Disciplinary and Institutional Governance of AI in Academic Research

Why this matters

The rapid adoption of AI tools in research is outpacing the development of disciplinary norms, institutional policies, and governance frameworks. Researchers across all fields are navigating inconsistent and often absent guidelines for AI use in writing, reviewing, and publishing, creating integrity risks and inequitable access to AI benefits. Establishing evidence-based governance frameworks is a field-wide priority with implications for all researchers.

Suggested approaches

• Conduct systematic empirical studies of how AI governance policies vary across disciplines, journals, and institutions, mapping the landscape of current practice and identifying effective policy models
• Develop and pilot test governance frameworks with specific guidance for different research roles (authors, reviewers, editors), evaluating their effectiveness in maintaining research integrity
• Investigate disciplinary differences in appropriate AI use norms, engaging field-specific expert communities to co-develop context-sensitive guidelines

Expected impact

Evidence-based governance frameworks would enable research institutions and publishers to develop coherent, fair AI policies, protect scientific integrity, and ensure equitable access to AI research tools across disciplines and resource levels.

A question to explore

I want to investigate how academic disciplines and institutions should govern AI use in research workflows. What does the evidence tell us about current policy gaps and their consequences for research integrity, and what would a rigorous study design look like to develop and evaluate effective AI governance frameworks across disciplinary contexts?