Integrating Artificial Intelligence into Physics Education

Abstract

Physics education has long been recognized as one of the most intellectually demanding areas of science instruction. Students frequently struggle with abstract concepts, complex mathematical formalisms, and the need to visualize invisible phenomena such as electromagnetic fields, quantum states, or thermodynamic processes. Traditional teaching methods, while foundational, often face limitations in providing individualized support, timely feedback, and engaging visualizations that match the diverse learning needs of students. These persistent challenges contribute to high dropout rates in physics courses, declining interest in STEM fields, and difficulties in developing deep conceptual understanding.

In recent years, the rapid advancement of artificial intelligence technologies has opened new possibilities for addressing these long-standing issues. Computer and information communication technologies have over the years continued to evolve, leading to the development of artificial intelligence [1]. Tools such as adaptive learning systems, intelligent tutoring platforms, generative AI models, and sophisticated simulation environments are increasingly being integrated into educational practice. AI offers the potential to personalize learning pathways, generate interactive visualizations, provide immediate formative feedback, and support both students and instructors in more efficient and effective ways. As a result, the integration of artificial intelligence into physics education has become a highly relevant and timely topic in contemporary educational research.

The purpose of this paper is to examine the integration of artificial intelligence into physics education, with a particular focus on its pedagogical opportunities, practical applications, and associated challenges. Specifically, the paper aims to:

-analyze the main benefits that AI technologies can bring to the teaching and learning of physics;

-review existing practical applications and case examples of AI use in physics education;

-identify key limitations, risks, and ethical considerations related to AI integration; and

-propose recommendations for the effective and responsible implementation of AI tools in physics instruction.