Enhancing peer review efficiency: A mixed-methods analysis of artificial intelligence-assisted reviewer selection across academic disciplines

Abstract

This mixed-methods study evaluates the efficacy of artificial intelligence (AI)-assisted reviewer selection in academic publishing across diverse disciplines. Twenty journal editors assessed AI-generated reviewer recommendations for a manuscript. The AI system achieved a 42% overlap with editors' selections and demonstrated a significant improvement in time efficiency, reducing selection time by 73%. Editors found that 37% of AI-suggested reviewers who were not part of their initial selection were indeed suitable. The system's performance varied across disciplines, with higher accuracy in STEM fields (Cohen's d = 0.68). Qualitative feedback revealed an appreciation for the AI's ability to identify lesser-known experts but concerns about its grasp of interdisciplinary work. Ethical considerations, including potential algorithmic bias and privacy issues, were highlighted. The study concludes that while AI shows promise in enhancing reviewer selection efficiency and broadening the reviewer pool, it requires human oversight to address limitations in understanding nuanced disciplinary contexts. Future research should focus on larger-scale longitudinal studies and developing ethical frameworks for AI integration in peer-review processes.