| Abstract | The rapid emergence of generative artificial intelligence (AI) is reshaping the strategic, pedagogical, and institutional landscapes of higher education. This chapter examines how universities differ in their responses to generative AI, drawing on a latent profile analysis of institutional guidelines to develop a four fold typology of AI adoption. The findings demonstrate that institutional characteristics—such as research intensity, internationalisation, academic reputation, and faculty resources—strongly shape whether universities take restrictive, enabling, or ambivalent positions toward AI. Highly reputable and research intensive universities tend to adopt defensive, risk averse strategies, emphasising academic integrity, authorship norms, and reputational protection. In contrast, institutions with lower reputational stakes or greater instructional capacity are more likely to explore AI as an opportunity for pedagogical innovation, competitiveness, and student support. Two neutral profiles reveal more complex structural tensions, where universities balance innovation with concerns about equity, diverse student needs, and resource constraints. Across all profiles, AI introduces a dual structure of expectation and anxiety: while institutions anticipate gains in efficiency, research productivity, and policy automation, they simultaneously confront uncertainties related to governance, labour, expertise, and legitimacy. These findings underscore that AI adoption is not merely a technical decision but an expression of institutional strategy shaped by historical trajectories, accumulated capital, and exposure to reputational risk. The chapter situates these patterns within global frameworks such as UNESCO’s AI and Education guidelines and Jagannathan’s work on digital learning for sustainable development. It concludes by outlining implications for policy, governance, and sustainability, arguing that AI has become a key driver of divergence in the futures of universities worldwide. |
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