Review of using Generative Artificial Intelligence in Physics Education: A case study of KMUTT (Ratchaburi) - Lekprasert, Banyat; Sornbundit, Kan; Intarapong, Pisitpong; Chuenkhum, Songpol (KKU Science Journal, 2025)
The rapid advancement of Generative Artificial Intelligence (Gen-AI) has opened new possibilities for enhancing teaching and learning across disciplines, including physics education. This article reviews the potential applications of Gen-AI in undergraduate physics instruction, with a focus on its implementation at King Mongkut’s University of Technology Thonburi (KMUTT), Ratchaburi campus. Two primary areas are explored: (1) the use of Gen-AI to support the development of students’ physics reasoning skills through guided problem-solving and self-explanation strategies, and (2) the feasibility of employing Gen-AI as an offline automated assessment tool to provide timely feedback and reduce instructional workload. The review also addresses key challenges related to pedagogy, data privacy, and language compatibility, particularly in the context of Thai-language instruction. Preliminary evaluations of open-source language models are discussed, highlighting both their potential and current limitations. The findings suggest that with thoughtful integration, Gen-AI can play a transformative role in physics education by promoting deeper conceptual understanding and supporting scalable, personalized learning environments.