ENHANCING STUDENT ENGAGEMENT THROUGH AI-BASED LEARNING ENVIRONMENTS

Abstract

This conference paper examines how artificial intelligence (AI)-based learning environments can enhance student engagement in higher education. The study focuses on the integration of learning analytics, recommendation systems, intelligent tutoring, adaptive feedback, and interactive programming tasks to support behavioral, cognitive, emotional, and social engagement. The proposed model links learner profiling, real-time analytics, personalization, feedback, and pedagogical design into a unified engagement-oriented learning environment. A model-based experimental framework is presented with sample indicators and illustrative results. The findings suggest that AI-supported adaptive learning environments may increase student participation, improve persistence in programming tasks, strengthen self-regulated learning, and support the development of programming competence. The paper also discusses implementation challenges, including data privacy, teacher readiness, infrastructure, and the need for human-centered AI design.