The effectiveness of using neural networks and machine learning in teaching physics

Abstract

Neural networks and machine learning (ML) technologies have seen rapid advancement across various fields, including education. In physics education, these technologies enhance both teaching methods and student learning outcomes through personalized learning, automated feedback, simulations, and data-driven insights. This article explores the applications and benefits of neural networks and ML in teaching physics, addressing their ability to create adaptive learning environments, improve student engagement, and optimize teaching strategies through real-time data analysis.