Artificial Intelligence in Scholarly Peer Review: Ethical Considerations, Current Practices, and Future Implications
International Review of Research in Open and Distributed Learning, vol.27, no.3, pp.11-30, 2026 (SSCI, Scopus)
- Publication Type: Article / Article
- Volume: 27 Issue: 3
- Publication Date: 2026
- Doi Number: 10.19173/irrodl.v27i3.10081
- Journal Name: International Review of Research in Open and Distributed Learning
- Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Agricultural & Environmental Science Database, EBSCO Education Source, Education Abstracts, ERIC (Education Resources Information Center), Directory of Open Access Journals, Social Science Premium Collection (ProQuest), Education Collection (ProQuest), Education Source Ultimate (EBSCO)
- Page Numbers: pp.11-30
- Keywords: academic publishing, artificial intelligence (AI), publication ethics, research integrity, scholarly peer review
- Open Archive Collection: AVESIS Open Access Collection
- Anadolu University Affiliated: Yes
Abstract
The integration of artificial intelligence (AI) into scholarly peer review represents a fundamental transformation of academic publishing’s quality control mechanisms. This report critically examined the ethical considerations, institutional practices, and emerging technologies associated with AI-assisted peer review. Drawing on recent policy documents from major publishing organizations, empirical research on AI implementation, and critical scholarship on algorithmic bias, this analysis revealed significant tensions between efficiency gains and integrity preservation. While AI tools have demonstrated potential for addressing reviewer burnout and publication delays, their deployment raises critical concerns regarding confidentiality breaches, accountability gaps, algorithmic bias, and the erosion of expert judgment. Major organizations (e.g., International Committee of Medical Journal Editors) and leading publishers such as Elsevier and Taylor & Francis have emphasized disclosure where AI is used, human accountability, and strict confidentiality controls—often prohibiting uploading unpublished manuscripts into generative AI tools. However, empirical evidence has suggested nontrivial, and potentially growing, undisclosed large language model (LLM)-assisted text in peer review in some conference contexts. This report concluded that AI should serve as an augmentative rather than substitutive technology in peer review, with robust governance frameworks, transparent disclosure mechanisms, and continuous evaluation of equity implications essential for responsible implementation.