03 · What You Need to Know
Why Understanding a Criticism Is Essential to Responsible Peer Review
Peer Review Requires Defensible Judgment, Not Merely Plausible Comments
Peer reviewers are invited because their expertise can contribute to an informed evaluation of submitted research. Their responsibility is not simply to produce a list of possible weaknesses.
A useful criticism identifies a problem, explains its scientific significance, and indicates what the authors need to clarify or address.
For example, a reviewer who questions the appropriateness of a statistical model should understand which assumption may have been violated and how that issue could affect the reported estimates.
Without this understanding, the reviewer cannot reliably distinguish a substantive weakness from a generic recommendation.
The Committee on Publication Ethics (COPE) emphasizes that reviewers should provide objective, constructive assessments and recognize the limits of their expertise. These responsibilities remain relevant when AI contributes suggestions.
Why AI-Generated Technical Language Can Be Misleading
Generative AI can produce fluent explanations using specialized methodological terminology. The resulting criticism may resemble one written by an experienced statistician or disciplinary expert.
However, fluency does not establish that the model has correctly understood the research design or applied the relevant methodological principle.
For instance, an AI system might recommend multilevel modeling whenever students are nested within classrooms. This may be appropriate in some studies, but the actual need depends on the estimand, sampling structure, dependence among observations, and analytical strategy.
The model might overlook that the authors already used cluster-robust standard errors or another defensible approach.
A reviewer unfamiliar with these distinctions may repeat the recommendation without realizing that the manuscript has already addressed the concern.
Understanding a Concept Is Different From Recognizing Its Name
Academic terminology can create an illusion of comprehension. A reviewer may recognize a term without understanding its assumptions or implications.
Consider the following examples.
| AI-Generated Concern |
What the Reviewer Needs to Understand |
| "The model suffers from endogeneity." |
What source of endogeneity is plausible, which parameter is affected, and why the research design does not adequately address it. |
| "Measurement invariance was not established." |
Whether the study compares latent constructs across groups and which level of invariance is necessary for the intended comparison. |
| "The analysis violates independence assumptions." |
Which observations may be dependent, what procedure was used, and how dependence could affect inference. |
| "The sample size is statistically inadequate." |
Which inferential objective requires greater precision or power and whether the criticism applies to the study design. |
| "The authors should calculate inter-rater reliability." |
Whether coder agreement is appropriate within the qualitative methodology actually used. |
Each statement could be relevant in a particular study. None is automatically justified merely because it contains recognized methodological terminology.
The Reviewer Must Connect the Criticism to Actual Manuscript Evidence
A defensible criticism requires a connection between the alleged problem and the manuscript's reported procedures or findings.
Suppose AI suggests that a study has not addressed missing data. The reviewer should examine whether the authors reported missingness, used complete-case analysis, applied multiple imputation, or described another strategy.
If the manuscript does not provide enough information, the appropriate comment may be a request for clarification rather than an accusation that the analysis was conducted incorrectly.
The distinction matters because incomplete reporting and methodological failure are not equivalent.
Reviewers should avoid transforming an AI-generated suspicion into a definitive criticism without examining the evidence.
Can Reviewers Use AI to Learn About an Unfamiliar Method?
Potentially, when permitted by the journal and without unauthorized disclosure of confidential manuscript information.
A reviewer might seek a general explanation of measurement invariance, propensity score weighting, or the assumptions underlying a particular regression model.
Such explanations may help the reviewer identify which authoritative sources to consult.
However, an AI explanation should not be treated as a definitive methodological reference. The reviewer should verify the concepts through established statistical literature, methodological textbooks, or appropriate reporting guidance.
If the issue remains beyond the reviewer's competence, the reviewer should acknowledge that limitation rather than rely on an AI-generated explanation as a substitute for expertise.
When Should the Reviewer Request Specialist Assessment?
Not every reviewer is expected to possess expertise in every aspect of a multidisciplinary manuscript.
An educational technology study may combine learning analytics, psychometric measurement, qualitative inquiry, and complex statistical modeling. A reviewer may be qualified to assess its educational significance while lacking the expertise needed to evaluate a specialized estimation procedure.
When an unfamiliar issue could materially affect the manuscript's conclusions, the reviewer may inform the handling editor that additional methodological or statistical review would be appropriate.
This is more defensible than presenting an unverified AI-generated criticism as an authoritative judgment.
Recognizing the limits of one's expertise is part of responsible peer review, not a deficiency to be concealed.
What Happens When an Unsupported Criticism Enters the Review?
Incorrect reviewer comments can create consequences beyond an awkward exchange between authors and reviewers.
Authors may spend considerable time conducting unnecessary analyses, collecting additional data, or revising defensible methodological decisions.
Editors may also give weight to an apparently technical criticism when assessing whether the manuscript requires major revision or rejection.
If the criticism is unsupported, the review process may become less fair and less efficient.
For this reason, confident but irrelevant AI-generated reviewer comments deserve particular scrutiny.
AI May Recommend a Different Method Rather Than Identify a Defect
A common problem is treating methodological alternatives as mandatory corrections.
For example, AI may recommend structural equation modeling instead of regression analysis, or a randomized trial instead of an observational design.
These alternatives may answer different questions or require assumptions that are not justified by the study.
A reviewer should ask whether the existing method is inappropriate for the stated objective, not merely whether another method could also have been used.
Methodological preference should not be presented as methodological necessity.
Confidentiality Still Applies When Investigating AI Criticisms
Reviewers should not upload confidential manuscript material to an external AI service merely to obtain an explanation of an unfamiliar criticism.
The International Committee of Medical Journal Editors (ICMJE) advises reviewers to follow the journal's AI policy or seek permission before using AI to facilitate review, while protecting manuscript confidentiality.
Questions containing unpublished results, distinctive methods, or detailed reviewer observations may disclose protected information even when the manuscript PDF is not uploaded.
Where permitted, reviewers may formulate general methodological questions without revealing manuscript-specific details.
The restrictions discussed in AI use and peer-review confidentiality remain applicable.
How Can a Reviewer Decide Whether a Criticism Is Ready to Include?
A useful test is whether the reviewer can answer four questions independently.
What exactly is the alleged problem? Where does the manuscript provide evidence of it? Why does the problem matter for the research question or conclusions? What response or revision would reasonably address it?
If the reviewer cannot answer these questions, the concern may require further investigation.
If the evidence remains uncertain, the comment should reflect that uncertainty. A request for clarification may be justified even when a definitive accusation is not.
If the reviewer cannot establish the relevance of the concern, it should not be included as a methodological weakness.
Does Independent Verification Require Reproducing Every Calculation?
Not necessarily. The appropriate verification depends on the nature of the criticism and the available evidence.
A straightforward arithmetic discrepancy may be checked through independent calculation. A concern about causal inference may require evaluating the design and identification assumptions. A qualitative methodological issue may require examining the coherence between the research approach and the analytical procedures.
Reviewers should use verification methods appropriate to the claim rather than assume that every concern can be resolved through a numerical calculation.
For specialized statistical concerns, independent verification of statistical claims may require reproducible software or additional information from the authors.
Why Responsibility Cannot Be Delegated to the AI System
The reviewer is accountable for the assessment submitted to the journal. AI systems are not independent scholarly reviewers who can answer editorial questions, defend methodological criticisms, or accept responsibility for unsupported recommendations.
Even when a journal authorizes limited AI assistance, the reviewer must understand the final comments and verify their evidentiary basis.
This reflects a broader principle: substantive scholarly judgment remains a human responsibility in peer review.
AI may contribute questions or explanations, but it cannot relieve the reviewer of the obligation to determine whether those contributions are scientifically defensible.
06 · What This Means for You
What to Do When AI Suggests a Problem Outside Your Expertise
The appropriate response depends on whether the concern can be understood and verified through reasonable investigation.
Reviewers should avoid two extremes: accepting the AI's criticism without scrutiny and dismissing every unfamiliar suggestion merely because it requires additional learning.
A practical decision framework
If you understand and can verify the criticism
Include it when relevant, supported by the manuscript, and proportionate to the methodological issue.
If the concept is unfamiliar but can be investigated
Consult authoritative methodological references and independently assess whether the concern applies.
If the manuscript lacks enough information to determine whether a problem exists
Request specific clarification rather than presenting the suspected issue as a demonstrated defect.
If the concern requires expertise beyond your competence
Inform the handling editor and suggest additional specialist review where warranted.
If you cannot substantiate the AI-generated criticism
Do not include it as an established methodological weakness.
Separate Intellectual Curiosity From Reviewer Authority
AI-generated suggestions can introduce reviewers to unfamiliar methodological concepts. That may be educationally useful.
However, learning that a particular statistical issue exists is not the same as establishing that it invalidates a manuscript.
The reviewer should develop sufficient understanding before making a substantive claim, especially when the criticism could influence the editorial decision.
Watch Out
Do not use AI-generated technical language to conceal uncertainty about your own understanding. A reviewer comment should communicate a concern you can explain and substantiate, not merely reproduce a sophisticated-sounding recommendation.
Peer review benefits from reviewers who recognize both the strengths and limits of their expertise. When a concern cannot be independently evaluated, seeking appropriate specialist assessment is more defensible than outsourcing scholarly judgment to a language model.
