Can Generative AI Help Draft Responses to Peer-Reviewer Comments?

Can AI Help You Write a Response Letter After Receiving Peer-Review Comments?

You receive a decision inviting major revisions to your manuscript. Two reviewers have provided extensive comments, including requests for methodological clarification, additional analyses, stronger theoretical justification, and changes to the discussion.

You understand most of the concerns, but preparing a point-by-point response is demanding. Each comment requires a carefully worded explanation of what you changed, where the revision appears, or why a particular recommendation was not adopted.

Generative AI seems particularly useful for this task. It can organize comments, suggest professional wording, and help turn revision notes into a structured response letter.

Yet a convincing response is not necessarily an accurate one. AI may state that a requested analysis has been completed, that a limitation has been addressed, or that a manuscript section has been revised even when none of those actions occurred.

The central question is whether AI can assist with the communication of revisions without creating a misleading account of the research.

Yes, but Every Response Must Reflect What the Authors Actually Did

How Generative AI Can Support Accurate and Constructive Responses to Reviewers

A Response Letter Is a Record of Revision, Not Merely a Persuasive Document

A response-to-reviewers letter serves several purposes. It demonstrates that the authors have considered the reviewers' concerns, explains the actions taken, and helps editors determine whether the revised manuscript adequately addresses the issues raised.

The letter should therefore correspond to verifiable changes in the manuscript.

For example, if the authors state that they added a sensitivity analysis, the analysis should exist and its results should be accurately reported. If they claim to have revised the discussion, the relevant passage should actually contain the described changes.

Generative AI can improve how these actions are communicated, but it cannot substitute for completing the revisions themselves.

Which Parts of the Response Process Can AI Help With?

AI assistance may be useful for organizing information and improving communication, provided the journal permits the intended use.

These activities differ from allowing AI to decide which scientific revisions are necessary. The authors must retain responsibility for the research decisions.

Begin With a Complete Record of Reviewer Comments

Before drafting responses, authors should preserve the reviewers' comments accurately and identify the issues requiring action.

Reviewer reports often contain several concerns within one paragraph. A comment may request both clarification of the sampling procedure and revision of the conclusions drawn from the sample.

If AI summarizes the comment too aggressively, one requirement may disappear.

Authors should therefore compare any AI-generated summary or action list with the original reviewer report.

The more detailed task of organizing reviewer comments into a revision action plan can help separate requests for clarification, manuscript editing, and additional analysis.

Complete the Scientific Revision Before Claiming It Was Done

One of the safest approaches is to separate the research work from the drafting of the response.

Authors should first determine what changes are appropriate, conduct any necessary analyses, revise the manuscript, and document the resulting changes.

AI can then help express those completed actions in a clear response.

This sequence reduces the risk that the response letter becomes a list of promises rather than an account of completed work.

For example, a response should not state that missing-data sensitivity analyses were conducted merely because the reviewer requested them or the AI proposed them.

Why AI May Invent Manuscript Revisions

Generative AI can produce conventional response-letter language without having evidence that the described changes occurred.

Common formulations include statements such as "We have revised the Methods section," "The requested analysis has been added," or "We now acknowledge this limitation in the Discussion."

These statements are appropriate only when they are true.

If the AI receives reviewer comments without an accurate record of completed revisions, it may infer that the requested changes were made and draft responses accordingly.

This is particularly dangerous because the language sounds entirely normal in academic correspondence.

The problem of invented manuscript changes in AI-generated reviewer responses requires explicit verification against the revised document.

Distinguish Completed, Partial, and Unperformed Revisions

Not every reviewer concern can or should be addressed through the exact change requested.

Some comments may be fully resolved through additional analysis. Others may be addressed through clarification or a more cautious interpretation. Some recommendations may be inappropriate or infeasible.

The response should accurately represent the extent of the action taken.

This distinction is essential because a response letter should provide an accurate account of the revised research, not simply reassure reviewers that their concerns have been addressed.

Can AI Help Authors Respond to Difficult or Critical Comments?

Yes, it may help authors communicate disagreement without becoming defensive, where such assistance is permitted.

For example, a reviewer might insist that an exploratory qualitative study include statistical power calculations. The authors may reasonably disagree if the recommendation does not align with their methodological approach.

AI could help formulate a respectful explanation, but the methodological justification must be established independently.

A response should acknowledge the concern, explain the relevant research design, and clarify why the requested procedure was not adopted.

The goal is not to persuade the reviewer through elaborate diplomacy. It is to make the scientific reasoning understandable and defensible.

Disagreement Does Not Necessarily Mean the Authors Must Change the Method

Peer reviewers provide expert recommendations, but their suggestions are not automatically methodologically necessary.

Authors may disagree when a recommendation rests on an incorrect assumption, requires a different research question, or would introduce an inappropriate analytical procedure.

However, disagreement should be grounded in evidence and relevant methodological standards rather than personal preference.

Generative AI may help improve the wording of the explanation, but it should not invent supporting references or exaggerate the strength of the authors' position.

The distinction is explored further in using AI to explain why authors disagree with reviewers.

AI Can Accidentally Promise Work That Has Not Been Completed

Response letters often contain commitments such as conducting an additional analysis, revising a figure, or expanding a theoretical discussion.

When drafting from reviewer comments alone, AI may describe these activities as completed or imply that the authors have committed to them.

For example, a generated response might state that the authors have conducted subgroup analyses even though they have only considered whether those analyses are feasible.

Authors should distinguish decisions, plans, and completed actions.

Before submission, every claim about a revision should be checked against the manuscript and the actual research record.

Page and Line Numbers Must Be Verified

Many journals expect authors to identify where revisions appear in the updated manuscript.

Generative AI may produce plausible page or line references, especially when asked to draft a complete response letter.

However, those references may be invented or become outdated after further formatting changes.

Authors should insert verified page and line numbers only after the revised manuscript has reached an appropriately stable version.

If exact references are not yet available, temporary placeholders are preferable to fabricated locations.

Can AI Help Explain Additional Statistical Analyses?

AI may assist with drafting an explanation of analyses that the authors have actually performed.

However, the analysis itself must be independently verified, including the model specification, assumptions, numerical results, and interpretation.

For example, if a reviewer requests a robustness check, the authors should conduct the appropriate analysis before asking AI to help explain its findings.

The response should accurately describe whether the new results support, qualify, or change the original conclusions.

AI should not be permitted to invent a favorable statistical outcome or claim that a sensitivity analysis confirmed robustness without evidence.

Should Authors Upload Reviewer Comments to an AI Tool?

Not without considering the journal's policies, confidentiality expectations, and the AI provider's data-handling arrangements.

Reviewer reports may contain unpublished manuscript details, confidential editorial information, or material subject to restrictions.

Authors generally have legitimate access to the comments sent to them, but that access does not necessarily authorize unrestricted processing by an external service.

Before uploading comments, manuscript sections, or response drafts, authors should determine whether the journal permits the intended use and whether the tool provides appropriate protections.

The separate question of uploading confidential reviewer comments to AI tools deserves attention because the response-writing process often involves unpublished material.

Does AI-Assisted Response Writing Require Disclosure?

Disclosure requirements depend on the journal's current policy and the nature of the assistance.

Some publishers distinguish routine language editing from substantive generative AI use. Others may require disclosure of particular AI-assisted writing activities.

Authors should consult the journal's instructions rather than assume that response letters are exempt from AI policies.

If disclosure is required, it should accurately describe whether AI assisted with language editing, organization, or substantive drafting.

Disclosure does not excuse fabricated revision claims or unauthorized processing of confidential material.

Who Is Responsible for the Final Response Letter?

The authors remain responsible for the accuracy of the response submitted to the journal.

AI cannot confirm that a manuscript revision was completed unless the relevant evidence is available and properly verified. Nor can it assume responsibility for a misleading explanation or an invented analytical result.

Authors should ensure that every response corresponds to the actual revised manuscript and that any methodological disagreement is supported by defensible reasoning.

The responsibility extends beyond the corresponding author. Coauthors should review revisions and responses relevant to their contributions, particularly when the changes affect the scientific interpretation of the study.

Drafting a Response to a Reviewer Who Questions Causal Interpretation

The response is appropriate only because the hypothetical authors actually completed the stated revisions.

If the authors had merely planned those changes, the same wording would be misleading. The accuracy of the response depends on the underlying research record, not on the fluency of the generated text.

Misconceptions About AI-Assisted Responses to Reviewers

How to Use AI Without Misrepresenting Your Manuscript Revisions

The most defensible approach is to maintain a direct connection between each reviewer comment, the authors' decision, the completed revision, and the corresponding response.

AI may assist with wording after those relationships have been established, but it should not independently decide what happened during the revision process.

Maintain a Revision Verification Record

A practical revision record can include the original reviewer comment, the authors' planned response, the completed action, the manuscript section affected, and the verified location of the change.

Such a record helps prevent discrepancies between the response letter and the manuscript.

It also supports coauthor review, particularly when different researchers are responsible for statistical analyses, theoretical revisions, or methodological clarification.

The most useful role for generative AI is to improve the communication of genuine revision decisions. The integrity of the response depends on the authors' actual actions and their willingness to represent those actions accurately.

Checklist for AI-Assisted Responses to Peer Reviewers

Frequently Asked Questions About AI-Assisted Reviewer Responses

AI Can Help Explain Your Revisions, but It Cannot Make Them True

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