Generative AI in peer review: evolving policies and regulatory pathways for scientific rigour

Abstract

The rapid adoption of large language models (LLMs) and generative artificial intelligence (GenAI) is reshaping academic publishing and raising new questions about peer review integrity, transparency, and governance. This paper examines GenAI use across editorial and reviewer workflows and considers its implications for confidentiality, accountability, evaluative authority, and scientific rigour. Drawing on the literature on research integrity and AI governance, along with a targeted comparative review of major publisher and editorial policies, the analysis identifies an evolving shift from undifferentiated restriction toward more function-specific, risk-based governance. The analysis examines three regulatory pathways: transparent integration within journal-controlled workflows, conditional reviewer use with disclosure, and prohibition when adequate confidentiality, data-protection, or other safeguards cannot be assured. The analysis also highlights disclosure challenges, including under-reporting, reputational effects, and the difficulty of reliably identifying undeclared AI use, while recognizing reviewer workload and increasingly complex, interdisciplinary research as key drivers of AI-assisted review. Drawing on these findings, the paper develops a tiered regulatory framework based on the AI system's function and capabilities, manuscript sensitivity, institutional governance capacity, disclosure, human accountability, reviewer education, and proportionate oversight. GenAI should therefore be governed not as a uniform technology but according to how, where, and for what purpose it is used, while preserving human responsibility for reviewer judgments and final editorial decisions.