Which Parts of Peer Review Require Irreducible Human Judgment?

If AI Can Evaluate a Manuscript, What Must the Human Reviewer Still Do?

Generative AI can summarize a manuscript, identify possible methodological weaknesses, check familiar reporting conventions, and produce a structured reviewer report. Some systems can also suggest statistical concerns or compare a study's claims with general methodological principles.

These capabilities raise an uncomfortable question for scholarly publishing. If AI can perform many activities associated with peer review, what exactly remains the responsibility of the human reviewer?

The answer cannot simply be that humans are better at everything. Automated systems may perform particular bounded tasks efficiently, and human reviewers are themselves susceptible to error, inconsistency, and bias.

Instead, the distinction concerns the kinds of scholarly judgments that require disciplinary interpretation, contextual understanding, responsibility for consequences, and the ability to defend an assessment within an editorial process.

Peer review is not merely the production of comments. It is an accountable evaluation of what a study establishes, why its contribution matters, and whether the criticism directed toward it is justified.

Human Judgment Remains Essential Where Scientific Evaluation Requires Interpretation and Accountability

Where Human Scholarly Judgment Matters Most in Peer Review

Peer Review Is an Evaluative Practice, Not Merely a Technical Procedure

Peer review involves examining whether research methods, evidence, interpretations, and contributions are appropriate to the questions being investigated.

Some aspects can be assessed through relatively explicit criteria. For example, a reviewer may check whether a manuscript reports an ethics approval statement or provides a sufficient description of its sampling procedure.

Other judgments are less readily reducible to fixed rules.

Determining whether a methodological limitation undermines the central conclusion requires understanding what the authors intended to establish, what their design permits, and how uncertainty affects the interpretation.

Similarly, evaluating originality requires considering the relationship between a manuscript and existing scholarship rather than merely detecting differences in terminology.

AI may provide relevant information, but producing an assessment is not equivalent to assuming responsibility for its scientific justification.

Which Peer-Review Tasks Most Clearly Require Human Judgment?

The distinction is not between tasks that AI can technically attempt and tasks it cannot generate text about. It concerns which decisions require an accountable expert to establish their validity.

These responsibilities do not imply that one reviewer must possess expertise in every relevant discipline. Where specialist knowledge is necessary, requesting additional assessment may be more appropriate than attempting to compensate through AI-generated expertise.

Judging Scientific Significance Requires Disciplinary Context

A manuscript's contribution cannot always be determined by comparing its terminology with existing publications.

A study may use familiar methods while producing important evidence from an underexamined population. Another may introduce technically sophisticated procedures without answering a meaningful research question.

For example, an educational technology study examining offline digital learning in geographically isolated schools may not introduce a new algorithm. Its contribution may instead concern how technological constraints reshape instructional participation and equity.

An AI assessment focused heavily on technical novelty could underestimate that contribution.

The reviewer must consider the study's significance within its disciplinary, theoretical, and practical context.

Methodological Quality Cannot Be Reduced to One Universal Checklist

Different research traditions establish credibility through different methodological practices.

A randomized trial may require careful assessment of allocation, intervention fidelity, attrition, and statistical estimation. An interpretive qualitative study may require attention to reflexivity, analytical coherence, and the relationship between interpretation and evidence.

Neither design should automatically be judged according to criteria developed for the other.

Generative AI may reproduce common methodological recommendations without recognizing their limits.

For example, a demand for inter-rater reliability may be appropriate in some coding-reliability approaches but inconsistent with the assumptions of reflexive thematic analysis.

The human reviewer must establish which standards are appropriate rather than treating methodological familiarity as methodological superiority.

Interpreting Evidence Requires More Than Identifying Statistical Results

Statistical calculations may be reproducible, but their scientific interpretation depends on the research design and assumptions.

For example, a statistically significant association between learning management system engagement and academic performance does not independently establish that platform use caused higher grades.

The reviewer must consider potential confounding, temporal ordering, measurement validity, and the strength of the authors' claims.

AI may suggest these questions, but it cannot relieve the reviewer of establishing which concerns apply to the actual manuscript.

The same principle applies to qualitative interpretation, where multiple defensible readings may exist and the reviewer must assess their coherence and evidentiary support.

Distinguishing a Limitation From a Fatal Flaw Requires Judgment

Every research design has limitations. The presence of a limitation does not automatically make a study unsuitable for publication.

A single-institution study may have limited transferability. A cross-sectional design may constrain causal interpretation. A qualitative study may prioritize contextual understanding rather than population estimation.

These limitations become consequential when they undermine claims the authors actually make.

A reviewer must determine whether the appropriate response is clearer acknowledgment, additional analysis, narrower interpretation, or a more fundamental reconsideration of the findings.

AI-generated criticism may overlook this distinction and recommend extensive revisions where a qualified limitation statement would suffice.

Reviewers Must Decide Whether a Requested Revision Is Proportionate

Peer review can impose substantial demands on authors. Requests for additional analyses, new data collection, or alternative research designs may require considerable time and resources.

These requests should be justified by their importance to the manuscript's central claims.

For example, asking authors to conduct a randomized trial to replace an appropriately framed observational study may effectively demand a different research project.

By contrast, requesting that the authors revise unsupported causal language may directly address the relevant weakness.

The reviewer must distinguish necessary corrections from methodological preferences.

This judgment is particularly important when AI produces plausible but irrelevant reviewer comments.

Evaluating Novel Research Requires Openness to Unfamiliar Approaches

Unconventional research can be difficult to assess because it does not always conform to established expectations.

A novel theoretical perspective or context-specific methodology may initially appear unusual without being scientifically weak.

AI systems may reproduce familiar scholarly patterns and could favor conventional approaches under some conditions.

However, human reviewers may also exhibit conservatism or disciplinary bias. Human judgment is not inherently free from error.

The responsibility is to evaluate unfamiliar work fairly, seek additional expertise where necessary, and avoid treating deviation from convention as sufficient evidence of poor quality.

Ethical Evaluation Requires Evidence and Procedural Fairness

Reviewers may encounter concerns involving participant protection, informed consent, data integrity, undisclosed conflicts of interest, or potentially inappropriate research practices.

AI may identify possible reporting omissions or unusual patterns, but such observations do not establish misconduct.

A reviewer should distinguish a request for clarification from an allegation of unethical conduct.

When serious concerns arise, the appropriate response is generally to inform the handling editor through the journal's established procedures rather than make unsupported accusations in comments to authors.

Scientific integrity requires both careful examination of evidence and fairness toward the researchers being evaluated.

Why AI Cannot Assume Responsibility for a Reviewer Report

Generative AI can produce a report that resembles expert criticism, but it cannot accept the professional obligations attached to a peer-review invitation.

It cannot independently assume accountability to the editor, respond as an invited scholarly expert, or accept responsibility for an unfair recommendation.

The reviewer must understand and verify the concerns submitted under their name.

This is why reviewers should not rely on AI-generated criticisms they cannot personally understand.

The boundary concerns accountable scholarly authority, not merely whether a model can produce a sophisticated explanation.

Meaningful Human Oversight Is Not a Final Approval Button

Human oversight can become superficial when a reviewer or editor accepts an AI-generated assessment without independently examining the evidence.

This may produce automation bias, particularly when the model presents a confident recommendation or numerical quality score.

Meaningful oversight requires the human decision-maker to understand the task, assess the supporting evidence, recognize the possibility of error, and possess genuine authority to reject the recommendation.

For consequential assessments, independent review before exposure to AI-generated suggestions may help reduce anchoring, although its effectiveness should be evaluated rather than assumed.

Human involvement should therefore be assessed by the quality of the judgment exercised, not simply by the presence of a person in the workflow.

Can AI Support Human Judgment Without Replacing It?

Potentially, when the journal permits the intended use and confidentiality is protected.

AI may assist with organizing independently developed comments, explaining general methodological concepts, or suggesting questions that the reviewer subsequently verifies.

These activities can support the reviewer's preparation without necessarily determining the substantive assessment.

However, even language editing may change the certainty or severity of a criticism. Reviewers must examine the final wording carefully.

The broader question of generative AI assistance in peer-review writing therefore depends on both editorial authorization and independent scholarly verification.

Confidentiality Places an Independent Limit on AI Assistance

Peer-review manuscripts are ordinarily confidential editorial communications.

Reviewers should not upload unpublished manuscript material to external AI services without the journal's required authorization and appropriate safeguards.

The International Committee of Medical Journal Editors (ICMJE) advises reviewers to follow the journal's AI policy or request permission before using AI to facilitate peer review.

Even when AI could help identify a legitimate methodological concern, unauthorized processing of the manuscript may violate confidentiality obligations.

Scientific usefulness does not override the conditions under which reviewers receive access to unpublished research.

Does Human Judgment Mean AI Can Never Contribute to Editorial Decisions?

No. The appropriate distinction is between decision support and the delegation of accountable authority.

An authorized AI system may help editors identify administrative omissions or flag possible issues requiring specialist examination.

However, consequential editorial decisions should remain grounded in evidence that responsible editors can independently evaluate and defend.

The question is not whether AI influenced the process at all, but whether its contribution was appropriate, validated, and subject to meaningful oversight.

This distinction also applies to AI-assisted editorial manuscript evaluation.

When AI Recommends Rejection but Human Judgment Finds a Defensible Study

The example illustrates why scholarly judgment requires attention to the research question, methodological tradition, evidentiary support, and significance of the contribution.

It does not establish that qualitative studies are exempt from criticism. Rather, it demonstrates that the criticism must be appropriate to the study being evaluated.

Misconceptions About Human Judgment and AI in Peer Review

How to Preserve Meaningful Human Judgment in AI-Assisted Peer Review

The most defensible approach is to establish the reviewer's independent assessment before considering any authorized AI suggestions.

Reviewers should identify the study's contribution, evaluate its methods and evidence, and determine which concerns are scientifically consequential.

Evaluate What the Reviewer Actually Contributes

A human reviewer adds value by identifying which questions matter, assessing the relationship between methods and claims, and determining whether criticisms are fair and proportionate.

The reviewer also accepts responsibility for communicating those judgments to the editor.

These responsibilities should not be obscured by an AI-generated report that appears comprehensive merely because it contains many comments.

The appropriate boundary between AI assistance and human judgment is determined by the nature of the scientific decision, the reliability of the assistance, and the obligations of the editorial process.

Checklist for Preserving Human Judgment in Peer Review

Frequently Asked Questions About Human Judgment in Peer Review

Peer Review Requires Someone Who Can Defend the Judgment

How to Cite This Guide

This guide is intended to be read, shared, and used in research, teaching, and academic work. If you draw on its ideas, explanations, or other content, please acknowledge the source by citing the guide. Doing so gives appropriate credit and helps your readers locate the original resource.