03 · What You Need to Know
How AI Can Help Structure a Manuscript Revision Without Losing Important Details
A Revision Plan Is Different From a Summary of Reviewer Comments
A summary describes the main concerns raised during peer review. A revision action plan translates those concerns into tasks that authors can evaluate, assign, complete, and verify.
For example, the comment "The authors should clarify the sampling strategy and explain how missing data were handled" contains at least two distinct requests.
A summary might combine them under "methodological clarification." A useful action plan would preserve separate tasks for sampling and missing-data reporting.
This distinction matters because authors can address one part of a comment while unintentionally overlooking another.
AI may assist with identifying individual requests, but the resulting task list must be checked against the original wording.
Why Reviewer Comments Should Be Broken Into Actionable Units
Reviewer reports are not always organized as project-management documents. A single paragraph may contain observations, questions, suggested analyses, and requests for textual revisions.
Authors may find it useful to separate these elements while preserving their relationship to the original comment.
Each task should identify what the reviewer raised, what the authors need to evaluate, and what evidence would demonstrate that the concern was addressed.
For example, a request to clarify an instrument's validity evidence is different from a request to conduct a new validation study.
AI should not automatically convert the former into the latter.
What Should a Revision Action Plan Contain?
A structured plan can connect reviewer comments with decisions, responsibilities, manuscript changes, and verification records.
| Field |
Purpose |
Example |
| Comment identifier |
Preserves traceability |
Reviewer 2, Comment 4 |
| Original concern |
Records what was actually requested |
Clarify missing-data handling |
| Proposed action |
Identifies the work to evaluate or complete |
Review and expand the missing-data description |
| Priority |
Supports scheduling |
High, subject to editorial requirements |
| Responsible author |
Assigns accountability |
Statistical analysis lead |
| Task status |
Tracks progress |
Pending verification |
| Manuscript location |
Connects the task to the revision |
Methods, missing-data subsection |
| Response evidence |
Supports the response letter |
Verified revised paragraph |
These fields are suggested organizational practices rather than universal journal requirements. The plan should be adapted to the complexity of the revision and the journal's instructions.
AI Can Help Identify Overlapping Reviewer Requests
Different reviewers may raise similar concerns using different terminology.
One reviewer might request a stronger explanation of the sampling strategy, while another questions whether the sample adequately represents the intended population.
These comments may be related, but they are not necessarily identical.
The first may concern reporting completeness. The second may concern selection bias, generalizability, or the appropriateness of the authors' claims.
AI can suggest that the comments be grouped for coordinated revision, but the authors should preserve each reviewer's distinct concern.
A single manuscript change may address multiple comments, yet the response letter should still explain how each concern was handled.
Do Not Let AI Merge Comments That Require Different Scientific Responses
Overly aggressive consolidation can conceal important differences.
For example, a request to report effect sizes is not equivalent to a request to conduct sensitivity analysis. Both involve statistical reporting, but they address different questions.
Similarly, a request to acknowledge possible confounding differs from a request to estimate a causal effect using an alternative analytical strategy.
If AI combines these into a generic task such as "improve statistical analysis," the resulting plan may become less useful.
Authors should preserve enough detail to identify the actual work required.
How Should Authors Prioritize Reviewer Comments?
Revision priorities should reflect the editor's instructions, the scientific consequences of each concern, and the dependencies between tasks.
Major methodological issues may need to be resolved before the discussion and abstract can be revised accurately.
For example, an additional analysis might change the magnitude or interpretation of a finding. Revising the abstract before completing that analysis could create unnecessary rework.
AI may suggest a preliminary order, but authors should not treat that order as authoritative.
The handling editor's explicit requirements should receive particular attention. Reviewer recommendations should also be evaluated for scientific relevance rather than accepted automatically.
Revision Tasks Often Have Dependencies
Some changes cannot be completed responsibly until earlier decisions or analyses have been resolved.
Consider a reviewer request to reassess the relationship between learning management system engagement and academic performance after adjusting for relevant covariates.
The authors may first need to evaluate the appropriateness of the proposed analysis, inspect the available variables, and determine whether the data support the requested procedure.
Only after conducting and verifying any justified analysis should they revise the results, discussion, abstract, and response letter.
AI can help map these dependencies, but it should not assume that a requested analysis is feasible or methodologically necessary.
Contradictory Reviewer Comments Require Author and Editorial Judgment
Reviewer reports may contain recommendations that cannot easily be satisfied simultaneously.
One reviewer may ask the authors to expand the theoretical discussion, while another recommends substantial shortening. One may request additional subgroup analyses, while another warns against excessive exploratory testing.
AI may identify the apparent conflict and organize the competing requests.
However, it cannot determine the correct resolution simply by combining both recommendations.
Authors should consider the scientific rationale, journal requirements, and editor's guidance. Where the conflict is consequential, clarification from the handling editor may be appropriate.
Can AI Identify Which Comments Require New Analyses?
AI may help flag comments that appear to request statistical recalculation, additional modeling, robustness checks, or further qualitative analysis.
However, identifying a request is different from establishing that the requested analysis is appropriate, feasible, or necessary.
For example, a reviewer may request a subgroup analysis even when the study lacks adequate data to support meaningful subgroup inference.
The authors must assess the methodological implications before committing to the work.
The related question of whether AI can identify reviewer comments requiring new analyses deserves separate attention because such requests can materially change the research.
AI Should Not Invent Revision Tasks
When asked to create a comprehensive plan, generative AI may suggest additional work beyond the reviewers' actual requests.
Some suggestions may be useful, but they should be clearly distinguished from original reviewer comments.
For example, a reviewer may ask the authors to clarify the theoretical framework. AI might recommend collecting additional interview data, conducting structural equation modeling, or rewriting the entire conceptual model.
Those recommendations should not be presented as requirements imposed by the reviewer.
Authors should distinguish source-derived tasks from AI-generated suggestions and independently decide whether additional work is warranted.
How Should Authors Track Completed Revisions?
Each task should have a status that accurately reflects the work performed.
Useful distinctions include not started, in progress, completed, awaiting verification, partially addressed, and not adopted with justification.
Authors should avoid marking a task complete merely because a paragraph has been drafted or an AI system has generated a proposed response.
Completion should be supported by the actual manuscript change, verified analysis, or documented explanation.
Where multiple authors are involved, the responsible contributor should confirm the scientific accuracy of the relevant revision.
The Revision Plan Should Support the Response Letter
A well-maintained action plan can become the factual basis for the point-by-point response to reviewers.
Once revisions are completed, authors can describe the action taken, identify the manuscript location, and explain any justified disagreement.
AI may help convert verified revision records into professional prose.
However, AI-assisted responses to reviewers must not claim that work was completed merely because it appeared in the plan.
A planned analysis and a completed analysis are not interchangeable.
Confidentiality Applies to Reviewer Reports and Revision Materials
Reviewer comments may contain unpublished manuscript information, confidential editorial correspondence, or details subject to journal restrictions.
Authors should not assume that receiving the comments gives unrestricted permission to upload them to any AI service.
The journal's current policies and the provider's data-handling arrangements should be checked before processing confidential materials.
Where AI use is authorized, authors should limit the information shared to what is necessary and appropriate for the task.
If authorization is unclear, a manually maintained revision tracker may be preferable to transmitting the reports to an external system.
Should AI Use in Revision Planning Be Disclosed?
Disclosure requirements depend on the journal's policies and the nature of the assistance.
Some journals distinguish administrative organization or language editing from substantive AI-generated content. Others may impose broader disclosure requirements.
Authors should not assume that revision planning is automatically exempt from AI-use policies.
When disclosure is required, it should accurately describe the tool's role without suggesting that AI made scientific revision decisions independently.
