Regulating Artificial Intelligence in Higher Education: Comparative InstitutionalAnalysis of Legal Governance, Educational Accountability, and Human CapitalDevelopment in the European Union and the United States

Abstract

This article examines how legal and governance frameworks shape the regulation of artificial intelligence in
higher education and how these regulatory choices affect educational accountability, institutional capacity,
and economic development. Using a comparative institutional analysis of the European Union and the United
States, the study argues that AI governance in higher education is not merely a technical or pedagogical issue
but a legally structured developmental problem. The European Union represents a risk-based, rights-oriented,
and supranational regulatory model, anchored in the AI Act, the General Data Protection Regulation, and the
Digital Education Action Plan. The United States represents a decentralized, guidance-based, and innovationoriented model shaped by federal civil rights law, sectoral privacy regulation, agency guidance, institutional
autonomy, and market-driven educational technology adoption. The comparison reveals that regulatory
coherence enhances accountability and public trust but may increase compliance burdens, whereas
decentralized flexibility promotes experimentation but risks uneven protection, fragmented institutional
standards, and unequal student outcomes. The findings indicate that AI regulation affects higher education
through three causal mechanisms: legal classification of risk, institutional coordination capacity, and
accountability infrastructures for data, assessment, and student rights. The article contributes to law,
education, and development scholarship by proposing a governance-development model linking regulatory
capacity, educational quality, institutional legitimacy, and human capital formation.