Enhancing Student Engagement with AI-Driven Personalized Learning Systems
Zaharuddin, Yu Chen, Goh Yao
Abstract
This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both students and educators, this research identifies key factors that contribute to improved student motivation, interaction, and academic performance. The findings suggest that AI-driven systems not only provide tailored learning experiences but also foster a deeper connection between students and their learning content. The paper concludes with recommendations for future research and practical applications in educational institutions to further optimize the use of AI for enhancing student engagement.