Policy Fragmentation or Institutional Alignment? Institutional Governance of AI in Universities and Business Schools
Lydia Manikonda, Dominique Outlaw
Why It Matters
What makes this one worth your time
Understanding the governance of AI in education is crucial for ensuring that curricula meet evolving workforce demands and accreditation standards.
This research highlights the misalignment of AI policies in universities and business schools, impacting education and accreditation.
Summary
The paper analyzes AI policies across higher education institutions in the U.S., revealing a divergence between university-level and school-level policies regarding AI integration in curricula.
Key contributions
- Analysis of AI policies across 34 states in the U.S.
- Identification of key differences in policy focus between university and business school levels.
- Recommendations for aligning institutional policies with discipline-specific learning objectives.
Notable insights
- The use of natural language processing to analyze institutional policies provides a novel methodological approach to understanding policy divergence.
- The distinction between university-level and school-level policies reveals critical gaps in aligning educational objectives with institutional governance.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.03584v1 Announce Type: new 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.