AI Governance in Higher Education: A course design exploring regulatory, ethical and practical considerations

AI governance education is fragmented and needs integration: Current AI ethics training is siloed by discipline and disconnected from real-world practice, creating a gap between what students learn and what professionals actually need to address ethical, legal, and governance challenges in their careers.

Specific operational failures repeat across AI systems: The paper identifies five recurring problems—bias, misspecified objectives, generalization errors, misuse, and governance breakdowns—that appear consistently across sectors, suggesting these are predictable risks that can be systematically taught and prevented through proper training.

Interdisciplinary curriculum is essential for responsible AI: Rather than teaching ethics, law, and policy separately, the proposed approach integrates technical foundations with these domains, recognizing that professionals need to understand how regulatory requirements, ethical principles, and technical realities interact in practice.

Global regulatory perspectives matter for AI professionals: The curriculum draws on frameworks from the EU, China, and international bodies, reflecting that AI governance is not uniform worldwide and professionals need exposure to different regulatory approaches to navigate the global landscape effectively.

Experiential learning and stakeholder engagement are critical: The course emphasizes hands-on learning and engaging diverse stakeholders, moving beyond theoretical knowledge to help students develop practical skills in diagnosing risks, navigating regulations, and communicating with different groups—capabilities essential for real-world responsible AI governance.