Governing generative AI in applied undergraduate education: integrating technological, institutional, and cultural dimensions

Generative AI is rapidly reshaping applied undergraduate education, streamlining teaching practices and enabling personalised learning, yet its diffusion amplifies a triad of ethical challenges. These challenges encompass technological opacity and dependency, institutional policy inertia and diffuse accountability, and cultural frictions tied to shifting teacher – student dynamics and the erosion of traditional educational values. To address these tensions, this study proposes a three-dimensional governance framework integrating transparent algorithm oversight, adaptive institutional mechanisms, and culturally anchored ethical education. By embedding cultural and relational dynamics – dimensions often absent from governance models that prioritise technological regulation and policy compliance – the framework offers a context-sensitive strategy for managing AI-driven risks in higher education. The findings extend Institutional Complexity Theory by demonstrating how multi-layered governance can temper AI-induced disruptions and sustain educational ethics. For educators, administrators, and policymakers, the study delivers a practical blueprint for responsible AI integration, ensuring that technological innovation serves as a catalyst for ethically grounded and sustainable learning environments rather than a source of destabilisation.