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<p>Generative AI and virtual reality are transforming educational and cultural experiences by enabling interactive, personalized, and immersive environments. This study employed a mixed-method sequential explanatory design to investigate gender-specific differences in user engagement, interaction patterns, and learning outcomes within AI-powered virtual museum environments. Sixty university students (30 males and 30 females) participated, engaging with AI-driven virtual assistants through VR headsets. Quantitative analyses using independent t-tests revealed that while both genders achieved significant knowledge gains, females demonstrated higher task-focused engagement and more fact- and clarification-oriented interactions, whereas males favored exploratory and entertainment-oriented queries. Qualitative thematic analysis further highlighted gender-specific preferences in system design and AI communication styles. The findings underscore the importance of designing adaptive AI systems that account for diverse interaction styles and learning needs. Recommendations include integrating gamification, enhancing AI responsiveness, and tailoring communication approaches to foster inclusive and equitable educational experiences.</p>

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Gender Differences in AI-Powered Virtual Museum Guides: Learning Outcomes and Interaction Patterns in Immersive VR

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Kannikar Intawong,
  • Pakinee Ariya,
  • Songpon Khanchai,
  • Phimphakan Thongthip,
  • Kitti Puritat

摘要

Generative AI and virtual reality are transforming educational and cultural experiences by enabling interactive, personalized, and immersive environments. This study employed a mixed-method sequential explanatory design to investigate gender-specific differences in user engagement, interaction patterns, and learning outcomes within AI-powered virtual museum environments. Sixty university students (30 males and 30 females) participated, engaging with AI-driven virtual assistants through VR headsets. Quantitative analyses using independent t-tests revealed that while both genders achieved significant knowledge gains, females demonstrated higher task-focused engagement and more fact- and clarification-oriented interactions, whereas males favored exploratory and entertainment-oriented queries. Qualitative thematic analysis further highlighted gender-specific preferences in system design and AI communication styles. The findings underscore the importance of designing adaptive AI systems that account for diverse interaction styles and learning needs. Recommendations include integrating gamification, enhancing AI responsiveness, and tailoring communication approaches to foster inclusive and equitable educational experiences.