Implementing Artificial Intelligence (AI) as adaptive learning in higher education transforms student interaction with educational material. The rapid expansion of Artificial Intelligence (AI) applications in higher education has accelerated the shift toward adaptive learning systems designed to personalize instruction, enhance learner engagement, and improve academic outcomes. As institutions increasingly rely on these technologies, the need for effective governance frameworks has become critical. This study adopts a desktop research approach to examine existing literature, theoretical models, and policy guidelines surrounding the governance of AI-driven adaptive learning in higher education. The analysis highlights key governance dimensions—including ethical standards, data privacy, algorithmic transparency, institutional accountability, and regulatory compliance—that shape the responsible deployment of AI technologies. Findings indicate that while AI-enhanced adaptive learning offers substantial pedagogical benefits, such as real-time analytics, individualized learning pathways, and predictive student support, it also raises challenges related to bias, equity, intellectual property, and institutional readiness. The paper argues that robust governance mechanisms are essential to balancing innovation with ethical stewardship. It proposes a conceptual governance model that integrates institutional policy, human oversight, technological safeguards, and continuous evaluation to ensure trustworthy, transparent, and equitable AI adoption. The results indicate that with meticulous execution and continuous assessment, AI-driven adaptive learning can substantially improve the educational experience at higher education institutions, rendering learning more personalized, efficient, and effective.
Andrew Enaifoghe* (Fri,) studied this question.