ORIGINAL RESEARCH article
Front. Polit. Sci.
Sec. Politics of Technology
Volume 7 - 2025 | doi: 10.3389/fpos.2025.1666661
This article is part of the Research TopicGenerative AI Tools in Education and its Governance: Problems and SolutionsView all 15 articles
Generative AI governance in higher education: a case study from Africa
Provisionally accepted- University of KwaZulu-Natal, Durban, South Africa
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This article presents a pioneering case study from Africa on the institutional integration of generative artificial intelligence (Gen-AI) in higher education. Drawing on the experience of the University of KwaZulu-Natal (UKZN) in South Africa, we describe the development of academic guidelines for the responsible use of large language models (LLMs) across teaching, learning, and research contexts. The UKZN AI Academic Guidelines are among the first in Africa to offer a comprehensive institutional framework aligned with national policy priorities, while also responding to global debates on transparency, authorship, and academic integrity. This study highlights the distinctive challenges and opportunities of Gen-AI adoption in a resource-constrained, multicultural, and rapidly transforming academic environment. It offers insight into how African universities can craft forward-looking, enabling policies that prepare staff and students for an AI-driven future. We describe a participatory, interdisciplinary approach to policy development and examine how the Guidelines address practical questions of curriculum integration, ethical use, and disclosure. By foregrounding an African perspective, this case contributes to a more geographically inclusive discourse on educational technology. It demonstrates how global principles of trustworthy AI can be locally interpreted and operationalised, offering a model that may be relevant for institutions across the Global South and beyond.
Keywords: academic integrity, AI literacy, curriculum integration, disclosure framework, Generative AI, governance, Higher education policy, Transparency
Received: 15 Jul 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Thaldar, Botes, Badru, Chenia, Duma, Dlamini, Amin, Hugo, Govender, Bruce-Brand, Koorbanally and Chuturgoon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Donrich Thaldar, thaldard@ukzn.ac.za
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