Artificial Intelligence in Peer Review: A Bibliometric-Guided Thematic Review and a Task-Contingent Legitimacy Framework

Abstract

The rapid adoption of large language models (LLMs) has prompted extensive debate about their appropriate role in peer review, scholarly publishing’s primary quality-control mechanism. However, AI has not yet been formally approved as a peer-review tool by most academic journals. This study reviews the emerging AI-in-peer-review literature to identify research trends, synthesize empirical evidence across review tasks, and develop a conceptual framework for AI-assisted review. Using a PRISMA-guided Scopus search (176 records identified, 162 included), we combined three-layer content analysis (theme, editorial stance, and AI autonomy) with a synthesis of 18 empirical studies. The literature expanded from 6 records before 2023 to 45 records in the first half of 2026 and remains dominated by commentary and opinion (57%), with the remaining 43% comprising research studies, technical work, and reviews. Editorial perspectives are generally balanced, and authors overwhelmingly favor assistive, human-in-the-loop AI over human-only or full automation. Empirical evidence shows a task-contingent pattern: AI performs well on narrowly defined evaluative tasks (Pearson r > 0.9 in some settings) but less reliably when predicting editorial decisions (accuracy 40–67%; correlations as low as ρ = 0.00). AI legitimacy may depend more on task type than on any governance position, a pattern we formalize in a Task-Contingent Legitimacy framework offering a task-tiered policy approach and testable propositions.