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PONE-D-23-14620ChatGPT versus human in generating medical graduate exam questions – An international prospective studyPLOS ONE

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Additional Editor Comments:

The authors should pay careful attention to each of the comments below and address the issues raised by the reviewers. Since the scale of the current study is not sufficient to support a generic conclusion, the authors need to be more cautious when interpretting the results.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I Don't Know

Reviewer #4: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an interesting study. The actual quality of MCQ can be assessed by item analysis after testing it on students. The authors used assessment by experts. The study is interesting. needs some changes.

In title specify exam of which country? Different countries has different level of difficulty.

"An international prospective study" is not needed.

Background in abstract may have more detail about why this study was conducted.

Avoid old references.

Figure 3 is hazy.

Reviewer #2: My main criticism concerns the method used to assess the quality of the questions. For me the only scientifically valid way to evaluate the quality of the questions is to use them in real life, in official exams for example. Only the statistical analysis of students' answers makes it possible to evaluate the quality of a question. This is especially true for distractors. Making this criticism in the discussion is necessary in my opinion.

Reviewer #3: Many thanks for the authors to tackle this hot topic in medical education. I enjoyed reading your manuscript however, the following points should be addressed to improve it

1. The title “ChatGPT versus human in generating medical graduate exam questions” indicate more general term while the study mentioned only MCQ type of questions so either you need to include other types of questions or change the title to indicate the MCQ.

2. Method, how you make sure that the two faculty staff who generated the 50 questions were not using AI to generate questions? Clarify this in method section.

3. Some results mentioned in the discussion section “When the questions generated by ChatGPT were reviewed, we could also observe that they were compatible with the guidance from the Division of Education, American College ofSurgeons, with minimal negative features, including a minimal use of negative stem (only14% (7/50), compared to 12% (6/50) by human examiners) with a lack of “except”,“All/none of the above”

This should be mentioned in result section and discussed in the discussion section.

4. Regarding assessor evaluation of quality of questions it would be better to assess the Bloom levels of questions and add it as another parameter to ensure that the questions not only assessing the lower levels of Bloom (remembering and understanding) specially for questions generated by AI.

5. Table 3 need more clarification (how you calculate the percentage in each column)

6. Table 4 the calculation of % should be the number of truly guessed question written by AI to the 50 questions written by AI, the same for human correct guess(e.g for assessor B it should be 14/50 not 14/23

7. Attach a strobe checklist for the article and address the missed part in the manuscript.

8. Look for instruction of authors for both incitation and reference section.

Reviewer #4: The authors made a comparative study between ChatGPT and Human Experts in generating medical domain MCQ questions from textbooks. The study offers some insight on the performance of chatGPT in generating medical MCQs.

However, I don't find any technical contribution in this article. What is the contribution of the authors? They only made some comparison between the MCQs generated by ChapGPT and human experts using certain evaluation criteria.

Additionally, the article needs some improvement for common readers:

There is no explanation on how the questions are generated by ChatGPT. I feel a large number of readers do not have specific idea on how to generate MCQs from a textbook (as input) using ChatGPT. So, there should be some discussion on that.

What are the list of human input (like, specific information, selection of specific portion, any tuning, parameter setting) or amount of experience required to generate meaningful questions using ChatGPT? For instance, the authors mentioned, the length of the text was limited by ChatGPT at 510 tokens. What are the other such inputs?

The authors claimed that "There was no significant difference in question quality between questions drafted by AI versus human". This is actually the key highlight of the paper. However, this is not completely supported by evidence. When I study the values in Table 3, I find that the human experts are much better than ChatGPT. For instance, appropriateness of the question: 18 (AI) vs 27 (human); Clarity and specificity 18 (AI) vs 26 (human); Distractor quality 21 (AI) vs 26 (human). The gap is significant. Then how the claim is justified?

Also, the number of questions is too less to make such a generic comment on ChatGPT vs Human. The study should consider much larger number of questions, more subjects as input, more number of human experts.

More number of MCQ evaluation metrics are required to be considered for complete evaluation of the MCQs from all aspects. The evaluation of quality of MCQs is a tricky task, and various metrics have been used in the literature in the past two decades. For example, well-formed, sentence length, sentence simplicity, difficulty level, answerable, sufficient context, relevant to the domain, over-informative, under-informative, grammaticality, item analysis etc. are some metrics I find in the literature.

Finally, the human evaluators give a score against each evaluation metric in a scale of 1-10. What was the specific guidelines given to the evaluators for the scoring? Only the name of a evaluation metric might have certain ambiguity and might carry different meaning to different experts. So, there should be proper guidelines for the evaluators. Please mention those.

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Reviewer #1: No

Reviewer #2: Yes: Pr. Olivier Palombi, Grenoble Université Alpes

Reviewer #3: Yes: Nazdar Ezzaddin Alkhateeb

Reviewer #4: No

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ChatGPT versus human in generating medical graduate exam multiple choice questions – A multinational prospective study (Hong Kong SAR, Singapore, Ireland, and the United Kingdom)

PONE-D-23-14620R1

Dear Dr. CO,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jie Wang, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study addresses an important and relevant topic concerning the potential use of AI language models for generating MCQs in medical education. The research purpose is well-defined, and the study's objectives are clear.

The study adopts a prospective design and employs a systematic approach by comparing ChatGPT-generated MCQs with those developed by human examiners. The use of standardized assessment scores and independent international assessors enhances the reliability and validity of the findings.

The article highlights the significant time advantage of ChatGPT in generating MCQs, with a total time of 20 minutes and 25 seconds to create 50 questions, compared to 211 minutes and 33 seconds taken by human examiners for the same task. This efficiency demonstrates the potential practicality of using AI in question generation.

The study reveals that ChatGPT-generated MCQs show comparable quality to those created by human examiners across most domains, including appropriateness, clarity, discriminative power, and suitability for medical graduate exams. This suggests that ChatGPT is capable of producing high-quality MCQs in these areas

Reviewer #2: (No Response)

Reviewer #4: Actually, I feel some more experiments are required to validate the claim. But it seems the authors feel their work is sufficient. Anyway ...

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Himel Mondal

Reviewer #2: Yes: Full Professor Olivier Palombi, Université Grenoble Alpes

Reviewer #4: No

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