Authors: Nellylyn Moyo, Naume Sonhera Abstract: The adoption of generative artificial intelligence (GenAI) has quickly evolved from its experimental stage to a question of institutional governance in higher education. Although initial approaches to the integration of GenAI in universities have been characterized by ad hoc trial-and-error methods and inconsistent policies, higher education organizations are now taking a more systematic approach to the regulation and implementation of GenAI for educational, research, and administrative purposes. This study provides a conceptual critical literature review of the approaches being adopted by universities toward GenAI, emphasizing trends in the global context and in Africa and South African higher education. The study demonstrates that GenAI readiness should not be viewed solely in terms of willingness to use AI technologies but rather as a multifaceted construct. Institutional governance, on the other hand, has evolved to incorporate responses related to issues of academic integrity, redesigning assessments, AI literacy, requirements for disclosure, development of staff, protection of data, and responsible-use guidelines. Nevertheless, such an evolution is patchy, reactive, and very much dependent on institutional capacities, disciplinary differences, and available resources. In the case of African higher education, the possibilities generated by the advent of GenAI have been heavily determined by structural limitations, such as digital inequality, uneven infrastructures, immature policies, and lack of capacity building measures. South Africa emerges as a particularly interesting example because of the emergence of governance responses in a space characterised by inequalities related to access, digital literacy, and institutional capacity. The study argues for an approach to GenAI adoption readiness that would consider the issue at three interdependent levels: individual readiness, organisational readiness, and structural readiness. Such an approach would enable a more contextually sensitive perspective on responsible GenAI adoption.
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