03 · What You Need to Know
Why Producing a Peer-Review Report Is Different From Performing Peer Review
Generative AI Can Reproduce the Structure of a Reviewer Report
Academic peer reviews often follow recognizable conventions. They may begin with a brief description of the manuscript, identify major methodological or conceptual concerns, distinguish substantive revisions from minor editorial matters, and conclude with an overall assessment.
Generative AI can imitate this structure because it has learned patterns of scholarly language and document organization.
For example, a model may produce headings such as "Major Comments," "Minor Comments," and "Recommendations for Revision." It may use appropriate academic terminology and phrase criticisms in a professional tone.
However, conformity to the expected format does not establish that the underlying evaluation is sound.
A report can look complete while overlooking the manuscript's most consequential weakness or inventing problems that are not present.
Why an AI-Generated Review May Appear More Reliable Than It Is
Generative AI can produce confident and technically fluent criticism. This may encourage reviewers to treat its observations as authoritative, particularly when the comments concern unfamiliar statistical or methodological concepts.
For example, a model might state that a manuscript fails to address selection bias, lacks an appropriate power analysis, or uses an unsuitable statistical procedure.
Each concern may be legitimate in some studies. Yet the model may not have established that it applies to the manuscript being reviewed.
The problem is not simply that AI can make mistakes. It is that the resulting report may provide few visible indications of which criticisms are grounded in the manuscript and which are generic or unsupported.
Reviewers should therefore distinguish the apparent completeness of the report from the evidentiary quality of its claims.
What Can Go Wrong in an Automatically Generated Peer Review?
Several failure modes may occur when a model is asked to evaluate a manuscript comprehensively.
| Potential Failure |
Illustrative Example |
Possible Consequence |
| Invented criticism |
Claims that the authors omitted an analysis that is actually reported |
Unnecessary revision requests |
| Generic criticism |
Requests a larger sample without considering the study design |
Irrelevant methodological demands |
| Misinterpretation |
Treats an observational association as a causal claim when the authors did not |
Unfair assessment of the manuscript |
| Omission |
Overlooks a consequential problem with the primary outcome measure |
False reassurance about research quality |
| Fabricated reference |
Invents a methodological citation to support a criticism |
Misleading scholarly justification |
| Inappropriate recommendation |
Demands randomized assignment for a descriptive study |
Requests inconsistent with the research purpose |
| Overconfident judgment |
Declares the study invalid without examining relevant qualifications |
Potentially distorted editorial decisions |
These are plausible failure modes, not claims that every AI-generated review will contain them. Their significance depends on the model, task, and evidence available.
Nevertheless, they illustrate why generated reviewer reports require more scrutiny than ordinary language-editing suggestions.
Can AI Identify Genuine Methodological Weaknesses?
AI may suggest questions about sampling, measurement, statistical assumptions, confounding, or interpretation.
Some suggestions may direct attention to genuine problems. Others may be incorrect or irrelevant.
For example, a model may recommend examining whether a study accounts for classroom-level clustering. This could be important in educational research involving students nested within classes.
However, the recommendation would be inappropriate if the model ignored an analytical procedure that already accounted for clustering or misunderstood the unit of analysis.
The reviewer must therefore verify whether AI-suggested methodological weaknesses are actually supported by the manuscript.
A plausible question is not the same as a demonstrated defect.
AI May Criticize a Manuscript for Not Being a Different Kind of Study
One particularly problematic form of automated criticism occurs when a model evaluates a study against methodological expectations that do not match its purpose.
A qualitative interview study may be criticized for lacking statistical generalizability. An exploratory study may be faulted for not testing prespecified hypotheses. A descriptive analysis may be criticized for failing to establish causality.
These criticisms can confuse the limitations of a design with failures to conduct the intended study appropriately.
A sound peer review asks whether the chosen methods are suitable for the research question and whether the conclusions remain within the evidence.
It does not automatically demand that every manuscript adopt a preferred methodological tradition.
Why Manuscript Confidentiality May Prohibit Automatic Review Generation
Producing a manuscript-specific AI review ordinarily requires supplying information about the unpublished submission.
That may involve uploading the PDF, pasting sections, providing supplementary files, or entering detailed descriptions of the study.
Submitted manuscripts are generally confidential editorial communications. Reviewers do not automatically have authority to transfer them to an external AI service.
The International Committee of Medical Journal Editors (ICMJE) recommends that reviewers follow the journal's AI policy or request permission before using AI to facilitate peer review. Its guidance emphasizes confidentiality and accountability.
Some publishers prohibit uploading manuscripts or manuscript-derived information to generative AI tools.
Therefore, uploading a confidential manuscript for AI-generated review may be prohibited regardless of whether the resulting report appears useful.
Watch Out
Do not assume that removing author names makes a manuscript safe for AI processing. Unpublished findings, distinctive methods, datasets, and other scientific information may remain confidential even when the submission is blinded.
Does Using an Enterprise or Private AI Tool Change the Answer?
Different AI systems may provide different data protection arrangements. Some offer contractual restrictions on training, controlled retention, institutional access management, or local processing.
These arrangements may reduce particular confidentiality risks, but they do not automatically authorize the use of a manuscript for AI-generated peer review.
A journal may prohibit automated manuscript assessment by reviewers regardless of the tool's privacy features.
Reviewers should establish whether the journal permits the specific activity and whether the proposed processing environment meets its requirements.
Permission to use AI for language editing does not necessarily extend to asking the system to evaluate the complete manuscript.
Can a Reviewer Submit an AI-Generated Report After Editing It?
Editing an AI-generated review does not automatically resolve the underlying ethical or methodological concerns.
If the manuscript was uploaded without authorization, revising the resulting text does not reverse the disclosure.
If the generated report contains unsupported criticisms, rewriting them in the reviewer's own words does not make them valid.
The reviewer must independently examine the manuscript, understand the basis for each concern, and ensure that the final report reflects their own scholarly assessment.
Even substantial editing may be insufficient if the reviewer has effectively delegated the scientific evaluation to a system whose reasoning and evidence were not independently checked.
Can AI-Generated Reviews Introduce Bias?
AI-generated assessments may reflect patterns in the material used to develop the model, including preferences for familiar research designs, conventional scholarly language, or frequently represented methodological approaches.
This could disadvantage unconventional but defensible studies or research conducted in less represented contexts.
For example, a model may interpret unfamiliar educational practices as methodological weaknesses simply because they differ from commonly represented examples.
Such risks should not be assumed to occur uniformly across models or tasks. They require empirical evaluation.
Nevertheless, reviewers should avoid treating AI-generated judgments as neutral merely because they are produced by software.
The broader issue of AI bias in peer-review decisions becomes especially consequential when generated assessments influence editorial recommendations.
Can AI Reliably Recommend Acceptance, Revision, or Rejection?
Generative AI can produce recommendations resembling editorial judgments, but the presence of a recommendation does not establish that it is justified.
Decisions about manuscript suitability involve disciplinary significance, methodological adequacy, evidentiary support, journal scope, and editorial standards.
These considerations cannot be reduced to whether the model produces a persuasive summary of strengths and weaknesses.
Furthermore, reviewers ordinarily provide recommendations within an editorial process in which the editor retains decision-making authority.
An AI-generated acceptance or rejection recommendation should not substitute for independent scholarly evaluation or the journal's authorized editorial procedures.
What Is the Difference Between AI-Assisted Writing and AI-Generated Peer Review?
The distinction concerns who determines the substance of the assessment.
AI-assisted review writing
The reviewer independently evaluates the manuscript and uses permitted AI assistance to improve the presentation of established comments.
AI-generated peer review
The AI system generates substantial criticisms, evaluations, or recommendations from manuscript information.
The second activity raises stronger concerns about delegated judgment, unsupported criticism, and confidential manuscript processing.
Even the first requires compliance with journal policy and verification of the final text.
The distinction is important when considering AI-assisted improvement of reviewer comments.
Could Journals Use AI as Part of an Authorized Editorial Workflow?
Some journals or publishers may investigate AI-assisted editorial screening, reporting checks, or other administrative functions within approved systems.
Such arrangements differ from an individual reviewer independently uploading a confidential manuscript to a general-purpose AI service.
An authorized editorial workflow may involve defined access controls, contractual safeguards, disclosure requirements, validation procedures, and human oversight.
However, institutional authorization does not itself establish that an AI assessment is accurate or unbiased.
Any automated evaluation used in consequential editorial decisions should be assessed for reliability, fairness, and suitability for its intended purpose.
What Must Remain the Human Reviewer's Responsibility?
The reviewer must understand the manuscript, evaluate its evidence, and determine which concerns are scientifically justified.
They must distinguish substantial weaknesses from minor reporting issues, avoid unsupported demands, and communicate criticism fairly.
They must also protect confidential information and follow the journal's disclosure requirements.
These responsibilities cannot be transferred to a generative AI system simply because it can produce a document resembling a peer-review report.
The central issue is which parts of peer review require independent human judgment and how any authorized technological assistance can support, rather than displace, that judgment.
