AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities

TL;DR

This paper examines how top U.S. universities govern responsible AI use in higher education through case studies, highlighting strategies, strengths, and limitations of their guidelines to inform better AI governance practices.

Contribution

It provides a detailed analysis of AI governance strategies in prestigious universities, focusing on multi-unit and role-specific governance approaches.

Findings

Identified multi-unit governance structures for AI in universities

Highlighted role-specific governance strategies for AI

Discussed strengths and limitations of current AI guidelines

Abstract

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.