Hands-Off or Hands-On? Variation in Area Chair Practices and Implications for AI Support

2026-08-07

AI Summary

This study addresses the challenges faced by Area Chairs (ACs) in AI conference peer review, including heavy workloads, heterogeneous reviewer expertise, and complex coordination demands, compounded by a lack of systematic understanding of their practical needs and receptivity to AI tools. Through semi-structured interviews and design probes with 27 ACs, the research uncovers two distinct reviewing paradigms—“hands-off” and “hands-on”—challenging the prevailing assumption of AC role homogeneity. Building on these insights, the work proposes three human-centered AI design principles: accommodating diverse AC practices, supporting coordination of reviewer discussions, and ensuring human primacy in final decision-making. These contributions lay a theoretical foundation for developing more effective AI-augmented peer review support systems.

Abstract

Area chairs (ACs) play a critical role in the peer-review process, managing conflicts and ensuring fair outcomes. Although AI tools have been proposed to support ACs, little is known about the challenges they face and their perceptions of these technologies. In this paper, we conduct interviews including a design probe with 27 ACs in AI to explore their challenges, strategies, and perspectives on potential AI tools. Through thematic analysis, we identify key tensions arising from the growing volume of submissions, uneven reviewer expertise, and the complex task of managing the relationship between reviewers and authors. Most importantly, we find substantial variation in how ACs engage with submissions and influence outcomes: some adopt a largely hands-off approach, while others take a more hands-on role in guiding discussions and decisions. This variation challenges the notion of a single, universal AC practice and highlights the need to account for diverse approaches. When reflecting on the potential use of AI tools, ACs expressed a cautious stance, drawing on their domain knowledge and heightened awareness of AI limitations. From these findings, we derive three design implications: tailoring AI assistance to diverse AC practices, design assistance for discussion moderation, and embedding human-centered AI principles that preserve human agency in decision-making.

Problem

• Area Chairs
• peer review
• AI support
• human-AI collaboration
• academic reviewing

Innovation

• Area Chairs
• AI-assisted peer review
• human-centered AI
• design implications
• review moderation