Explainable Artificial Intelligence for Adaptive Virtual Reality Systems: A Conceptual Framework for Transparent User Adaptation

Adaptive Virtual Reality (VR) systems are increasingly incorporating artificial intelligence (AI) to personalize user experiences through continuous adjustments to task difficulty, navigation support, interaction strategies, comfort settings, and narrative progression. By analyzing multimodal data such as gaze behaviour, physiological responses, movement patterns, and task performance, these systems can dynamically modify virtual environments to enhance engagement and performance. Despite these advances, the decision-making processes underlying AI-driven adaptations remain largely opaque, limiting users' understanding of why specific adaptations occur and potentially reducing trust, perceived fairness, and technology acceptance. While Explainable Artificial Intelligence (XAI) has emerged as an effective approach for improving transparency in AI-enabled decision-making, most existing XAI techniques have been developed for discrete prediction tasks and are not readily applicable to the continuous, immersive, and context-sensitive nature of adaptive VR environments. This paper proposes a seven-layer conceptual framework for integrating explainability into adaptive VR systems while preserving user immersion and interaction quality. The framework consists of seven interconnected components: Adaptation Trigger Layer, Inference and Adaptation Engine, Explanation Generation Layer, Explanation Modality Layer, Granularity and Timing Control, Trust Calibration Loop, and Evaluation Layer. Unlike existing approaches that primarily emphasize personalization accuracy, the proposed framework treats explainability as a core design principle and explicitly addresses the trade-offs between transparency, immersion, usability, and user trust. In addition, the framework incorporates ethical considerations related to privacy, physiological data processing, and responsible AI, providing a broader human-centred perspective for future adaptive VR systems. The paper concludes by outlining future research directions and offering practical guidance for researchers and designers seeking to develop transparent, trustworthy, and user-centred adaptive virtual reality applications.