Scholarlink

Virtual reality (VR) enhanced by Generative AI (GenAI) avatars offers opportunities for adaptive and engaging educational experiences. While prior studies emphasize the roles of performance expectancy (PE) and technological efficacy (TE) in learner engagement, few examined how AI scaffolding influences metacognition (META), agency, and motivational beliefs in VR. This study applied structural equation modeling (SEM) to investigate these dynamics in two experimental groups in low-immersive VR (desktops). The model demonstrated acceptable fit (CFI = 0.912, TLI = 0.887) and R \(^{2}\) values were highest for intention and technological efficacy (both > 0.70), with metacognition at 0.491. PE predicted both intention to continue using the system and TE, while GPT interaction increased META. However, META negatively predicted PE, suggesting a trade-off between reflection and system use. These results highlight the importance of designing AI scaffolding that supports metacognitive processes without undermining motivation. Integrating AI within VR environments can better align with learner expectations and improve both engagement and learning outcomes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Generative AI as a Learning Partner: Structural Insights from a VR–GPT Educational Platform

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Lei Fang,
  • Xue Zhou,
  • Daniel Shen,
  • Aru Nurgissayeva,
  • Umawathy Techanamurthy,
  • Renia Lopez-Ozieblo

摘要

Virtual reality (VR) enhanced by Generative AI (GenAI) avatars offers opportunities for adaptive and engaging educational experiences. While prior studies emphasize the roles of performance expectancy (PE) and technological efficacy (TE) in learner engagement, few examined how AI scaffolding influences metacognition (META), agency, and motivational beliefs in VR. This study applied structural equation modeling (SEM) to investigate these dynamics in two experimental groups in low-immersive VR (desktops). The model demonstrated acceptable fit (CFI = 0.912, TLI = 0.887) and R \(^{2}\) values were highest for intention and technological efficacy (both > 0.70), with metacognition at 0.491. PE predicted both intention to continue using the system and TE, while GPT interaction increased META. However, META negatively predicted PE, suggesting a trade-off between reflection and system use. These results highlight the importance of designing AI scaffolding that supports metacognitive processes without undermining motivation. Integrating AI within VR environments can better align with learner expectations and improve both engagement and learning outcomes.