https://zenodo.org/uploads/19704034

This article examines the effectiveness of artificial intelligence (AI)-based individualized learning models in physics education, with a particular focus on adaptive learning systems. The study explores how AI technologies personalize learning trajectories, adjust content difficulty, and provide real-time feedback to learners. Using both quantitative and qualitative research approaches, the paper evaluates the impact of adaptive learning platforms on students’ academic performance and conceptual understanding in physics. The findings indicate that AI-driven systems significantly enhance student engagement, improve learning outcomes, and support differentiated instruction. Statistical analysis reveals measurable gains in test scores and retention rates among students exposed to adaptive learning environments. Furthermore, qualitative observations highlight increased motivation and self-regulated learning behaviors. The research underscores the potential of AI to transform traditional physics education by offering tailored instruction that meets individual learner needs. The results contribute to ongoing discussions on digital pedagogy and provide practical implications for educators and policymakers seeking to integrate AI technologies into science education.