AI-generated peer review feedback lacks depth and specificity
An Australian academic received peer review feedback that appeared to be AI-generated, lacking specificity and depth needed for meaningful critique. The incident raises concerns about the use of AI in academic publishing and calls for transparency and opt-out protections for authors.
What happened
An academic researcher collaborating with the World Health Organisation received peer review comments on a manuscript in January 2025 that appeared to be AI-generated. The comments were generic and uniform in tone, lacked nuance, depth and specificity, contained no page or line numbers, and provided no specific examples to guide revision.
Reported impact
The reviewer's feedback was described as vague and confusing, and did nothing to improve the manuscript. The incident raised concerns about the integrity of the peer review process and the need for transparency and accountability in journals' use of AI.
Index analysis
The case highlights potential pitfalls when AI systems are used to generate academic peer review, including generic feedback, lack of nuance, and absence of specific guidance needed for meaningful manuscript improvement.
Governance controls
Journals and editors are called to establish stringent guidelines and oversight to ensure AI is used effectively in peer review, with authors being informed and offered the choice to opt out of AI-assisted reviews.
Classification
Sources and evidence
This record was researched and written by the Index, based on the following original reporting.