This study aims to explore the concept of AI-based adaptive learning in the context of higher education, particularly focusing on its acceptance among university lecturers. Employing a qualitative, conceptual review approach, the study relies entirely on secondary data through the analysis of relevant existing literature. The primary objective is to identify current trends, challenges, and the potential for implementing AI-driven adaptive learning systems, as well as to assess lecturers’ acceptance and readiness towards such technologies. This study applies the Technology Acceptance Model (TAM) to examine acceptance. The findings suggest that although awareness of AI’s capacity to personalize learning is increasing, the level of acceptance among lecturers remains influenced by factors such as technological literacy, institutional support, and perceived effectiveness. This study contributes towards policy formulation and the development of appropriate training to support the integration of AI in teaching and learning in universities.
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