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<p>Virtual reality (VR) can foster self-directed learning (SDL), yet traditional feedback in self-directed VR (SDVR) environments often fails to provide timely and individualized support, particularly for average and low achievers. Recent advancements in GPT-based assistants may offer adaptive, real-time feedback to address these limitations. This study employed a stratified randomized controlled design with 83 undergraduates (experimental <i>n</i> = 42; control <i>n</i> = 41) enrolled in an embedded-AI VR course. Learners completed SDVR units on embedded-AI hardware and block-based programming, followed by two hands-on tasks (EAI assembly and EAI programming). Primary outcome measures included SDL abilities, learning motivation, cognitive levels, and hands-on performance. Compared with traditional feedback, GPT-based feedback was associated with higher post-test SDL, motivation, cognitive level, and hands-on scores, with the most substantial gains observed among average and low achievers (LAs). The GPT-based feedback showed significant main effects across all outcomes (<i>p</i> &lt; .001), explaining a substantial portion of the variance in cognitive level (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{\omega\:}_{p}^{2}\)</EquationSource> </InlineEquation> = 0.397), SDL abilities (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{\omega\:}_{p}^{2}\)</EquationSource> </InlineEquation> = 0.299), and motivation (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:{\omega\:}_{p}^{2}\)</EquationSource> </InlineEquation> = 0.397), representing large effect sizes. These findings demonstrate that integrating GPT-based adaptive feedback into SDVR environments provides timely and personalized support that enhances SDL, motivation, and higher-level cognitive and practical performance, particularly for learners with weaker prior achievement. The study highlights the potential of generative AI to support more personalized and equitable learning in immersive VR settings.</p>

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Boosting self-directed learning in virtual reality: the role of gpt-driven feedback for low achievers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Wei-Sheng Wang,
  • Chia-Ju Lin,
  • Yueh-Min Huang,
  • Ting-Ting Wu

摘要

Virtual reality (VR) can foster self-directed learning (SDL), yet traditional feedback in self-directed VR (SDVR) environments often fails to provide timely and individualized support, particularly for average and low achievers. Recent advancements in GPT-based assistants may offer adaptive, real-time feedback to address these limitations. This study employed a stratified randomized controlled design with 83 undergraduates (experimental n = 42; control n = 41) enrolled in an embedded-AI VR course. Learners completed SDVR units on embedded-AI hardware and block-based programming, followed by two hands-on tasks (EAI assembly and EAI programming). Primary outcome measures included SDL abilities, learning motivation, cognitive levels, and hands-on performance. Compared with traditional feedback, GPT-based feedback was associated with higher post-test SDL, motivation, cognitive level, and hands-on scores, with the most substantial gains observed among average and low achievers (LAs). The GPT-based feedback showed significant main effects across all outcomes (p < .001), explaining a substantial portion of the variance in cognitive level ( \(\:{\omega\:}_{p}^{2}\) = 0.397), SDL abilities ( \(\:{\omega\:}_{p}^{2}\) = 0.299), and motivation ( \(\:{\omega\:}_{p}^{2}\) = 0.397), representing large effect sizes. These findings demonstrate that integrating GPT-based adaptive feedback into SDVR environments provides timely and personalized support that enhances SDL, motivation, and higher-level cognitive and practical performance, particularly for learners with weaker prior achievement. The study highlights the potential of generative AI to support more personalized and equitable learning in immersive VR settings.