Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design
Shuwen Zhou (),
Wenxuan Zheng (),
Yang Xu () and
Yingchia Liu ()
Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 6, issue 1, 59-82
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.
Keywords: Virtual Reality; Adaptive User Interface; Artificial Intelligence; User Experience (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/230 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:6:y:2024:i:1:p:59-82:id:230
Access Statistics for this article
Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek
More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().