Abstract

The integration of Artificial Intelligence (AI) in physics education has emerged as a transformative approach, reshaping traditional teaching and learning practices. This review examines recent research on AI applications across primary, secondary, and higher education levels, focusing on tools such as generative AI models, intelligent tutoring systems, interactive simulations, and adaptive learning platforms. Findings indicate that AI enhances conceptual understanding, supports personalized learning, and promotes active, inquiry-based engagement among students. Furthermore, AI tools assist teachers in lesson planning, assessment, and identifying misconceptions, thereby improving instructional effectiveness. Despite these benefits, challenges related to infrastructure, teacher preparedness, ethical concerns, and equitable access persist, highlighting the need for pedagogically informed implementation strategies. This study underscores the potential of AI to create interactive, student-centered, and data-driven learning environments while identifying research gaps for future investigation.

Keywords: Artificial Intelligence, physics education, generative AI, interactive simulations, pedagogical innovation