Enhancing Physics Education through Artificial Intelligence Tools

Abstract

The integration of Artificial Intelligence (AI) in education has opened new avenues for enhancing the teaching-learning process, particularly in subjects like physics, which often involve complex concepts and abstract reasoning. This research explores the application of AI tools in the domain of physics education and evaluates their effectiveness in improving student engagement, conceptual understanding, and performance outcomes. As traditional teaching methods frequently struggle to meet the diverse needs of 21st-century learners, AI offers promising alternatives through adaptive learning platforms, intelligent tutoring systems, and interactive simulations.

This study investigates ten widely-used AI tools—including PhET, Labster, ChatGPT, and Squirrel AI—by analyzing their roles in a structured 60-minute virtual physics class model. The methodology includes a mixed-method approach combining pre- and post-test evaluations, student surveys, and teacher interviews across five educational institutions. Quantitative results indicate a significant increase in student scores (average 28% improvement), while qualitative feedback highlights increased motivation, self-paced learning, and better concept retention.

The research also presents implementation strategies and acknowledges challenges such as digital inequality, high software costs, and the need for teacher training. Despite these hurdles, the findings support the transformative role of AI in modern physics education. The study concludes that a well-integrated AI teaching model can democratize access to quality science education and support deeper cognitive engagement among students.