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Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design

Author

Listed:
  • Shuwen Zhou
  • Wenxuan Zheng
  • Yang Xu
  • Yingchia Liu

Abstract

This paper presents a new approach to improving user experience in virtual reality (VR) environments using AI-driven user interface (UI) design. The proposed system uses advanced machine learning techniques to update UI content based on user interaction and real-time context. A comprehensive literature review examines the current state of VR interfaces, AI applications in UI/UX design, and evolving UI technologies. Data is a multi-layered process combining data collection, processing, and editing over time. The design was developed and evaluated through a rigorous study involving 50 participants, comparing a modified UI against a static UI control. The results showed a significant improvement in performance, experience reduction, and overall user satisfaction. The modified UI group showed an 18.6% reduction in completion time, a 47.8% reduction in errors, and a 34.9% increase in user satisfaction scores compared to the static UI group. Physiological data analysis supports these findings, showing reduced stress and increased engagement. This research contributes to the field of VR interface design by providing empirical evidence for the effectiveness of AI-driven adaptive UIs in improving user experience and field performance. Virtual.

Suggested Citation

  • Shuwen Zhou & Wenxuan Zheng & Yang Xu & Yingchia Liu, 2024. "Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 59-82.
    • Handle: RePEc:das:njaigs:v:6:y:2024:i:1:p:59-82:id:230
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    References listed on IDEAS

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    1. Prashis Raghuwanshi, 2024. "AI-Powered Neural Network Verification: System Verilog Methodologies for Machine Learning in Hardware," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 39-45.
    2. Yuan, Zhenyu & Yang, Jie & Zhang, Yufeng & Wang, Shikai & Xu, Tingnian, 2015. "Mass transport optimization in the anode diffusion layer of a micro direct methanol fuel cell," Energy, Elsevier, vol. 93(P1), pages 599-605.
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    Cited by:

    1. Xiaoan Zhan & Yang Xu & Yingchia Liu, 2024. "Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 463-478.

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