Personalized Learning and Innovative Teaching Approaches in Universities: Artificial Intelligence-Supported Science Education

Abstract

This study aims to analyze AI-supported personalized learning applications in university science education and their integration with innovative teaching approaches by drawing on existing literature. The study examines how AI transforms learning environments, optimizes learning processes according to individual student needs, and influences teachers' pedagogical approaches. The methodology employed involves a thorough analysis of the relevant literature, evaluating the role of AI-supported systems in science education and the effectiveness of personalized and adaptive learning systems. The findings show that artificial intelligence improves conceptual understanding and problem-solving skills, but there are significant gaps and contradictions in the literature regarding long-term impact, scalability, applicability in different socioeconomic contexts, and faculty members' digital pedagogical competencies. It also emphasizes the need for further research on student autonomy and the risk of over-reliance on artificial intelligence. To address these gaps, comprehensive empirical studies, interdisciplinary collaborations, and designs that support student autonomy are recommended.