Higher Education
Governing Artificial Intelligence in Education
Comparing the U.S.'s decentralized approach to AI in education with the EU's risk-based model, this brief argues the real bottleneck isn't regulatory design, it's institutional capacity to implement it. It closes with concrete priorities for policymakers and education leaders.
Why it matters: Every region seems to be racing to write AI-in-education policy, but the U.S. and EU are running two very different playbooks — and neither is winning on implementation. Co-written with Dr. Jorgete Vitorino Clarindo dos Santos, this brief asks the question institutions actually need answered: not "what should the policy say," but "can we actually run it."
Key takeaways:
The U.S.'s decentralized, implementation-driven model and the EU's structured, risk-based model hit the same wall: policies that don't translate into institutional practice.
Gaps in institutional capacity, data governance, and oversight — not weak regulatory design — are what produce uneven outcomes and limited evidence of real impact on learning.
The fix is a shift from principle-based governance to implementation-focused frameworks: build capacity first, embed accountability into decisions, and measure impact continuously.
Published: Policy Paper · The Digital Economist · April 22, 2026
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