Governing Artificial Intelligence in Education argues that the effectiveness of AI governance in education is shaped less by the strength of regulatory design and more by the capacity to implement, operationalize, and sustain policy within real institutional environments. The brief examines how the United States and the European Union approach AI governance through contrasting models—one decentralized and implementation-driven, the other structured and risk-based—and shows how both systems encounter similar limitations when policies are not translated into practice. Drawing on comparative analysis, it highlights how gaps in institutional capacity, data governance, and oversight mechanisms contribute to uneven outcomes, persistent inequities, and limited evidence of impact on learning and decision-making.
This policy brief proposes a shift from principle-based governance toward implementation-focused frameworks that prioritize operational clarity, institutional readiness, and continuous evaluation. It outlines key policy priorities, including strengthening human and technical capacity, embedding accountability into decision-making processes, and establishing mechanisms to assess how AI systems affect educational outcomes over time. Intended for policymakers, education leaders, and institutional stakeholders, the brief positions AI governance not as a purely regulatory challenge but as a systems-level coordination problem, where effective outcomes depend on aligning policy design with the realities of educational practice.
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