Artificial Intelligence for Adaptive Virtual Reality: Methods, Applications, and Future Prospects

ABSTRACT

Virtual Reality (VR) is a transformative technology offering immersive and interactive experiences, revolutionizing fields. However, VR cannot be applied as a one‐size‐fits‐all solution for diverse users, creating a major challenge that demands adaptive and personalized approaches. Artificial Intelligence (AI) has become a crucial enabler of such personalization, yet the specific ways in which AI techniques are implemented to create adaptive VR systems remain insufficiently synthesized in the current literature. This study addresses this gap by presenting a comprehensive overview and analysis of existing AI‐driven methods and approaches used to deliver adaptive VR applications. A review of 33 high‐quality studies reveals that AI, including classical Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Reinforcement Learning (RL), Computer Vision (CV), and Large Language Models (LLMs), forms the backbone of VR's evolution toward more user‐centric experiences. Current research is dominated by applications in entertainment, education, and healthcare, while manufacturing, engineering, and cultural heritage are also advancing. AI enhances VR through real‐time 3D content generation, intelligent avatars, gesture recognition, and immersive feedback. Nevertheless, challenges such as computational demands, latency, limited generalizability, and ethical concerns about data privacy and transparency persist, highlighting directions for future research.