This research scan is the first part of a series that will examine how artificial intelligence (AI) technologies are impacting scholarly communication. This post surveys the current discourse surrounding GenAI’s place within the peer review process. Primary academic concerns at the advent of GenAI revolved around ideas of authorship, plagiarism, and academic transparency, and these issues still drive a passionate conversation. However, as GenAI’s presence in society has regularized, academics have come to understand how this technology might aid in tasks beyond composition, driving a renewed discussion about GenAI’s role within the peer review workflow as an editorial aid. For instance, with proper oversight, GenAI stands to support under-resourced journals in administrative tasks (e.g., matching articles to reviewers and first-round copyediting), giving editors more time for those tasks that require human judgement and creativity. Of course, the introduction of GenAI into these workflows raises its own issues that warrant their own nuanced discussion. Below, we outline core considerations in this discussion, drawing on the latest research on the subject; we also make recommendations for how journals might approach implementing AI in peer review and provide annotations of key sources for further reading.

Please see 'Notes' below for any suggested/official citation information added by the author(s) of this work. Otherwise, you can cite the HSS Commons instance of this publication as follows:

• Dase, K., Colin-Arce, A., Forghan Parast, F. F., Siemens, R., (2026), "AI in Peer Review: A Targeted Research Scan", HSSCommons: (DOI: 10.25547/ZSTN-3Q98)

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1. peer review
2. publishing