PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education

Hong Gao, Yiyang Xie, Enkelejda Kasneci

Research output: Contribution to journalArticlepeer-review

Abstract

The advent of large language models (LLMs) such as ChatGPT has demonstrated significant potential for advancing educational technologies. Recently, growing interest has emerged in integrating ChatGPT with virtual reality (VR) to provide interactive and dynamic learning environments. This study explores the effectiveness of ChatGTP-driven VR in facilitating machine learning education through PerVRML. PerVRML incorporates a ChatGPT-powered avatar that provides real-time assistance and uses LLMs to personalize learning paths based on various sensor data from VR. A between-subjects design was employed to compare two learning modes: personalized and non-personalized. Quantitative data were collected from assessments, user experience surveys, and interaction metrics. The results indicate that while both learning modes supported learning effectively, ChatGPT-powered personalization significantly improved learning outcomes and had distinct impacts on user feedback. These findings underscore the potential of ChatGPT-enhanced VR to deliver adaptive and personalized educational experiences.

Original languageEnglish
JournalInternational Journal of Human-Computer Interaction
DOIs
StateAccepted/In press - 2025

Keywords

  • ChatGPT
  • immersive learning
  • large language models
  • machine learning education
  • personalized learning
  • Virtual reality

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