Who’s Doing the Reviewing? Tracing AI Delegation in Scientific Peer Review

Generative artificial intelligence is already being used in scientific peer review. Current work has focused largely on the reviews that AI helps produce, leaving much less understood about the human–AI process that produces them: how researchers consult, evaluate, adopt, and integrate AI assistance while reviewing a paper. This paper traces how 104 researchers with prior peer-review experience evaluated a paper while using an embedded AI assistant. We recorded AI queries, transfers of AI-generated text into their reviews, subsequent editing, engagement with the source paper, and review content. 66 used AI, and 47 asked it to generate review content. Reviewers who asked AI for review content but did not copy it spent about as much time with the paper as those who used AI for other epistemic purposes. By contrast, reviewers who copy-pasted AI-generated content spent about half as much time viewing the source paper as those who requested generated content but did not copy it. Among these reviewers, the pasted text was typically left nearly unchanged. Their reviews also showed greater similarity to other reviews of the same paper than those of generation users who did not copy. Additionally, reviewers who requested AI-generated content showed reduced ability to locate support in the paper for their own review points. Collectively, these findings indicate that “AI-assisted reviewing” encompasses multiple distinct behaviors. Among them, generating review content and, especially, directly adopting AI-generated judgments through copy-pasting cut against two core features of peer review: careful inspection and verification of manuscript evidence, and independent experts bringing distinct judgments to the same paper.