Detecting AI-Generated Content in Academic Peer Reviews

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Authors: Siyuan Shen, Kai Wang
Cited by: 2

Abstract

The growing availability of large language models (LLMs) has raised questions about their role in academic peer review. This study examines the temporal emergence of AI-generated content in peer reviews by applying a detection model trained on historical reviews to later review cycles at International Conference on Learning Representations (ICLR) and Nature Communications (NC). We observe minimal detection of AI-generated content before 2022, followed by a substantial increase through 2025, with approximately 20% of ICLR reviews and 12% of Nature Communications reviews classified as AI-generated in 2025. The most pronounced growth of AI-generated reviews in NC occurs between the third and fourth quarter of 2024. Together, these findings provide suggestive evidence of a rapidly increasing presence of AI-assisted content in peer review and highlight the need for further study of its implications for scholarly evaluation.

Fields of Study & Subjects

Expert finding and Q&A systems Academic Publishing and Open Access Student Assessment and Feedback

Publication details

Journal / source
arXiv (Cornell University)
Publication date
30 January 2026
Document type
Preprint