Journal of Scholarly Communication

Vol. 1 (2026)
Research Articles

Generative AI in Peer Review: An Evaluation of Capabilities, Limitations, and Responsible Implementation Strategies

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  • Zhongshi Wang,
  • Mengyue Gong
Zhongshi Wang
Editorial Department of Systems Microbiology and Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi 214122, China
Mengyue Gong
Editorial Department of Systems Microbiology and Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi 214122, China

Published 2026-09-08 — Updated on 2026-09-09

Keywords

  • Generative AI; Large language models; Peer review

Copyright (c) 2026 Zhongshi Wang, Mengyue Gong (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Wang, Z., & Gong, M. (2026). Generative AI in Peer Review: An Evaluation of Capabilities, Limitations, and Responsible Implementation Strategies. Journal of Scholarly Communication, 1. https://doi.org/10.62160/JSC44

Abstract

Generative artificial intelligence (GenAI) has been increasingly integrated in academic publishing process. This study aims to access the performance of GenAI in peer review using multi-disciplinary manuscript samples. Five GenAI models underwent peer review tests, systematically evaluated for completeness, accuracy, and rationality of responses using combined manual scoring and statistical analysis. Findings indicated high accuracy in summarizing manuscript content and identifying suitable journals. However, GenAI models exhibited significant limitations in identifying scientific errors and evaluating paper innovation. The results demonstrate clear competency boundaries for GenAI within the peer review, particularly concerning tasks requiring nuanced professional judgment. This study clarifies the potential application scope of GenAI in peer review and provides a basis for its responsible future implementation.

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