Governing generative AI in digital education: how institutional guidance becomes course-level policy

Universities are grappling with the governance of generative AI in digital education, finding that institutional guidance alone is insufficient. This study analyzes course syllabi as primary mechanisms for translating university-level AI principles into specific, student-facing rules within individual courses and assessments. It highlights the variability and alignment challenges in operationalizing AI governance at the course level.

Why it matters

The effective governance of generative AI is crucial for maintaining academic integrity, ensuring equitable access to technology, and preparing students for an AI-integrated future. Institutions must develop coherent strategies to translate high-level policies into practical, consistent, and adaptable guidelines at the operational (course) level to manage technological disruption and foster responsible innovation.

Key insights

• Institutional guidance on generative AI in digital education is not fully resolving governance challenges at the course level.
• Syllabi function as critical formal governance texts, converting broader university principles into actionable student policies.
• The study utilized qualitative comparative document analysis of 35 syllabi from a large U.S. public research university (22 from Education, 13 from other fields).
• Analysis focused on comparing course-level statements with the institutional governance framework.
• Key areas of analysis included primary governance stances, institutional alignment, policy rationales, and discourse registers.
• Findings reveal how university principles are translated into course-specific rules for acceptable AI use.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1886876