From Adaptation to Intelligence: A Systematic Review of Data, Strategies, and Impact in Personalized VR
Tangyao Li, Yitong Zhu, Hai‐Ning Liang, Yuyang Wang · International Journal of Human-Computer Interaction · 2026
As virtual reality (VR) systems advance, they are increasingly expected to adapt intelligently to individual users’ states, abilities, and preferences. While prior research has examined user-state sensing and adaptive interaction design in VR, existing reviews typically address these aspects in isolation. In this article, we examine the growing body of research on personalization in VR, specifically how user data collected during immersion is used to drive adaptive strategies that tailor the experience and enhance engagement, performance, or other goals. We synthesize findings from studies that employ adaptive techniques across diverse application domains and summarize a five-stage conceptual framework that unifies these adaptive mechanisms. Our analysis reveals emerging trends: integrating multimodal sensors, shifting from purely reactive to hybrid adaptation systems, and adopting artificial intelligence approaches. Finally, we identify key challenges related to data, modeling, and evaluation, and outline future directions toward more effective and user-centered VR systems.