[2602.00319] Detecting AI-Generated Content in Academic Peer Reviews

Computer Science > Computation and Language arXiv:2602.00319 (cs) [Submitted on 30 Jan 2026 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Detecting AI-Generated Content in Academic Peer Reviews Authors:Siyuan Shen, Kai Wang View a PDF of the paper titled Detecting AI-Generated Content in Academic Peer Reviews, by Siyuan Shen and 1 other authors View PDF HTML (experimental) 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. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI) Cite as: arXiv:2602.00319...