Enhancing Badminton Rule Learning Through a <scp>GPT</scp> ‐Integrated <scp>LINE</scp> Bot

ABSTRACT Background Over the past 2 years, the Generative Pre‐trained Transformer (GPT), a large language model (LLM) developed by OpenAI, has gained significant momentum across various educational fields. However, its application in physical education (PE) for teaching theoretical knowledge, such as badminton rules, has remained largely underexplored. Objectives This study aims to evaluate the effectiveness of a GPT‐integrated LINE Bot in facilitating students' learning of badminton rules, compared to traditional teacher‐led question‐and‐answer (Q&amp;A) methods. Methods Using a quasi‐experimental design, this study divided the participants into an experimental group that used the GPT‐integrated LINE Bot and a control group that received traditional Q&amp;A support. The data collected were analysed quantitatively using a two‐tailed independent samples t ‐test, and the results were further supported qualitatively through semi‐structured interviews. Results and Conclusions The results showed no significant difference in learning outcomes between the two groups, indicating that the GPT‐integrated LINE Bot is equally effective as traditional methods. Positive feedback from participants highlighted the bot's clarity and helpfulness, suggesting its potential as an AI‐supported Q&amp;A tool that can enhance access to clarification in physical education badminton rule instruction.