From Adaptation to Intelligence: A Systematic Review of Data, Strategies, and Impact in Personalized VR

Jul 16, 2026

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Personalized virtual reality increasingly aims to adapt intelligently to individual users’ states, abilities, and preferences. This systematic review consolidates research on how in-immersion user data is transformed into adaptive strategies that tailor experiences and support goals such as engagement and performance. Findings across multiple application domains are synthesized into a unified five-stage framework. Results highlight trends including multimodal sensing, hybrid adaptation beyond reactive control, and growing use of artificial intelligence, while also outlining data, modeling, and evaluation challenges.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

Introduction

Scope

From Adaptation to Intelligence: A Systematic Review of Data, Strategies, and Impact in Personalized VR TANGYAO LI, YITONG ZHU, HAI-NING LIANG, and YUYANG WANG∗ , The Hong Kong University of Science and Technology (Guangzhou), China arXiv :2510 . 13123v2 [ cs .HC] 10 Jul 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 paper, we examine the growing body of research on personalization in VR, with a particular focus on how user data collected during immersion is used to drive adaptive strategies that tailor the experience and enhance engagement, performance, or other specific goals. We synthesize findings from studies that employ adaptive techniques across diverse application domains and summarize a five-stage conceptual framework that unifies adaptive mechanisms across domains. Our analysis reveals emerging trends, including the integration of multimodal sensors, the transition from purely reactive to hybrid adaptation systems, and the adoption of artificial intelligence approaches. Finally, we identify key challenges related to data, modeling, and evaluation, and outline future research directions toward more effective and user-centered VR systems. Additional KeyWords and Phrases: Virtual reality, Adaptive systems, Humancomputer interaction, User Experience, Review 1 Introduction Virtual reality (VR) offers users an immersive experience by creating a virtual environment that leverages the combined power of hardware, software, tracking technology, and perceptual science [158, 174] . Through various immersive setups, especially with the use of head-mounted displays (HMDs), VR enables experiences that are difficult or impossible to achieve through traditional interfaces [184] . Advances in VR hardware, particularly HMDs and motion tracking technologies, have made immersive VR experiences more accessible and robust. The first HMD,“The Ultimate Display”, was invented by Ivan Sutherland in the mid-1960s [175] . The landscape of VR has undergone a significant transformation over the last decade. The release of the consumer versions of Oculus Rift and HTC Vivein 2016 made VR more accessible to a much wider audience, but the high cost and technical limitations are drawbacks [157] . Oculus Go, the first standalone VR headset, was released in 2018 at a more affordable price. Later, the Oculus (now Meta) Quest series further improved immersion and interactivity by supporting six degrees of freedom tracking through inside-out sensing [69] . Recent advancements include the 2024 release of Apple Vision Pro and PICO 4 Ultra. Nowadays, multiple standalone VR HMDs are available on the consumer market and have been widely adopted in domains such as gaming, training, healthcare, and education [9, 47, 148, 195, 216] . However, users differ substantially in their skills, preferences, and cognitive capacities, making one-size-fits-all VR experiences insufficient. To address these differences, many VR systems attempt to ∗ Corresponding author Authors’ Contact Information: Tangyao Li, [tli724@connect.hkust-gz.edu.cn](tli724@connect.hkust-gz.edu.cn); Yitong Zhu, [yzhu162@connect.hkust-gz.edu.cn](yzhu162@connect.hkust-gz.edu.cn); Hai-Ning Liang, [hainingliang@hkust-gz.edu](hainingliang@hkust-gz.edu). cn; Yuyang Wang, [yuyangwang@hkust-gz.edu.cn](yuyangwang@hkust-gz.edu.cn), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China. tailor the experience to individual users to improve user experience (UX) . Personalized interaction, also referred to as adaptive VR or personalized VR, encompasses a range of strategies [37, 137] . These may include, but are not limited to, customizing virtual environments to enhance immersion, adjust

FAQs

What does the systematic review focus on in personalized VR?

It examines how user data collected during immersion drives adaptive strategies in personalized VR, and how those strategies affect engagement, performance, and other targeted goals.

Which unified framework does the review propose for adaptive VR mechanisms?

The review synthesizes results into a five-stage conceptual framework that unifies adaptive mechanisms across different application domains.

What key challenges and future directions are identified?

The review identifies challenges related to data, modeling, and evaluation, and outlines future research directions aimed at more effective and user-centered VR systems.

Jul 16, 2026

Upload by Patrick