03 · What You Need to Know
How AI Can Exaggerate the Completeness and Significance of Manuscript Revisions
Addressing a Comment Is Not Necessarily the Same as Resolving the Underlying Problem
Reviewer comments can be addressed through different kinds of responses. Authors may correct an error, provide missing information, conduct additional analysis, qualify an interpretation, or explain why a requested change is inappropriate.
These actions do not necessarily produce equivalent outcomes.
For example, acknowledging that a cross-sectional design cannot establish temporal ordering addresses a limitation in the discussion. It does not change the study into a longitudinal investigation.
Similarly, explaining why a particular statistical model was selected may improve transparency without demonstrating that every possible model assumption has been satisfied.
A response letter should describe the action taken and its consequences accurately rather than imply that every concern has disappeared.
Why AI May Prefer Reassuring Language
Response-to-reviewers letters often use diplomatic formulations that emphasize improvement and responsiveness.
Generative AI can reproduce these conventions, sometimes using phrases such as "fully addressed," "comprehensively resolved," or "the revised analysis confirms the robustness of our findings."
Such wording may be appropriate in particular circumstances, but only when supported by the completed work.
When a model receives limited revision notes, it may generate a more reassuring account than the evidence warrants.
This can occur without inventing an entirely new manuscript section or analysis. The actual change exists, but its significance is overstated.
Common Forms of Overstatement in AI-Assisted Reviewer Responses
Overstatement can affect methodological claims, statistical interpretation, theoretical justification, and reporting completeness.
| Actual Revision |
Potential AI Overstatement |
More Accurate Interpretation |
| Added a confounding limitation |
"We have controlled for potential confounding." |
Potential confounding is now acknowledged, not eliminated. |
| Clarified missing-data handling |
"We resolved the missing-data problem." |
The existing procedure is described more clearly. |
| Conducted one sensitivity analysis |
"The findings are robust under all alternative assumptions." |
The findings were examined under one specified alternative. |
| Added theoretical discussion |
"The theoretical framework is now fully validated." |
The conceptual justification was expanded. |
| Revised causal wording |
"The causal inference concern has been eliminated." |
The interpretation is now more consistent with the observational design. |
| Expanded sampling description |
"The sample is now representative." |
Sampling transparency improved; representativeness was not necessarily established. |
| Added citations to the discussion |
"The literature review is now comprehensive." |
Additional relevant literature was incorporated. |
The central issue is whether the language accurately represents the evidentiary contribution of the revision.
A Limitation Statement Does Not Correct the Limitation
Authors frequently respond to reviewer concerns by acknowledging limitations more explicitly.
This can be scientifically appropriate, particularly when the limitation cannot be removed without collecting new data or fundamentally redesigning the study.
However, acknowledgment and correction are different activities.
For example, a manuscript may acknowledge that students self-selected into an educational technology intervention. This improves transparency about possible selection bias but does not establish that the bias has been removed.
AI-generated responses should not describe such clarification as a methodological correction unless an actual corrective procedure was performed and its implications verified.
Why Robustness Claims Require Particular Caution
Robustness is often discussed when authors conduct sensitivity analyses or estimate alternative statistical models.
However, one additional analysis cannot establish that findings are robust to every plausible analytical choice or assumption.
Suppose a study reports a positive association between learning management system engagement and academic performance. The authors estimate a second model using an alternative engagement measure and obtain a similar result.
This provides evidence about sensitivity to that particular operationalization.
It does not establish robustness to unmeasured confounding, selection bias, measurement error, or other assumptions not examined.
A response should identify which alternative was tested and what the results actually showed.
Statistical Significance Does Not Establish That a Concern Was Resolved
AI may describe a statistically significant result as confirming the validity of an analysis.
Yet statistical significance does not independently establish causal identification, measurement validity, practical importance, or correct model specification.
For example, an additional regression may produce a statistically significant coefficient while remaining vulnerable to omitted-variable bias.
Authors should avoid using p-values as general evidence that a reviewer concern has been fully addressed.
The American Statistical Association's guidance emphasizes that statistical significance should not be treated as a substitute for substantive interpretation or scientific reasoning.
Can AI Overstate the Importance of Textual Revisions?
Yes. Language editing and methodological clarification can improve a manuscript, but their significance should not be exaggerated.
For example, changing "demonstrates" to "suggests" may appropriately qualify an interpretation. It does not necessarily resolve concerns about the adequacy of the underlying evidence.
Similarly, expanding a paragraph about instrument development may improve reporting without supplying new validity evidence.
Authors should distinguish changes to the presentation of research from changes to the research procedures or evidence.
Partial Resolution May Be Scientifically Appropriate
A reviewer may identify a concern that cannot be fully resolved within the available research design.
For example, a study may lack a baseline measure needed to address certain alternative explanations. The authors cannot legitimately invent that measure after data collection.
They may instead revise their claims, acknowledge the limitation, and explain the implications for interpretation.
This can be a responsible response even though the underlying limitation remains.
The objective is not necessarily to eliminate every limitation, but to ensure that the manuscript's conclusions remain proportionate to the available evidence.
Why Reviewers Need to Know What Remains Unresolved
Editors and reviewers use response letters to evaluate whether the manuscript is ready for publication or requires further revision.
If the letter overstates the completeness of a response, they may underestimate remaining methodological uncertainty.
For example, an editor might interpret "the confounding issue has been fully addressed" as indicating that the authors performed an appropriate adjustment.
If the authors only added a limitation statement, that interpretation would be misleading.
Accurate descriptions help editors distinguish corrected problems from acknowledged limitations.
Can AI Overstate the Resolution of Qualitative Concerns?
Yes. The problem is not limited to quantitative analysis.
A reviewer may ask authors to clarify how qualitative themes were developed and how contradictory cases influenced interpretation.
The authors may expand their methodological description but not revisit the underlying analysis.
An AI-generated response stating that the analytical framework was comprehensively revalidated would exaggerate the work performed.
Qualitative revisions should accurately distinguish reporting improvements from substantive reanalysis.
The appropriate description depends on the study's methodological tradition and actual analytical decisions.
Overstatement Can Occur Without an Invented Revision
This distinction is particularly important.
When AI invents a manuscript change, it describes work that does not exist. When AI overstates a response, it may describe genuine work but exaggerate its scope, significance, or completeness.
Invented revision
Claims that a manuscript change or analysis occurred when it did not.
Overstated revision
Describes a real change as more comprehensive or conclusive than the evidence supports.
Both can mislead editors and reviewers, but they require slightly different verification procedures.
The related problem of AI inventing manuscript changes in reviewer responses concerns the existence of the claimed work. Overstatement concerns what the work actually establishes.
How Can Authors Verify the Extent of Resolution?
Authors should compare each reviewer concern with the action taken and the evidence supporting that action.
The verification should ask whether the response accurately describes the work, whether the work addresses the underlying concern, and whether important limitations remain.
For example, a new sensitivity analysis may address one aspect of model dependence but leave other assumptions unexamined.
The response should identify that scope rather than claim comprehensive robustness.
Where the revision is partial, the authors should explain the remaining limitation without implying that the reviewer concern was ignored.
Should Authors Use Terms Such as "Fully Addressed"?
Such terms should be used cautiously.
A concern may be fully addressed when the requested clarification, correction, or analysis has been completed and the underlying issue has been adequately resolved.
However, authors should not equate completion of a requested action with elimination of all related scientific uncertainty.
For example, a reviewer request to clarify the sampling procedure may be fully satisfied through improved reporting, even though the study's sampling limitations remain.
The response should describe the specific completed action rather than rely on broad declarations of completeness.
What If the Reviewer Requested More Than the Authors Could Provide?
Authors may explain that a requested analysis or additional data collection is infeasible or inappropriate.
They should provide the scientific rationale and identify any alternative response, such as a narrower interpretation or expanded limitation.
AI may help organize this explanation, but it should not convert a justified disagreement into a claim that the original request was fully satisfied.
Respectful acknowledgment and accurate reporting are preferable to overstated reassurance.
Confidentiality and Disclosure Still Apply
Reviewer comments and manuscript revisions may contain unpublished information or material subject to journal restrictions.
Authors should verify the journal's AI-use policies and the tool's data-handling arrangements before submitting such content to an external AI service.
Where AI assistance is permitted, the final response should be independently verified and disclosed according to applicable journal requirements.
Disclosure does not excuse exaggerated scientific claims or inaccurate descriptions of revision completeness.
