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

Synthesis: This study analyzes AI policies across higher education institutions in 34 U.S. states, using NLP to uncover a clear divergence: university-level policies emphasize data security and risk mitigation, while school-level policies (when they exist) focus on pedagogical applications and tool usage. Relatively few business schools maintain distinct AI policies, creating misalignment with discipline-specific learning objectives. The findings highlight the challenges of institutional AI governance and the tension between centralized compliance-oriented policies and the need for discipline-specific pedagogical guidance.

Research Approach

The study applied natural language processing to analyze AI policies from institutions across 34 states:

University-wide policies: Broad governance frameworks addressing AI use campus-wide

School/department-level policies: Unit-specific guidelines, primarily in business schools

Comparative analysis: NLP identified thematic differences between governance levels

Key Findings

Policy Divergence:

Policy Level	Primary Focus	Gaps
University-wide	Data security, risk mitigation, legal compliance	Limited pedagogical guidance
School-level (when present)	Pedagogical applications, tool usage guidelines	Rarely exist; inconsistent when present
Business school-specific	Discipline-integrated AI use	Few maintain distinct policies

Risk vs. pedagogy tension: University policies prioritize security and compliance; pedagogical guidance is scarce at the top level

Missing middle: Most institutions lack school/department-level policies, leaving faculty without discipline-specific AI guidance

Accreditation implications: The gap between university risk-management framing and discipline-specific learning objectives creates challenges for accreditation bodies

Faculty and student impact: Without clear, aligned policies, both faculty and students navigate AI use inconsistently

Implications for AI Governance in Education

The study reveals a fundamental structural challenge in AI governance: risk-averse centralized policies that fail to address pedagogical needs at the discipline level. The authors recommend that guidelines be aligned with broader institutional policies while explicitly addressing discipline-specific learning objectives and evolving workforce demands. This aligns with emerging AI Literacy frameworks that emphasize both technical and pedagogical dimensions of AI readiness.

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Citation

Manikonda, L., & Outlaw, D. (2026). Policy Fragmentation or Institutional Alignment? Institutional Governance of AI in Universities and Business Schools. arXiv:2608.03584v1.