Shivani

DOI: https://doi.org/10.5281/zenodo.21996858

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.

Explainable Artificial Intelligence (XAI), Adaptive Virtual Reality, Human-Centred AI, Trustworthy AI, Transparency, User Trust, Immersive Systems, Human–Computer Interaction.

Adaptive Virtual Reality (VR) systems are increasingly capable of delivering personalized experiences by continuously interpreting behavioural, physiological, and contextual user data. However, the growing use of intelligent adaptation has introduced significant challenges regarding transparency, trust, and user understanding, as existing Explainable Artificial Intelligence (XAI) approaches were primarily developed for static and non-immersive decision-making environments (Guidotti et al., 2018; Ribeiro et al., 2016; Lundberg & Lee, 2017). This highlights the need for explainability mechanisms specifically designed to address the unique characteristics of adaptive immersive systems. To address this challenge, this paper proposed a seven-layer conceptual framework that embeds explainability throughout the adaptive VR lifecycle, encompassing adaptation triggers, intelligent inference, explanation generation, explanation delivery, granularity and timing control, trust calibration, and multidimensional evaluation. Rather than treating explanations as an interface-level addition, the framework positions explainability as a fundamental architectural principle that supports transparent, trustworthy, and human-centred adaptive VR systems. By integrating technical, behavioural, experiential, and ethical considerations within a unified framework, the proposed model provides a structured foundation for both the design and evaluation of explainable adaptive VR applications. The proposed framework contributes to the emerging intersection of XAI and immersive computing by extending explainability beyond algorithmic interpretation toward user-centred interaction, adaptive communication, and continuous trust calibration. Its model-agnostic design enables broad applicability across diverse domains, including education, healthcare, rehabilitation, industrial training, and immersive entertainment, while providing practical guidance for researchers and developers seeking to incorporate explainability into next-generation adaptive VR systems. Although the framework remains conceptual, it establishes a clear research agenda for future work. Empirical validation through user-centred experiments, prototype implementations, and longitudinal evaluations will be essential to examine the effectiveness of different explanation strategies, modality selection, adaptive personalization, and trust calibration mechanisms. Such investigations will help refine the proposed framework and advance the development of adaptive VR systems that balance personalization, transparency, ethical responsibility, and immersive user experience. Ultimately, this work demonstrates that explainability should not be regarded as an auxiliary feature of adaptive Virtual Reality but as a core design principle for intelligent immersive systems. Embedding transparency throughout the adaptive pipeline has the potential to strengthen user trust, improve decision understanding, and support the development of more responsible, effective, and human-centred Virtual Reality experiences.

2009
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2016
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2018
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2019
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2020
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2021
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2022
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2024
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2025
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Shivani (2026). Explainable Artificial Intelligence for Adaptive Virtual Reality Systems: A Conceptual Framework for Transparent User Adaptation. International Journal of Computer Science Engineering Techniques, 10(4), 63–82. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.21996858