<![CDATA[The rapid development of Artificial Intelligence (AI) technologies has significantly transformed the landscape of higher education, particularly in the context of personalized learning. Traditional learning systems often apply uniform instructional methods that fail to address individual differences in students' learning pace, preferences, and cognitive abilities. Consequently, there is a growing need for adaptive learning systems capable of providing personalized educational experiences. Generative Artificial Intelligence (Generative AI) has emerged as a promising technology that can dynamically generate educational content, feedback, and learning pathways tailored to individual learners. This study proposes a Generative AI-Based Adaptive Learning Model designed to support personalized learning in higher education environments. The model integrates machine learning algorithms, learning analytics, and generative AI techniques to analyze student learning behavior and automatically generate adaptive learning materials and recommendations. The research adopts a design science research methodology (DSRM) to develop and evaluate the proposed model through conceptual design and system architecture analysis. The results indicate that integrating generative AI into adaptive learning systems can enhance learning personalization, improve student engagement, and support instructors in delivering more efficient and scalable educational experiences. Furthermore, the proposed model provides a flexible framework that can be implemented in various digital learning platforms and learning management systems. The findings of this study contribute to the development of intelligent learning environments that leverage generative AI to improve the quality of higher education in the digital era.]]>
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