Policy Fragmentation or Institutional Alignment? Institutional Governance of AI in Universities and Business Schools

2026-08-04

AI Summary

This study addresses the misalignment between institutional and college-level AI policies—particularly in business schools—that undermines pedagogical effectiveness and accreditation compliance as universities integrate artificial intelligence into teaching and governance. Analyzing AI policy documents from higher education institutions across 34 U.S. states, the research employs natural language processing for semantic analysis and topic modeling to uncover governance fragmentation: university-wide policies emphasize data security and risk management, whereas academic units prioritize instructional applications. Notably, most business schools lack standalone AI policies, resulting in disciplinary misalignment with institutional objectives. To reconcile this tension, the study proposes a tiered governance framework that harmonizes institutional coherence with disciplinary specificity, ensuring both strategic consistency and contextual adaptability in AI implementation.

Abstract

Artificial intelligence (AI) is rapidly transforming high-skilled domains, requiring higher education institutions (HEI) to balance the teaching of foundational principles with the integration of emerging tools to ensure workforce readiness. While HEI are increasingly adopting AI, many continue to grapple with how it should be incorporated into curricula and governed through policy, especially when such policies are set at different levels of an institution. This research analyzes AI policies across HEI from 34 states in the United States to investigate what these policies entail and how policies set across institutions as well as within different levels at an institution differ. Using natural language processing (NLP) to analyze institutional AI policies, we find a clear divergence: university-level policies emphasize data security and risk mitigation whereas school-level policies, when present, focus on pedagogical applications and tool usage. When focusing on business school specific policies, relatively few business schools maintain AI policies distinct from university frameworks, creating misalignment with discipline-specific learning objectives. This gap poses challenges particularly for faculty and students as well as for accreditation purposes. Our insights suggest that guidelines should be aligned with broader institutional policies while addressing discipline-specific learning objectives and evolving workforce demands.

Problem

• policy fragmentation
• institutional alignment
• AI governance
• higher education
• business schools

Innovation

• institutional governance
• AI policy alignment
• natural language processing
• higher education
• business school