Identifying key components and stakeholders for generative AI governance in higher education: a systematic literature review

The rapid spread of GenAI presents unprecedented opportunities and significant governance challenges for higher education, requiring a comprehensive framework. Addressing these issues is essential for improving Quality Education. This systematic literature review (SLR) addresses this need, as current understanding of GenAI governance remains scattered, mainly focusing on specific areas like academic integrity or particular policies. Using a rigorous SLR methodology based on Kitchenham's framework, this study systematically identifies and synthesizes key governance components and stakeholder dynamics. Our multi-theoretical analysis, guided by Socio-Technical Systems Theory and Stakeholder Theory, identified 30 core governance elements across social, technical, and socio-technical sectors, along with key stakeholders characterized by their power, legitimacy, and urgency. These findings were mapped to the COBIT 2019 framework to develop a structured, integrated GenAI governance model. The study makes a significant contribution through a novel, integrated conceptual framework that combines fragmented insights via a multi-theoretical approach. It offers the first comprehensive proposal for a GenAI governance model, providing a holistic view by including its socio-technical aspects and stakeholder interactions within a structured framework. Practically, it offers actionable guidance to higher education institutions to develop responsible GenAI policies, promote responsible use, mitigate risks, and ultimately enhance educational quality and integrity.