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

Chuhao Wu, He Zhang, John M. Carroll

TL;DR

This study investigates how large research universities govern Generative AI usage by analyzing AI guidelines from 14 Big Ten institutions. Using mind-mapping and thematic analysis, it identifies three core governance dimensions: multi-unit governance (involving IT, Teaching & Learning, Provost, Libraries, AI Center, and others), role-specific governance (for Faculty, Students, Staff, and Researchers), and academic characteristics (educative/advisory guidance, flexibility, and Socratic inquiry). The findings reveal a predominantly educative and flexible governance landscape with frequent cross-references and a potential information overload, suggesting centralized access through an AI governance hub to improve usability. The work offers practical implications for HEIs seeking responsible AI adoption and highlights considerations for workload and governance design in complex institutional contexts.

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.

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

TL;DR

This study investigates how large research universities govern Generative AI usage by analyzing AI guidelines from 14 Big Ten institutions. Using mind-mapping and thematic analysis, it identifies three core governance dimensions: multi-unit governance (involving IT, Teaching & Learning, Provost, Libraries, AI Center, and others), role-specific governance (for Faculty, Students, Staff, and Researchers), and academic characteristics (educative/advisory guidance, flexibility, and Socratic inquiry). The findings reveal a predominantly educative and flexible governance landscape with frequent cross-references and a potential information overload, suggesting centralized access through an AI governance hub to improve usability. The work offers practical implications for HEIs seeking responsible AI adoption and highlights considerations for workload and governance design in complex institutional contexts.

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.
Paper Structure (39 sections, 4 figures, 2 tables)

This paper contains 39 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Responsible AI Governance
  4. Technology Diffusion in HEIs
  5. Methods
  6. The Big Ten Universities
  7. Data Collection
  8. Data Analysis
  9. Results
  10. Multi-unit Governance of AI
  11. Information Technology.
  12. 1. Data-sharing policy.
  13. 2. Enterprise agreement.
  14. 3. Trustworthy AI.
  15. Teaching & Learning.
  16. ...and 24 more sections

Figures (4)

  • Figure 1: Characteristics of trustworthy AI systems defined by NIST: Valid & Reliable is a necessary condition of trustworthiness and is shown as the base for other trustworthiness characteristics. Accountable & Transparent is shown as a vertical box because it relates to all other characteristics tabassi2023artificial.
  • Figure 2: GenAI guidance published by AI Center at U7, categorized by student, faculty, and staff
  • Figure 3: Example use cases for GenAI provided by U6
  • Figure 4: Summarized framework of AI Governance through Official Guidance