.

As Godwin-Jones ( 2022 ) pointed out in his treatise on AWE tools in second language writing, GPT-powered programs are capable of not only correcting errors in essays, but can also compose essays. Given a simple prompt, generative artificial intelligence (GenAI) LLMs and chatbots that allow users to interface with LLMs, such as ChatGPT and Bard , can produce complete essays that are passable at the university level (Abd-Elaal et al., 2022 ; Herbold et al., 2023 ). It is also possible for English as a new language (ENL) writers to use GPT-powered machine translation to turn their essays written in their first language (L1) into an English essay (Godwin-Jones, 2022 ) take problematic writing and correct any mistakes wholesale, change its tone from informal to academic, or add cohesive elements like discourse markers (Tate et al., 2023 ). Educators have begun to use AI-powered plagiarism detectors to identify student submissions that were generated by AI, yet AI paraphrasing programs like Quillbot have been found to render AI-generated text indetectable by such tools (Krishna et al., 2023 ). With millions of users engaging with ChatGPT and other GenAI tools since ChatGPT ’s debut in November of 2022, public discourse has speculated on the disruptive and problematic nature of these tools for the field of education (Lampropoulos et al., 2023 ).

The public reaction to GenAI in education has been diverse. In Fütterer et al.’s ( 2023 ) systematic review of popular publications across Australia, New Zealand, The U.K., and the U.S., general sentiment appeared evenly split between positive and negative, but concerns about academic integrity have been raised (Sullivan, 2023 ), with some educational institutions deciding to ban ChatGPT than to allow its use (Yang, 2023 ). The disruption GenAI represents for language education has been likened to the pocket calculator’s impact on math education (Urlaub & Dessein, 2022 ), when institutions debated between prohibiting the technology or incorporating it by rethinking the educational objectives of math education. The prevailing sentiment on GenAI seems to be that reforms are needed to adapt educational practices in accommodation of the technology (Fütterer et al., 2023 ; Tseng & Warschauer, 2023 ). However, research is urgently needed so that teachers, students, and instructional designers can appropriately apply GenAI in education (Chiu et al., 2023 ).

This article represents a step in the direction of better understanding how GenAI might be used in language learning classrooms by examining how language teachers and learners employ it in the writing process. Specifically, we will attempt to investigate the efficacy of using GPT-4 as an AWE tool for generating corrective feedback on student writing and whether students will prefer this feedback over that of a human tutor.

Overview of relevant literature

ChatGPT is a public-facing GenAI chatbot that allows users to interface with LLMs. GenAI chatbots have been trained on a large corpus of language from the Internet to statistically predict the next most probable word in response to a user prompt; these responses are then put through an algorithm of reinforcement learning (OpenAI, 2023a ). From this relatively simple premise these tools can generate, synthesize, or modify natural language to a high degree of sophistication (Elkins & Chun, 2020 ), and are rapidly becoming more sophisticated (Baktash & Dawodi, 2023 ). GenAI has proven capable at a variety of tasks including writing essays or creative texts such as poems or stories, writing or correcting computer programming code, answering questions, summarizing and paraphrasing provided text, and synthesizing disparate tones and styles to generate new and creative text. The vast capabilities and ease of use of GenAI chatbots have led to widespread concerns of the misuse of these tools by students (Yeo, 2023 ).

Educational systems currently rely on student formative and summative writing in assessment and instruction to develop and assess critical thinking, argumentation, synthesis of information, knowledge and competence, and language proficiency (Behizadeh & Engelhard, 2011 ); but the benefits of writing extend in other ways, such as learning about oneself, participating in a community, or simply to occupy free time (Florio & Clark, 1982 ). With writing being a beneficial and critical component of many educational systems, the task of reforming these systems to accommodate GenAI authoring apps seems both daunting and unappealing. Yet the historical lesson of pocket calculators shows that it is equally unappealing to prohibit the technology, or even ignore it (Urlaub & Dessein, 2022 ).

Godwin-Jones ( 2022 ) called for the “thoughtful, informed differentiation in the use and the advocacy of AI-enabled tools, based on situated practice, established goals, and desired outcomes” (p. 13). To address this involved agenda researchers and practitioners need to re-examine educational objectives which, for ENL writing instruction, includes identifying the purpose of writing in the curricula. Writing for placement or other summative writing will have a different objective than process-oriented writing, for instance. How AI-enabled tools can be integrated with these objectives remains unclear.

From a foundation of second language acquisition principles, [Ingley, 2023 ] proposed several practical ways in which GenAI might be used to improve academic writing in ENL contexts. For example, they propose questioning AI-enabled chatbots, and reflecting on output as a way of generating ideas or better understanding a topic versus simply asking the AI to brainstorm a topic for you. They also suggest that AI can help by serving in specific roles (e.g., a conference proposal reviewer, a writing teacher in a writing conference) and organize writing by drafting outlines or by providing feedback on a draft’s organization. Similarly, they propose that feedback on coherence, grammar, vocabulary, and tone can be asked of these AI-tools to help support formative essay writing. Through purposeful prompting, AI-enabled chatbots can act as a more knowledgeable other (John-Steiner & Mahn, 1996 ) that can provide comprehensible input (Krashen, 1982 ) along different stages of the writing process.

Suggestions such as these illustrate how instructors might frame acceptable and unacceptable use of AI-enabled writing tools by learners. Working in concert with AI in a creative and iterative process positions the learner as the driver in the writing process, as opposed to the learner prompting the AI to do all the thinking for them. Identifying and communicating the ethical and appropriate use of AI is an urgent task for practitioners. Since learners have increasingly relied on forms of AI in the writing process for decades, from the red or blue squiggly lines under text in word processors to recommendations on usage and style from Grammarly , they may not question using more enveloping forms of writing assistance.

From the perspective of learners, the use of AI by teachers and institutions may also need to be negotiated in terms of what is appropriate and ethical. Major exams such as the GRE and TOEFL often rely on AI-enabled AWE programs to score large numbers of essays (Elliot & Klobucar, 2013 ), as algorithmic assessment of writing reduces bias and noise and is likely more consistently accurate than the judgments of human experts (Grove et al., 2000 ). But with easily accessed AWE tools like Grammarly , and GenAI tools like ChatGPT , it is simple for any teacher to offload the responsibility of essay evaluation to automated processes (Kumar, 2023 ). Personalized learning through evaluating and giving feedback on essay writing has been identified as a potential strength of GenAI (Chiu et al., 2023 ; Farrokhnia et al., 2023 ; Zhu et al., 2023 ), which can, in turn, help decrease teacher workload (Farrokhnia et al., 2023 ) and prevent teacher burnout. However, teachers will need to make informed decisions regarding if and when to incorporate AWE by consulting learner perceptions and considering the benefit to learning.

Although Grammarly has been shown to be useful as an AWE tool (Fitria, 2021 ), it is not yet known whether ChatGPT and similar GenAI tools can effectively or reliably be used for this purpose, nor whether learners will accept feedback from these tools. Programs like Grammarly and Pigai are specifically designed for essay evaluation and scoring using latent semantic analysis, a modeling approach that relies on large corpora of essays to determine whether a student’s writing is statistically similar to writing in that corpora in terms of both mechanics and semantics (Shermis et al., 2013 ). The LLMs that ChatGPT interfaces with, on the other hand, are not trained with a corpora from a specific domain, such as essays, but with text scraped from the Internet. The domain-general nature of the LLMs behind ChatGPT means its efficacy as an AWE tool needs to be researched before being used as such.

In a recent feasibility study, Dai et al. ( 2023 ) used ChatGPT to provide corrective feedback in undergraduate writing. They found the GenAI feedback to be more readable and detailed than instructor feedback, but still maintained high agreement levels with instructor feedback on certain (but not all) aspects of student writing. Another study that examined ChatGPT for essay evaluation and feedback by Mizumoto and Eguchi ( 2023 ) fed a corpus of 12,100 essays by non-native English writers to ChatGPT and compared rubric-grounded feedback and scores to benchmark levels. Their results showed that ChatGPT was reasonably reliable and accurate. These studies suggest the feasibility and reliability of using GenAI tools like ChatGPT for the purpose of AWE, however the efficacy and student perceptions of ChatGPT AWE use needs to be better understood.

GenAI has many known and unknown limitations which need to be considered before using it as an AWE tool. One of the limitations identified by OpenAI itself is the tendency for ChatGPT to produce text that is untruthful and even malicious (OpenAI, 2023a ). ChatGPT does not function as an information retrieving program in the way that internet search engines do, for example, and only produces text that is tailored to the user prompt using the statistically best-fitting combination of words. Considering students’ tendency to accept information from AWE tools without verifying it (Koltovskaia, 2020 ), this suggests a need to teach learners to approach GenAI-produced output critically. Other relevant concerns about the use of GenAI are bias in output and privacy of user data (Derner & Batistič, 2023 ). Although OpenAI is working on solutions to these issues (OpenAI, 2023a ), safeguards are still vulnerable to certain prompting practices (Derner & Batistič, 2023 ).

The accuracy and efficacy of GenAI chatbots relies to some extent on prompt engineering (Strobelt et al., 2023 ), as well as which LLM is used (e.g., BERT, GPT-4). According to Zhou et al. ( 2023 ), prompt engineering is the practice of optimizing the language of a prompt with the intention of eliciting the best possible performance from LLMs. With prompt engineering, users can guide ChatGPT to desired behaviors by specifying things like task, context, outcome, length, format, and style.

Prompting for optimal AWE application is not yet fully explored. Mizumoto and Eguchi ( 2023 ) used a zero-shot prompting method where scoring samples were not included. Their scoring rubric was inputted in plain text format and they inserted all of their essays (n = 12,100) using a for loop in Python. Dai et al. ( 2023 ) used multi-turn prompting and pasted each essay (n = 103) at the end of each prompt. It may be within the capabilities of ChatGPT to therefore act as an AWE tool and, provided a scoring rubric or other criteria, furnish corrective feedback on student writing without fine-tuning. However, prompt engineering is a nascent science and it is not yet known whether such a practice would produce corrective feedback reliably from LLMs.

Ultimately, the success of any learning technology depends on whether users adopt it. According to Davis’s ( 1989 ) seminal paper, the primary influences on user adoption of technology are in its perceived ease of use and perceived usefulness. Huawei and Aryadoust’s ( 2023 ) review of AWE literature revealed that several studies reported that students and teachers viewed AWE scores negatively compared to scores provided by human raters. However, studies with ENL populations have noted that students often find human feedback to be confusing (Weigle, 2013 ). One advantage of LLMs is the ability to tailor the output by, for example, asking the chatbot to reiterate feedback in easier to understand terms or to explain things further. Roscoe et al.’s ( 2017 ) study of student perceptions of AWE described students’ attitudes toward the system as “cautiously positive” (p. 212) which was influenced by presenting the AWE system as helpful, student initial expectations of the system, and student direct experience with the system feedback. Given the prominence that ChatGPT has garnered, its reputation will likely precede itself in the classroom and students and teachers may not initially trust it as an AWE tool, but whether students and teachers perceive the feedback from ChatGPT as being useful has not yet been studied.

The present study

ChatGPT represents a new technology with a vast array of capabilities in natural language processing. Educators are rushing to understand how this technology can appropriately be incorporated into classrooms, which has inspired new research agendas investigating its limitations and affordances. One avenue of research that is needed is understanding how GenAI can be included in the writing process in a way that is acceptable to both students and teachers. This study intends to examine the efficacy of ChatGPT and GPT-4 as an AWE tool in terms of language improvement and student perceptions in the ENL population. Specifically, this study will be guided by the following research questions.

Does the application of AI-generated feedback result in superior linguistic progress among ENL students compared to those who receive feedback from a human tutor?

Does the preference for AI-generated feedback surpass that for human tutor-generated feedback among ENL students?

Study 1 explored the first research question by means of a six-week longitudinal mixed repeated measures quasi experimental design. Study 2 investigated the second research question through a weekly survey administered over six weeks.

Participants

Both studies were conducted at a small liberal arts university in the Asia–Pacific region during the shortened Spring 2023 semester. A non-probability self-selection method was used to recruit 91 participants who were ENL students enrolled in an academic reading and writing language course. Based on the institution’s English Language Admission Test, students were assessed as having at least a Common European Framework of Reference (CEFR) B1 English proficiency level. Standard ethical procedures were followed, with participants voluntarily consenting to participate. As part of the communication with the participants, it was explicitly stated that their involvement in the study would not be rewarded with additional academic credit.

The first research question was explored in study 1 with a total of 48 ENL students, 21 males and 27 females, ranging in age from 20 to 30 years old. They were divided into two groups: a control group (CG) that received feedback on their assignments from a human tutor, and an experimental group (EG) that was given feedback generated by AI (GPT-4).

To address the second research question, in study 2 a separate group of 43 ENL students, composed of 13 males, 30 females, whose ages ranged from 19 to 36 years, received written feedback on their weekly assignments from both AI and human tutors. Complete questionnaire responses varied among participants across the six weeks, from 32 to 41 with an average of 37.7.

Instruments

In study 1, to gauge the linguistic progress among students, the study implemented a pre-and post-test design. A diagnostic writing test administered on the first day of class served as the pretest and a final writing exam served as the posttest. For these assessments, and for a recurring weekly writing task, participants were required to write a 300-word paragraph centered around diverse academic topics discussed in class and integrate sources from readings.

In study 2, to assess student preferences between human and AI-generated feedback, a questionnaire was developed to gather quantitative and qualitative data (see appendix A). Eight five-point Likert scale items, arranged into four pairs, captured various dimensions of feedback preference, including, satisfaction, comprehensibility and clarity, helpfulness, and overall preference (e.g.,: “I am satisfied with the feedback I received from the ENL tutor this week compared with “I am satisfied with the feedback I received from the AI program this week”). Participants were asked to respond using a scale from “1—Strongly Disagree” to “5—Strongly Agree.” In addition, participants were asked “If you were to only get one kind of feedback next week, which kind of feedback would you prefer?”. A follow-up open-ended question in which participants were asked to provide an explanation for their choice was also included.

OpenAI’s GPT-4 was utilized to generate feedback on student writing for both studies. GPT-4 is a multimodal LLM that can process image and text inputs and produce text outputs. GPT-4 was selected as we found it to provide the most suitable and accurate feedback out of the LLMs we tested. Furthermore, GPT-4 outperformed other LLMs on academic benchmarks, at least at the time of its release (OpenAI, 2023b ) and when this study was conducted.

The prompt sent to ChatGPT to generate feedback on students’ weekly writing consisted of several parts. Two experienced language educators familiar with the course and its assignments and assessments developed the prompt iteratively in line with the prompt-engineering framework presented by [Ingley, 2023 ]. First, GPT-4 was given the role of a professional language teacher who is an expert on providing feedback on the writing of English language learners. Second, the weekly writing prompt that students were given was included. Third, the LLM was instructed to, using simple language, comment on six areas of the students’ writing: the topic sentence, the development of ideas, language that lowers the academic quality of the writing, the use of transitional phrases, the use of sources and evidence, and the grammatical accuracy of the language of the writing. The AI was instructed to put the feedback on grammatical accuracy into a table that organized the following elements: the sentence where the error in the writing is found, the error type, a description of what this kind of error is, and suggestions as to how to address the error. Lastly, the student’s paragraph was copied into the prompt. An example prompt and resultant feedback are given in appendices B and C. A teaching assistant utilized GPT-4 and the prompt described above to generate feedback for each student. Feedback was emailed to students within two working days to ensure students had ample time to read the feedback and incorporate it into their revisions, should they desire to.

Data collection procedures

In study 1, students completed the pretest in the first week of the semester. For the next six weeks these students completed a weekly writing assignment. For each week students wrote a 300 word paragraph on a provided topic related to the material in class, received feedback either from a human tutor (CG) or AI (EG), revised, and submitted a final draft. Human tutors were paid trained English language tutors from the university’s ENL Tutor Program and certified by the College Reading & Learning Association. Participants in the CG held one 30-min one-on-one tutoring session per week with the same tutor for the duration of the study. The EG was required to submit an initial draft of their writing assignment each week. These submissions were reviewed to remove any identifying information. We then utilized GPT-4 to generate individualized feedback for each student, which was subsequently sent to them via email. Upon receiving and reviewing the AI-generated feedback, students made revisions to their writing and submitted their final drafts. In week 8, the final week of the semester, these students completed the posttest. Both pre- and posttests were independently rated by two experienced academic English language instructors. An analytic rubric assessing four key writing areas, namely content, coherence, language use, and sources and evidence, was used to assess students’ writing in the pre- and post test. Each category was scored individually on a scale of four, with descriptors outlining the performance at each level. Scores for each criterion varied from 1 to 4, with 1 representing an "Initial" level of performance and 4 representing a "Highly Developed" level of performance. The maximum achievable score on the rubric was 40. Inter-rater reliability was assessed by calculating intraclass correlation coefficients (ICC). These values indicated excellent inter-rater reliability for the CG and EG pretests (0.932, p  < 0.001; 0.919, p  < 0.001), as well as good reliability for the CG and EG posttests (0.877, p  < 0.001; 816, p  < 0.001).

In study 2, participants received feedback from a human tutor and from AI (GPT-4) on their weekly writing assignments. These human tutors were unaware that the participants were also receiving AI-generated feedback. The preference survey was distributed to participants via Qualtrics, once a week after students had completed their weekly writing assignment.

Data analysis procedures

To explore the first research question in study 1, a repeated-measure analysis of variance (RM-ANOVA) was conducted using SPSS 28. Using a general linear model, the time of the tests (pretest [T1] and posttest [T2]) served as a within-subjects variable, with the group serving as a between-subjects variable (experimental group or EG, and control group or CG). Missing data, outliers, and possible statistical assumption violations were examined. The scores of one student who only completed the posttest were not included in the analysis. Shapiro–Wilk test results suggested the data was normally distributed, except for the posttest control group (0.833, p  < 0.001). Levene’s test of equality of error variances indicated homogeneity of variance for both pre- ( p  = 0.888) and posttest ( p  = 0.938). RM-ANOVA results reported below follow a Greenhouse-Giesser correction. Lastly, two independent samples t -tests were conducted to explore potential differences in means at T1 and T2 to see if there were any significant differences in proficiency levels between the two groups before and after the treatment.

To explore the second research question in study 2, descriptive statistics were calculated for questions 1–9 for each week. Three researchers independently performed a thematic analysis of the qualitative data and then consulted on salient themes found in the data.

Relating to RQ1 in Study 1

Descriptive statistics for the EG and CG scores for the pre- (T1) and posttest (T2) along with results of the Shapiro–Wilk tests are reported in Table 1 . Both groups made similar progress in their academic writing over the six week long treatment, as indicated by the increase in mean scores for the EG (5.848) and CG (6.86).

The results of the mixed 2 × 2 RM-ANOVA analysis (Table 2 ) revealed there was no significant interaction effect between group and time (F = 3.094, p  = 0.085, η p 2  = 0.063) The effect size of the difference ( η p 2 ) signifies that this two-way interaction accounts for 6.3% of the variance in scores. The test of between-subjects effects revealed no significant difference between the EG and CG (F = 0.241, p  = 0.626, η p 2  = 0.005), indicating that the method of providing feedback (human tutor or AI) did not have a significant effect on students’ posttest scores; the between-subjects variable (group) only accounted for 0.5% of the variance.

Independent sample t- tests of the between-subjects variable at T1 and T2 are reported in Table 3 . No significant difference was found between EG and CG means in the pre- and posttest, suggesting no significant difference in writing proficiency between the groups at either times of measurement.

Relating to RQ2 in Study 2

The descriptive statistics for question items 1–9 of the weekly preference survey are shown in Appendix A. The data suggest a near even split in preferences. Question 9, for example, asked “If you were to only get one kind of feedback next week, which kind of feedback would you prefer?”. Figure  1 shows the slight fluctuations in mean responses to this question, with a value of 1.5 indicating an even split in preference. The six-week average of the number of students that preferred feedback from human tutors was 18, which was slightly lower than the average of those that preferred AI-generated feedback (19.667), although this may be due to fewer students completing the survey in the final week. The means for human tutor feedback (H) for the entire six weeks, were slightly higher in each pair of items: satisfaction (Q1&2), H = 4.277 AI = 4.262; clarity (Q3&4), H = 4.319 AI = 4.236; helpfulness (Q5&6), H = 4.305 AI = 4.228; and preference (7–8), H = 4.2 AI = 4.126. Given how close each pair of means were, and to further explore the data, students were grouped according to how they answered Q9 in week six, and a t- test for comparing the mean response of these two groups for each item was conducted. All means were nonsignificant between these groups, further suggesting that student preference was equally split.

Mean responses to item 9, where 1 equals preference for human feedback and 2 equals preference for AI feedback

Line graphs that included items 1–8 (Fig.  2 ), and each pair of items (Figs.  3 , 4 , 5 , 6 ) show two trends. First, preference for human tutor feedback is generally slightly higher. Second, means were highest in weeks one, three, and six, with slight dips in weeks two, four, and five. Important to note, however, is that the means for items Q1-8 in all weeks, except for Q6&8 in week two, were above 4, suggesting that students generally perceived both forms of feedback as being of value.

Means of items 1–8

Means of items 1 and 2 relating to satisfaction with feedback

Means of items 3 and 4 relating to clarity

Means of items 5 and 6 relating to helpfulness

Means of items 7 and 8 relating to preference

An analysis of the qualitative data did not yield any insights into why the means of items 1–8 dipped in weeks two, four, and five. It may be that the writing topics proved more difficult in these weeks, or that students did not get as much out of the human or AI-generated feedback because of other pressures in their academic and/or personal lives, such as having unit tests during these weeks.

There were several recurring themes in the qualitative data as to why some students preferred receiving feedback from a human tutor. Perhaps the most prominent was the affordances of sitting down face to face and interacting with a human. For some this was viewed as beneficial because they found personal interaction to be “more engaging” and “a fun way to learn” when compared to just reading through AI-generated feedback. Some cited the ability to ask follow-up questions and get immediate feedback as being instrumental. Others noted that interacting with a human tutor allowed them to develop their writing and speaking skills at the same time. One student observed that “AI comment is super helpful, but personal characteristic [sic] could be missing after AI revision”, suggesting that reliance on AI might unintentionally result in the erasure of the personal voice of the writer.

As for those students that preferred receiving AI-generated feedback, clarity, understandability, consistency, and specificity of feedback, especially in regards to academic writing style and vocabulary, were common themes found in the data. Several students commented on how the AI-generated detailed feedback on errors in academic writing. For example, one student wrote “the AI program provides me with concrete feedback and easy-to-understand documentation of where the errors were.” Another noted “The AI feedback was so accurate and it also suggested academic words that I could use in place of the words that I used which was not very academic.” Several students commented how AI does not have constraints of time or availability, and students can review the feedback whenever they want: “AI gives me correct and accurate feedback on every sentence, no matter the time. However, [ENL] tutors have limited time and can only get limited feedback.”

Several students highlighted the advantages of both forms of feedback. For example, one student wrote “I prefer to have both. The reason why I like my [ENL] tutor, is because I think interaction helps my brain to learn and focus, so I could improve and progress. On the other hand, the AI also was very helpful, it pointed out the problems precisely and clear to understand. I would say they are a good combination for students.” Another student wrote “If there was a question whether to receive feedback from both [ENL] tutors and AI program I would say yes because I would be able to learn more from both”.

Discussion and conclusion

Study 1 compared human tutor and AI-generated feedback to see if one would influence linguistic gains more than the other. The results indicate that AI-generated feedback did not result in superior linguistic progress among ENL students compared to those who received feedback from a human tutor. The between-subject variable of group did not have a significant effect on writing scores, suggesting that one method of feedback was not better than another in terms of scores.

While the study found that AI-generated feedback did not lead to superior linguistic progress among ENL students compared to human tutor feedback, it is important to consider the potential time-saving benefits offered by AI-generated feedback for educators. Utilizing AI for providing feedback can potentially significantly reduce the time teachers spend on reviewing and responding to each student's assignment, thereby freeing up valuable time for other tasks. Furthermore, the time efficiency of AI-generated feedback can be particularly advantageous in large classes where providing individualized feedback by the instructor is logistically challenging and time-consuming.

Study 2 investigated which form of feedback ENL students preferred and why. We found about half the students preferred receiving feedback from a human tutor, and half preferred AI-generated feedback. Those that preferred sitting down and discussing their feedback with a tutor cited the face-to-face interaction as having affective benefits, such as increasing engagement, as well as benefits for developing their speaking abilities. Those that preferred AI-generated feedback primarily cited the clarity and specificity of the feedback as being useful for improving their writing. This echoes the findings of Dai et al. ( 2023 ), namely that AI-generated feedback was found to be more readable and detailed than feedback from an instructor.

We offer several suggestions as to how the inclusion of AI-generated feedback might be better incorporated into practice. In this study, as students were emailed the feedback generated by the AI, the students had no opportunity to ask follow-up questions to the AI. This is because we wanted to limit students' access to the AI and prevent potential misuses where students asked the AI to write their assignments for them. However, providing opportunities for students to ask follow-up questions to the AI may lead to a greater preference in AI-generated feedback.

In light of the major findings highlighted above, we believe a mixed approach to providing feedback may be most beneficial for both language educators and students. By utilizing GenAI, language educators may be able to produce more detailed feedback in a shorter amount of time for each individual learner. Providing opportunities for students to discuss AI-generated feedback with a human tutor and ask follow up questions affords students with the benefits of each modality, namely the clarity and specificity of the AI-generated feedback, and the benefits of interacting with another human, such as engagement and the ability to practice speaking.

With AI industry leaders predicting artificial capable intelligence, able to perform day to day tasks, being available in two years (Suleyman, 2023 ), and super artificial intelligence arriving potentially this decade (Leike & Sutskever, 2023 ), it is of growing importance that practitioners in the field of language education become more familiar with this rapidly changing technology, its potential uses, and how it may drastically influence and personalize language education in the future.

Currently GPT-4 is not optimized for AWE purposes and therefore features like text annotation, common in existing AWE programs, are cumbersome to reproduce. However, some established AWE programs have begun to integrate GPT technology (e.g., GrammarlyGO ). It is likely only a matter of time before large language models exist that have been fine tuned and optimized for language learning and teaching purposes, including assessing writing. Furthermore, as the public gets increased access to these models (e.g., through application program interfaces, or APIs), GenAI will likely be woven into the learning management systems commonly used in educational institutions, thereby becoming a more integral part of the practices of educators and students.

A potentially fruitful avenue of research would be to examine how the proficiency levels of students affect their ability to understand and learn from AI-generated feedback. Furthermore, investigating GenAI’s ability to reliably assess and score writing would be insightful. Lastly, in a similar vein to this study, further research could focus on investigating the efficacy of AI-generated feedback on native English-speaking students.

In addition, we encourage language educators to consider the following questions:

What aspects of the language learning process are best performed by GenAI, now and in the future?

What aspects of the language learning process are best performed by humans, now and in the future?

As GenAI becomes more capable and prevalent, what skills will become more important for language educators to cultivate?

To conclude, while admittedly there are a number of vehicles for personalized learning, the potential of GenAI in this area merits further attention. As GenAI continues to be developed and permeate the sphere of language education, it becomes imperative to ensure a balanced approach, one that capitalizes on its strengths while duly recognizing the indispensable contributions of human pedagogy. The endeavor of comprehending and assessing the capabilities of GenAI, along with its potential influence on language learning and teaching, is arguably now of paramount importance.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Artificial intelligence

English as a new language

Generative Pretrained Transformer 4 (from OpenAI)

A specific chatbot interface for the GPT models

• Automated writing evaluation

Large language models

Generative Artificial Intelligence

A language model from Google

Graduate record examination

Test of English as a Foreign Language

Bidirectional Encoder Representations from Transformers (another type of language model)

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Sample prompt sent to GPT-4 to generate feedback

You will be a professional language teacher who is an expert on providing feedback on the writing of English language learners. Here is the writing prompt that students are given: [the weekly writing prompt was inserted here].

Below I will share with you a student’s writing. Based on their writing, comment on the following:

Using simple language, comment on the quality of the topic sentence and if it addresses the writing prompt. Provide suggestions for improvement but don’t write a new topic sentence for the student. Provide an example of an improved topic sentence that is about a different topic than the student’s writing. Start your feedback with the header in bold “ Feedback on the quality of the topic sentence: ”.

Using simple language, comment on the development of ideas throughout the paragraph. Specifically comment on the development of the main idea through supporting ideas and elaborating details such as examples and evidence. Start your feedback with the header in bold “ Feedback on the development of ideas throughout the paragraph: ”.

Using simple language, identify language in the paragraph that lowers the paragraph's academic quality. Provide examples of academic words and phrasing that would improve the writing. Start your feedback with the header in bold “ Feedback on the academic quality of the language: ”.

Using simple language, examine the use of transitional phrases in the paragraph. Are they appropriate for academic writing? How could they be improved? Start your feedback with the header in bold “ Feedback on the transitional phrases: ”.

Using simple language, evaluate the paragraph according to its use of sources and evidence. Start your feedback with the header in bold “ Feedback on the use of sources and evidence: ”.

Using simple language, comment on the grammatical accuracy of the language of the writing, such as spelling, capitalization, punctuation, singular and plural nouns, verb tense, subject verb agreement, word form, awkward phrasing, prepositions, articles, and sentence fragments and run-ons. Put the feedback on grammatical accuracy in a table with four columns. The first column includes the sentence where the error in the student’s writing is found. The second column is the error type. The third column is a description of what this kind of error is. The fourth column is a suggestion on how to address the error.

Here is the student’s paragraph. Using simple language, please provide feedback in the manner described above.

[The student’s paragraph was inserted here.]

Example AI-generated feedback

Original student writing.

The most important aspect of culture is a culture of perception, which helps build better societies and relationships. I know this is a unique culture for other people and has some disadvantages however, this is a very Japanese thing and important for Japanese people. In my opinion, this is a virtue of the Japanese people and something to be proud of. There are two reasons why I think the culture of perception is the most important aspect of culture. First of all, this culture led us to be the people who think and act on their own. For example, when I was 13 years old, I had an opportunity to help my father’s work. And I observed what my father doing and thought about how I can help him to work more efficiently. Thanks to that experience, I could move to actions beforehand my other coworkers tell me what want me to do. It had helped me many times. Moreover, even in other situations, such as assisting with activities and building good relationships. The second reason is that it leads us to be able to work more effectively and efficiently. In any kind of situation, communication will be an obstacle to working faster sometimes. I have one example of this. In the doctor industry, surgery is a race against time, and people in the surgery room should work efficiently and must be consistent in them. Furthermore, people who work for doctors should support them by observing their movement so that doctors can work without stress. I know that this culture also leads us in negative ways, such as exclusivism and less skill in conversation. I think that we have to understand and use this culture correctly so that we avoid negative results. In conclusion, the culture of perception is the most important aspect of culture and helps us to work more effectively and efficiently.

AI-generated feedback

Feedback on the quality of the topic sentence.

The current topic sentence introduces the main idea of the paragraph—the importance of the culture of perception in building societies and relationships. However, the topic sentence could be improved by being more concise and clearly stating the main idea. For example, for a different topic, an improved topic sentence might be: "Implementing renewable energy sources is crucial for mitigating the effects of climate change and promoting sustainable development."

Feedback on the development of ideas throughout the paragraph

The paragraph does a decent job of developing the main idea of the culture of perception being the most important aspect of culture. The author provides two reasons for this belief and elaborates on each reason with examples and evidence. However, the paragraph could benefit from more specific details and evidence to support the claims. Additionally, connecting the examples back to the main idea more explicitly would strengthen the paragraph.

Feedback on the academic quality of the language

The paragraph contains some informal language that detracts from its academic quality. For example, "this is a very Japanese thing" could be replaced with "this cultural aspect is particularly significant in Japan." Instead of "I know this is a unique culture for other people," a more academic phrasing could be "It is evident that this cultural aspect may be distinct for individuals from other backgrounds."

Feedback on the transitional phrases

The paragraph includes some transitional phrases, such as "First of all," "Moreover," and "In conclusion." These phrases are generally appropriate for academic writing. However, the transitions could be improved by using more varied phrases, such as "Additionally" or "Furthermore," to enhance the flow of ideas and maintain a consistent academic tone.

Feedback on the use of sources and evidence

The paragraph includes personal anecdotes and examples to support the main idea. While these examples help to illustrate the points being made, incorporating more external sources, such as studies, expert opinions, or statistics, would strengthen the argument and lend credibility to the claims.

Feedback on the grammatical accuracy

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Escalante, J., Pack, A. & Barrett, A. AI-generated feedback on writing: insights into efficacy and ENL student preference. Int J Educ Technol High Educ 20 , 57 (2023). https://doi.org/10.1186/s41239-023-00425-2

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Anatomy of an AI Essay

How might you distinguish one from a human-composed counterpart? After analyzing dozens, Elizabeth Steere lists some key predictable features.

By  Elizabeth Steere

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Since OpenAI launched ChatGPT in 2022, educators have been grappling with the problem of how to recognize and address AI-generated writing. The host of AI-detection tools that have emerged over the past year vary greatly in their capabilities and reliability. For example, mere months after OpenAI launched its own AI detector, the company shut it down due to its low accuracy rate.

Understandably, students have expressed concerns over the possibility of their work receiving false positives as AI-generated content. Some institutions have disabled Turnitin’s AI-detection feature due to concerns over potential false allegations of AI plagiarism that may disproportionately affect English-language learners . At the same time, tools that rephrase AI writing—such as text spinners, text inflators or text “humanizers”—can effectively disguise AI-generated text from detection. There are even tools that mimic human typing to conceal AI use in a document’s metadata.

While the capabilities of large language models such as ChatGPT are impressive, they are also limited, as they strongly adhere to specific formulas and phrasing . Turnitin’s website explains that its AI-detection tool relies on the fact that “GPT-3 and ChatGPT tend to generate the next word in a sequence of words in a consistent and highly probable fashion.” I am not a computer programmer or statistician, but I have noticed certain attributes in text that point to the probable involvement of AI, and in February, I collected and quantified some of those characteristics in hopes to better recognize AI essays and to share those characteristics with students and other faculty members.

I asked ChatGPT 3.5 and the generative AI tool included in the free version of Grammarly each to generate more than 50 analytical essays on early American literature, using texts and prompts from classes I have taught over the past decade. I took note of the characteristics of AI essays that differentiated them from what I have come to expect from their human-composed counterparts. Here are some of the key features I noticed.

AI essays tend to get straight to the point. Human-written work often gradually leads up to its topic, offering personal anecdotes, definitions or rhetorical questions before getting to the topic at hand.

AI-generated essays are often list-like. They may feature numbered body paragraphs or multiple headings and subheadings.

The paragraphs of AI-generated essays also often begin with formulaic transitional phrases. As an example, here are the first words of each paragraph in one essay that ChatGPT produced:

• “In contrast”
• “Furthermore”
• “On the other hand”
• “In conclusion.”

Notably, AI-generated essays were far more likely than human-written essays to begin paragraphs with “Furthermore,” “Moreover” and “Overall.”

AI-generated work is often banal. It does not break new ground or demonstrate originality; its assertions sound familiar.

AI-generated text tends to remain in the third person. That’s the case even when asked a reader response–style question. For example, when I asked ChatGPT what it personally found intriguing, meaningful or resonant about one of Edgar Allan Poe’s poems, it produced six paragraphs, but the pronoun “I” was included only once. The rest of the text described the poem’s atmosphere, themes and use of language in dispassionate prose. Grammarly prefaced its answer with “I’m sorry, but I cannot have preferences as I am an AI-powered assistant and do not have emotions or personal opinions,” followed by similarly clinical observations about the text.

AI-produced text tends to discuss “readers” being “challenged” to “confront” ideologies or being “invited” to “reflect” on key topics. In contrast, I have found that human-written text tends to focus on hypothetically what “the reader” might “see,” “feel” or “learn.”

AI-generated essays are often confidently wrong. Human writing is more prone to hedging, using phrases like “I think,” “I feel,” “this might mean …” or “this could be a symbol of …” and so on.

AI-generated essays are often repetitive. An essay that ChatGPT produced on the setting of Rebecca Harding Davis’s short story “Life in the Iron Mills” contained the following assertions among its five brief paragraphs: “The setting serves as a powerful symbol,” “the industrial town itself serves as a central aspect of the setting,” “the roar of furnaces serve as a constant reminder of the relentless pace of industrial production,” “the setting serves as a catalyst for the characters’ struggles and aspirations,” “the setting serves as a microcosm of the larger societal issues of the time,” and “the setting … serves as a powerful symbol of the dehumanizing effects of industrialization.”

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AI writing is often hyperbolic or overreaching. The quotes above describe a “powerful symbol,” for example. AI essays frequently describe even the most mundane topics as “groundbreaking,” “vital,” “esteemed,” “invaluable,” “indelible,” “essential,” “poignant” or “profound.”

AI-produced texts frequently use metaphors, sometimes awkwardly. ChatGPT produced several essays that compared writing to “weaving” a “rich” or “intricate tapestry” or “painting” a “vivid picture.”

AI-generated essays tend to overexplain. They often use appositives to define people or terms, as in “Margaret Fuller, a pioneering feminist and transcendentalist thinker, explored themes such as individualism, self-reliance and the search for meaning in her writings …”

AI-generated academic writing often employs certain verbs. They include “delve,” “shed light,” “highlight,” “illuminate,” “underscore,” “showcase,” “embody,” “transcend,” “navigate,” “foster,” “grapple,” “strive,” “intertwine,” “espouse” and “endeavor.”

AI-generated essays tend to end with a sweeping broad-scale statement. They talk about “the human condition,” “American society,” “the search for meaning” or “the resilience of the human spirit.” Texts are often described as a “testament to” variations on these concepts.

AI-generated writing often invents sources. ChatGPT can compose a “research paper” using MLA-style in-text parenthetical citations and Works Cited entries that look correct and convincing, but the supposed sources are often nonexistent. In my experiment, ChatGPT referenced a purported article titled “Poe, ‘The Fall of the House of Usher,’ and the Gothic’s Creation of the Unconscious,” which it claimed was published in PMLA , vol. 96, no. 5, 1981, pp. 900–908. The author cited was an actual Poe scholar, but this particular article does not appear on his CV, and while volume 96, number 5 of PMLA did appear in 1981, the pages cited in that issue of PMLA actually span two articles: one on Frankenstein and one on lyric poetry.

AI-generated essays include hallucinations. Ted Chiang’s article on this phenomenon offers a useful explanation for why large language models such as ChatGPT generate fabricated facts and incorrect assertions. My AI-generated essays included references to nonexistent events, characters and quotes. For example, ChatGPT attributed the dubious quote “Half invoked, half spontaneous, full of ill-concealed enthusiasms, her wild heart lay out there” to a lesser-known short story by Herman Melville, yet nothing resembling that quote appears in the actual text. More hallucinations were evident when AI was generating text about less canonical or more recently published literary texts.

This is not an exhaustive list, and I know that AI-generated text in other formats or relating to other fields probably features different patterns and tendencies . I also used only very basic prompts and did not delineate many specific parameters for the output beyond the topic and the format of an essay.

It is also important to remember that the attributes I’ve described are not exclusive to AI-generated texts. In fact, I noticed that the phrase “It is important to … [note/understand/consider]” was a frequent sentence starter in AI-generated work, but, as evidenced in the previous sentence, humans use these constructions, too. After all, large language models train on human-generated text.

And none of these characteristics alone definitively point to a text having been created by AI. Unless a text begins with the phrase “As an AI language model,” it can be difficult to say whether it was entirely or partially generated by AI. Thus, if the nature of a student submission suggests AI involvement, my first course of action is always to reach out to the student themselves for more information. I try to bear in mind that this is a new technology for both students and instructors, and we are all still working to adapt accordingly.

Students may have received mixed messages on what degree or type of AI use is considered acceptable. Since AI is also now integrated into tools their institutions or instructors have encouraged them to use—such as Grammarly , Microsoft Word or Google Docs —the boundaries of how they should use technology to augment human writing may be especially unclear. Students may turn to AI because they lack confidence in their own writing abilities. Ultimately, however, I hope that by discussing the limits and the predictability of AI-generated prose, we can encourage them to embrace and celebrate their unique writerly voices.

Elizabeth Steere is a lecturer in English at the University of North Georgia.

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AI Can’t Replace Teaching, but It Can Make It Better

Science teacher Daniel Thompson circulated among his sixth graders at Ron Clark Academy, in Atlanta, on a recent spring morning, spot-checking their work and leading them into discussions about the day’s lessons on weather and water. He had a helper: a voice-activated AI that summoned apps and educational videos onto large-screen smartboards.

When a student asked, “Are there any animals that don’t need water?” Thompson put the question to the AI. Within seconds, an illustrated blurb about kangaroo rats appeared before the class.

Thompson’s voice-activated assistant, Origin, is the brainchild of computer scientist Satya Nitta, who founded a company called Merlyn Mind after many years at IBM, where he had tried, and failed, to create an AI tool that could teach students directly. The foundation of that earlier, ill-fated project was IBM Watson, the AI that famously crushed several Jeopardy champions .

Despite Watson’s game show success, it wasn’t very good at teaching students. After plowing five years and $100 million into the effort, the IBM team admitted defeat in 2017. “We realized the technology wasn’t there,” says Nitta. “And it’s still not there.”

Since the November 2022 launch of OpenAI’s ChatGPT, an expanding cast of AI tutors and helpers is entering the learning landscape. Most of these tools are chatbots that tap large language models trained on troves of data to understand student inquiries and respond conversationally with a range of flexible and targeted learning assistance. These bots can generate quizzes, summarize key points in a complex reading, offer step-by-step graphing of algebraic equations, and provide feedback on the first draft of an essay, among other tasks.

Some tools are subject-specific, such as Writable and Photomath, while others offer more all-purpose tutoring, such as Socratic (created by Google) and Khanmigo, an AI collaboration tool created by OpenAI and Khan Academy, a nonprofit provider of online lessons covering an array of academic subjects.

As more tools proliferate and their capabilities keep improving, relatively few observers believe education can remain AI free. At the same time, even the staunchest techno-optimists hesitate to say that teaching is best left to the bots. The debate is about the best mix.

Skepticism about AI often centers on students using the technology to cut corners and on AI’s tendency to hallucinate , or to make stuff up in an eagerness to answer every query. The latter concern can be mitigated (albeit not eliminated) by measures like programming bots to base responses on vetted curricular materials. Less attention, however, is paid to an even thornier challenge for AI at the heart of effective teaching: engaging and motivating students.

Nitta says there’s something “deeply profound” about human communication that allows flesh-and-blood teachers to quickly spot and address things like confusion and flagging interest in real time.

He joins other experts in technology and education who believe AI’s best use is to augment and extend the reach of teachers, a vision that takes different forms. The goal of Origin, for example, is to make it easier for teachers to engage with students while also navigating apps and other digital teaching materials. Instead of being stationed by their computer, teachers can move around the class and interact with students, even the ones hoping to disappear in the back.

Others in education are trying to achieve this vision by using AI to help train tutors to have more productive student interactions, or by multiplying the number of students an instructor can engage with. Ultimately, these experts envision a partnership in which AI is called on not to be a teacher, but to supercharge the power of humans already doing the job.

Fixing AI Engagement

Origin was piloted by thousands of teachers nationwide this past school year, including Thompson and three other teachers at Ron Clark Academy. The South Atlanta private school, where tuition is heavily subsidized for a majority low-income student body, is in a brick warehouse renovated to look like a low-rise Hogwarts, replete with an elaborate clocktower and a winged dragon perched above the main entrance.

As Thompson moved among his students, he wielded a slim remote control with a button-activated microphone, which he uses to command the AI software. At first, Thompson told the AI to start a three-minute timer that popped up on the smartboard. Then he began asking rapid-fire review questions from a previous lesson, such as, “What causes wind?” When students couldn’t remember the details, Thompson asked the AI to display an illustration of airflow caused by uneven heating of the Earth’s surface.

At one point, he clambered up on a student worktable while discussing the stratosphere, claiming (inaccurately) that it was the atmospheric layer where most weather happens, just to see whether any students caught his mistake (several students reminded him that weather happens in the troposphere). Then he conjured a new timer and launched into a lesson on water by asking the AI assistant to find a short educational movie about fresh and saltwater ecosystems. As Thompson moved through the class, he occasionally paused the video and quizzed students about the new content.

Studies on student engagement, including research reviews released in 2018 in the journal Social Behavior and Personality and in 2020 by the Australian Journal of Teacher Education , have shown its importance for academic success. While AI has many strengths, Nitta says, “it’s not very good at motivating you to keep doing something you’re not very interested in doing.”

“The elephant in the room with all these chatbots is, how long will anyone engage with them?” he says. He claims that in trials for Watson, students ignored its attempts to engage with them.

At a spring 2023 TED talk shortly after launching Khanmigo, Khan Academy founder and CEO Sal Khan pointed out that tutoring has provided some of the biggest jolts to student performance among studied education interventions . But there aren’t enough tutors available nor enough money to pay for them, especially in the wake of pandemic-induced learning loss .

Khan Academy chief learning officer Kristen DiCerbo was the vice president of learning research and design for education publisher Pearson in 2016 when it partnered with IBM on Watson, which she describes as “a different technology” that was very reliant on scripted responses, in contrast to the unscripted interactions students can have with generative AI.

Khanmigo doesn't answer student questions directly, but starts with questions of its own, such as asking whether the student has any ideas about how to find an answer. Then it guides them to a solution, step by step, with hints and encouragement.

Notwithstanding Khan’s expansive vision of “amazing” personal tutors for every student on the planet, DiCerbo assigns Khanmigo a more limited teaching role. When students are working independently on a skill or concept but get hung up or caught in a cognitive rut, she says, “we want to help students get unstuck.”

Some 100,000 students and teachers piloted Khanmigo this past academic year in schools nationwide, helping to flag any hallucinations the bot has and providing tons of student-bot conversations for DiCerbo and her team to analyze.

“We look for things like summarizing, providing hints and encouraging,” she explains.

The degree to which Khanmigo has closed AI’s engagement gap is not yet known. Khan Academy plans to release some summary data on student-bot interactions later this summer, according to DiCerbo. Plans for third-party researchers to assess the tutor’s impact on learning will take longer.

AI Feedback Works Both Ways

Since 2021, the nonprofit Saga Education has also been experimenting with AI feedback to help tutors better engage and motivate students. Working with researchers from the University of Memphis and the University of Colorado, the Saga team pilot in 2023 fed transcripts of their math tutoring sessions into an AI model trained to recognize when the tutor was prompting students to explain their reasoning, refine their answers, or initiate a deeper discussion. The AI analyzed how often each tutor took these steps.

Tracking some 2,300 tutoring sessions over several weeks, they found that tutors whose coaches used the AI feedback peppered their sessions with significantly more of these prompts to encourage student engagement.

While Saga is looking into having AI deliver some feedback directly to tutors, it’s doing so cautiously because, according to Brent Milne, the vice president of product research and development at Saga Education, “having a human coach in the loop is really valuable to us.”

Experts expect that AI’s role in education will grow, and its interactions will continue to seem more and more human. Earlier this year, OpenAI and the startup Hume AI separately launched “emotionally intelligent” AI that analyzes tone of voice and facial expressions to infer a user’s mood and respond with calibrated “empathy.” Nevertheless, even emotionally intelligent AI will likely fall short on the student engagement front, according to Brown University computer science professor Michael Littman, who is also the National Science Foundation’s division director for information and intelligent systems.

No matter how humanlike the conversation, he says, students understand at a fundamental level that AI doesn’t really care about them, what they have to say in their writing, or whether they pass or fail subjects. In turn, students will never really care about the bot and what it thinks. A June study in the journal Learning and Instruction found that AI can already provide decent feedback on student essays. What is not clear is whether student writers will put in care and effort, rather than offload the task to a bot, if AI becomes the primary audience for their work.

“There’s incredible value in the human relationship component of learning,” Littman says, “and when you just take humans out of the equation, something is lost.”

This story about AI tutors was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the  Hechinger newsletter .

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Revolutionize Your Writing: The Best 5 AI Tools for Crafting High-Quality Essays

In the age of digital transformation, Artificial Intelligence (AI) has revolutionized various sectors, including education and professional writing. AI-powered tools have made it easier for students, professionals, and writers to craft well-structured and coherent essays. Here, we explore the five best AI tools to write essays, focusing on their unique features, benefits, and how they can enhance your writing process. The tools under discussion are Grammarly, GoodPen, MyStylus Generator, EditPad, and MyEssayWriter.

Part 1. Grammarly

Part 2. goodpen, part 3. mystylus generator, part 4. editpad ai essay writer, part 5. myessaywriter.

Grammarly is a well-known AI writing assistant that has become indispensable for many writers. Its advanced AI algorithms help in correcting grammar, punctuation, and style errors while providing suggestions for improving clarity and readability. Grammarly also features a plagiarism checker, ensuring the originality of your work. This comprehensive tool is designed to cater to writers across various domains, from academic writing to professional communication and creative writing.

Key Features:

• Grammar and Spelling Checker: Grammarly’s robust grammar and spelling checker automatically detects and corrects grammatical errors, spelling mistakes, and typos. This feature is particularly useful for ensuring that your essay is free from common writing errors that can detract from its overall quality. The tool offers detailed explanations for each correction, helping users understand the rules and improve their writing skills over time.
• Style Suggestions: Grammarly offers style suggestions that help enhance the tone and style of your writing according to the intended audience. Whether you are writing a formal academic essay, a business report, or a more casual blog post, Grammarly can adjust its recommendations to suit your needs. The tool also provides insights into your writing style, such as readability scores and vocabulary usage, allowing you to make more informed choices about how you present your ideas.
• Plagiarism Detection: The plagiarism detection feature compares your text against a vast database of academic papers, articles, and web content to ensure the originality of your work. This is crucial for maintaining academic integrity and avoiding potential plagiarism issues. The tool provides a detailed plagiarism report, highlighting any potential matches and offering suggestions for how to address them.
• Integration: Grammarly is compatible with various platforms, including MS Word, Google Docs, and web browsers, making it a versatile tool that can be used across different writing environments. Its seamless integration allows users to receive real-time feedback as they write, ensuring that their work is polished and professional.

Sign Up and Log In:

Create an account on Grammarly's website or log in if you already have one.

Install Extensions:

Install the Grammarly browser extension or desktop app for seamless integration with your writing platforms.

Upload or Write Your Text:

You can either upload your document or start writing directly in Grammarly's editor.

Review Suggestions:

As you write, Grammarly will underline errors and suggestions. Click on these to see detailed explanations and options for corrections.

Implement Changes:

Accept or ignore Grammarly’s suggestions to improve your text. Use the plagiarism checker to ensure originality before finalizing your essay.

Grammarly's comprehensive feedback makes it an excellent tool for anyone looking to polish their essays and ensure they are error-free and engaging. Its user-friendly interface and wide range of features make it a go-to choice for both novice and experienced writers. By using Grammarly, writers can not only improve their current projects but also develop better writing habits that will benefit them in future endeavors.

GoodPen is an AI writing tool designed to assist in the creation of essays with minimal effort. It provides users with well-structured content based on the given prompts and ensures that the essay follows a logical flow. GoodPen is particularly useful for writers who struggle with organizing their thoughts and need help generating coherent and structured essays. The tool's intuitive design makes it accessible to users of all skill levels, from students to professionals.

• Prompt-Based Writing: GoodPen generates content based on specific prompts or topics provided by the user. This feature is incredibly useful for those who have a topic in mind but are unsure how to start or develop their essay. By breaking down the writing process into manageable steps, GoodPen helps users overcome writer's block and start writing with confidence.
• Coherence and Structure: The tool ensures that the essay has a logical structure and coherence throughout. GoodPen’s AI algorithms are designed to maintain a consistent flow, making sure each paragraph transitions smoothly to the next. This is particularly beneficial for longer essays, where maintaining a clear and cohesive structure can be challenging.
• Customization: GoodPen allows users to tailor the content to meet specific requirements or preferences. You can adjust the tone, style, and level of detail to suit your particular needs, whether for academic, professional, or personal writing. The tool also offers various templates and outlines, helping users to organize their thoughts and present their ideas effectively.
• Create an Account: Sign up on the GoodPen website to access the tool.
• Choose a Template: Select a template or outline that suits your essay’s requirements.
• Input Your Prompt: Enter the specific prompt or topic you need to write about.
• Generate Content: GoodPen will provide a draft based on your input. Review the generated content and make adjustments as needed.
• Customize and Finalize: Tailor the essay to your specific needs by editing the tone, style, and details. Use the provided suggestions to enhance coherence and structure.

GoodPen is ideal for those who need to generate high-quality essays quickly, making it a valuable tool for students and professionals with tight deadlines. Its ability to produce well-organized and coherent content can significantly reduce the time and effort required to complete writing assignments. By using GoodPen, writers can focus on developing their ideas and arguments, rather than getting bogged down by structural concerns.

MyStylus Generator is a sophisticated AI tool that specializes in creating essays that are not only grammatically correct but also stylistically impressive. It uses advanced algorithms to understand the user's writing style and adapt the content accordingly, making it an excellent choice for writers who want their essays to reflect a unique voice and style. MyStylus Generator is designed to cater to a wide range of writing needs, from academic essays to creative writing projects.

• Stylistic Adaptation: MyStylus Generator mimics the user's writing style to produce personalized content. This feature is particularly beneficial for writers who want to maintain a consistent voice across their work. The tool analyzes previous writing samples to understand the user's style, ensuring that the generated content aligns with their unique voice.
• Advanced Topic Understanding: The tool delivers in-depth analysis and content generation on complex topics. MyStylus Generator’s AI is capable of understanding nuanced subject matter, making it suitable for more advanced writing tasks. The tool can generate detailed outlines, suggest relevant sources, and even provide specific examples to support your arguments.
• Ease of Use: The user-friendly interface simplifies the essay writing process. Even those who are not tech-savvy can navigate the tool easily and take full advantage of its features. MyStylus Generator offers a step-by-step guide to help users input their requirements and preferences, making the writing process straightforward and efficient.
• Sign Up: Create an account on the MyStylus Generator website.
• Upload Writing Samples: Provide a few samples of your writing to help the AI understand your style.
• Specify Your Topic: Enter the topic or prompt for your essay.
• Generate a Draft: The AI will produce a draft that matches your style and adheres to the topic.
• Review and Edit: Review the generated essay, make necessary edits, and ensure it meets your standards.

The MyStylus Generator is perfect for those who want their essays to reflect a unique voice and style, ensuring that the final product is both informative and engaging. Its ability to adapt to different writing styles makes it a versatile tool for a wide range of writing needs. By using MyStylus Generator, writers can create content that stands out and captures the reader's attention, whether they are working on academic assignments, professional reports, or creative projects.

EditPad offers a specialized AI essay writer that focuses on delivering high-quality essays tailored to the user's needs. This tool is designed to help users overcome writer's block and generate comprehensive essays with ease. EditPad’s AI Essay Writer is particularly useful for students and professionals who need to produce well-researched and coherent essays on a variety of topics. The tool's comprehensive features and user-friendly design make it an excellent choice for writers of all levels.

• Versatile Content Generation: EditPad’s AI Essay Writer can produce essays on a wide range of topics and subjects. Whether you need an essay on a scientific topic, a historical analysis, or a literary critique, the tool can generate relevant and high-quality content. The tool's versatility makes it suitable for a wide range of writing assignments, from short essays to in-depth research papers.
• Quality Assurance: The tool provides high-quality content with minimal errors. EditPad’s algorithms are designed to ensure that the generated essays are not only accurate but also well-written and free from common writing mistakes. The tool offers detailed feedback on grammar, style, and structure, helping users to refine their work and produce polished final drafts.
• User Control: EditPad allows users to guide the writing process by providing input and feedback. You can specify certain aspects of the essay, such as the thesis statement or key points, and the tool will incorporate your preferences into the final product. This level of customization ensures that the generated content meets your specific requirements and expectations.
• Sign Up: Create an account on the EditPad website to access the AI Essay Writer.
• Select Your Topic: Enter the topic or subject of your essay.
• Provide Guidelines: Input any specific requirements or guidelines for the essay, such as length, style, or key points to be included.
• Generate a Draft: The AI will generate a draft based on your input. Review the draft for accuracy and coherence.
• Edit and Customize: Make necessary edits to tailor the essay to your needs. Use the feedback provided by EditPad to enhance the quality of your final draft.

EditPad's AI Essay Writer is an excellent choice for those who need a reliable tool to generate well-researched and coherent essays, especially under time constraints. Its versatility and user-friendly features make it a valuable addition to any writer’s toolkit. By using EditPad, writers can save time and effort, allowing them to focus on other important aspects of their academic or professional work.

MyEssayWriter is an AI-driven platform that offers personalized essay writing services. It is designed to assist users in creating essays that meet academic standards and are tailored to specific requirements. MyEssayWriter’s focus on customization and quality makes it an excellent tool for students who need help with academic writing. The platform is also suitable for professionals who need to produce high-quality written content for various purposes.

• Custom Essay Writing: MyEssayWriter provides personalized essays based on user specifications. You can provide detailed instructions, and the tool will generate an essay that meets your exact requirements. This level of customization ensures that the essay is tailored to your specific needs, whether for academic, professional, or personal use.
• Academic Focus: The tool ensures the content meets academic standards and guidelines. MyEssayWriter’s algorithms are designed to produce essays that are not only well-written but also adhere to academic conventions and formatting rules. This includes proper citation and referencing, ensuring that your essay meets the required academic standards.
• User-Friendly Interface: The simple and intuitive platform makes it easy to navigate and use. Even those who are new to AI writing tools can quickly learn how to use MyEssayWriter and take full advantage of its features. The platform provides clear instructions and a step-by-step guide to help users input their requirements and generate high-quality essays.
• Create an Account: Sign up on the MyEssayWriter website to get started.
• Provide Essay Details: Enter the specific details of your essay, including the topic, length, style, and any other requirements.
• Upload Additional Materials: If you have any specific materials or references, upload them to help the AI generate a more accurate and tailored essay.
• Generate the Essay: The AI will produce a draft based on your input. Review the draft for accuracy and relevance.
• Edit and Finalize: Make any necessary edits to ensure the essay meets your standards. Use the feedback provided by MyEssayWriter to refine the content and improve its quality.

MyEssayWriter is particularly beneficial for students who need help with academic essays, providing them with high-quality, tailored content that meets their specific needs. Its ability to produce custom essays makes it a valuable resource for students at all levels of education. By using MyEssayWriter, students can ensure their essays meet academic standards and are well-structured and coherent.

The integration of AI in essay writing has significantly enhanced the way we approach and produce written content. Tools like Grammarly, GoodPen, MyStylus Generator, EditPad, and MyEssayWriter offer diverse features that cater to different writing needs, from grammar and style correction to prompt-based content generation and personalized essay writing. Whether you are a student looking to improve your academic writing or a professional aiming to produce polished and coherent content, these AI tools provide invaluable assistance in crafting well-structured and high-quality essays. Embracing these technologies can not only save time but also elevate the overall quality of your writing, making the process more efficient and enjoyable.

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