Detecting AI-Generated Content in Academic Peer Reviews

Siyuan Shen, Kai Wang
1/30/2026
cs.CLcs.AIcs.LG

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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.

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Cite this paper

@article{shen2026detecting,
  title  = {Detecting AI-Generated Content in Academic Peer Reviews},
  author = {Siyuan Shen and Kai Wang},
  year   = {2026},
  eprint = {2602.00319},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.00319v1}
}

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