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This study presents a comparative policy mapping analysis of publicly available generative AI policies and guidance across America’s flagship public universities, based on documents collected in 2025 and analyzed through thematic coding informed by Clark’s Triangle of Coordination. Findings indicate a distributed governance architecture in which instructional decisions are typically delegated to the course level and supported through instructor-facing resources, while institution-level guidance emphasizes data protection, approved tools, and risk management. Although institutions vary in posture, codification, and the depth of guidance provided, the findings point to structural convergence around this shared governance model, suggesting that flagship universities are responding to generative AI in ways that reinforce established distributions of authority within public higher education systems.
Jason LaFrance (Thu,) studied this question.