AI in the gatekeeper’s chair: elite researchers’ perceptions of AI-assisted feedback in journal peer review

Peer review serves as the structural foundation of scientific integrity, yet the system currently faces unprecedented strain. In response, AI tools are being deployed to assist human expert review, a development that has generated considerable debate. However, the psychological impact of this transition on the scientific community remains only partially understood. Our study utilises a sequential mixed-methods approach to examine the perceptions of AI-mediated feedback among a cohort of elite researchers (Nature and Science authors). Quantitative findings from our randomised experimental survey (N = 495) demonstrated that AI-assisted reviews were viewed as deficient in fairness, usefulness, and acceptance compared to human-led evaluations. Qualitative evidence from 47 in-depth interviews further identified a dual-layered aversion to AI-assisted feedback, concerning both the technology (AI use) and the agent (AI user). Specifically, scholars perceived AI-generated critiques as devoid of the necessary disciplinary nuance required for high-stakes evaluation. Moreover, a notable ‘AI user aversion’ emerged: reviewers who delegated tasks to AI were perceived as lacking the diligence and empathic engagement essential to the peer-review contract. Together, these finding suggest that the integration of AI into peer review may erode trust in the research evaluation process and journals should implement robust governance that preserves human oversight.