Artificial intelligence in Physics Education: a comprehensive literature review - Mahligawati, F; Allanas, E; Butarbutar, M H; Nordin, N A N (Journal of physics. Conference series, 2023)
This paper presents a comprehensive literature review on the application of artificial intelligence (AI) in physics education. The study aims to explore the fundamental concepts of AI, its diverse applications in physics learning, and the benefits and challenges associated with its implementation. Through a systematic search of academic databases, a collection of relevant research articles, journals, conference papers, and books related to AI in physics education was obtained. The selected studies were analyzed and synthesized to develop a coherent framework for understanding the various ways AI is utilized in physics education, such as concept introduction, individualization, social interaction, and assessment. The findings underscore the positive impact of AI on enhancing conceptual understanding, providing personalized instruction, promoting social interaction, and improving assessment methods. However, challenges in terms of technical infrastructure, teacher training, data privacy, and ethical considerations were also identified. The paper concludes with recommendations for future research, addressing these challenges, and fostering effective implementation of AI technologies in physics education. This review provides valuable insights for educators, researchers, policymakers, and stakeholders seeking to leverage AI’s potential to revolutionize physics education.