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Enhancing Badminton Rule Learning Through a GPT‐Integrated LINE Bot.

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 badmin...

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Published in:Journal of Computer Assisted Learning Vol. 42; no. 2; pp. 1 - 17
Main Authors: Lin, Kuo‐Chin, Hung, Hui‐Chun, Chen, Nian‐Shing
Format: research tables/charts Journal Article
Published: Wiley-Blackwell Apr2026
Online Access:View this record in EBSCOhost
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      dt: Apr2026
      vid: 42
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1002/jcal.70231
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        atl: Enhancing Badminton Rule Learning Through a GPT‐Integrated LINE Bot.
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        au:
          Lin, Kuo‐Chin
          Hung, Hui‐Chun
          Chen, Nian‐Shing
        affil: Center for Physical and Health Education, National Sun Yat‐sen University, Kaohsiung, Taiwan
      sug:
        subj:
          Racquet Sports Education
          Rules and Regulations Education
          Teaching Methods
          Artificial Intelligence, Generative Utilization
          Computer-Assisted Instruction
          Natural Language Processing
          Education, Physical Education
          Athletes Education
          Outcomes of Education
          Human
          Taiwan
          Funding Source
          Male
          Female
          Adult
          Middle Age
          Quasi-Experimental Studies
          Experimental Studies
          Clinical Trials
          Educational Technology Utilization
          Semi-Structured Interview
          Descriptive Statistics
          Data Analysis Software
          Two-Tailed Test
          T-Tests
          Chi Square Test
          Confidence Intervals
          Confidence
          Student Attitudes
          User-Computer Interface
          Student Satisfaction
          Student Knowledge Evaluation
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
          Female
      ab: 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&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&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&A tool that can enhance access to clarification in physical education badminton rule instruction. Lay Summary: GPT and other AI tools are increasingly being used in education across a wide range of subjects.The GPT‐integrated LINE Bot produced learning outcomes comparable to those of traditional teacher‐led Q&A.In the experimental group, students were able to ask questions individually and in parallel, resulting in a different questioning structure compared to whole‐class Q&A.GPT‐integrated Q&A systems have the potential to support more equitable access to clarification in physical education badminton rule instruction.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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