Localizing Responsible GenAI in Global Higher Education

Generative artificial intelligence is spreading across higher education, but responsible implementation varies across countries, systems, and institutions. Leaders must navigate fast-changing tools, divergent privacy regimes, global vendor markets, and shifting expectations for procurement and accountability. This conceptual chapter uses narrative synthesis to examine how institutions can localize responsible GenAI implementation without relying on a universal policy template. Drawing on global guidance, we develop a typology of three governance styles—risk-based regulation, principles-based governance, and standards-led assurance—and illustrate their implications through global higher education scenarios. We integrate these signals with organizational learning and change-leadership scholarship to identify four priorities: shared AI literacy, assessment redesign, accessibility and contestability, and right-sized vendor assurance. We argue that responsible implementation depends on institutional capacity to translate shared expectations into locally workable, care-centered routines.