AI-driven adaptive learning: Personalization in cognitive psychology-based learning in higher education

Abstract

In the digital era, AI-driven adaptive learning is becoming an innovative solution in higher education by customizing learning experiences based on student needs. This model increases learning engagement and effectiveness, but the challenge of integrating AI technology with optimal pedagogy remains. This research uses mixed methods, combining quantitative surveys and experiments with interviews and qualitative user interaction analysis. Inferential statistical analysis and thematic approaches were used to process the data. The results showed that the AI-based adaptive learning system accelerated concept understanding and increased student motivation. However, challenges such as digital infrastructure readiness, lecturers' skills in managing adaptive technology, as well as students' potential dependence on the system are still obstacles. The implications of this research confirm the importance of institutional policies to support AI integration, lecturer training in adaptive system management, and algorithm development that balances personalization with social interaction. The findings contribute to the development of a more holistic and inclusive model of adaptive learning in higher education.