HCAVR-PLE: A Dual-Phase Adaptive AI-VR Framework for Equitable, Explainable, and Engaging Physics Education

Abstract

There is still an issue of equity and involvement in secondary STEM education, especially among diverse students dealing with the concepts of physics in resource-limited environments. Traditional teaching methods, such as traditional virtual reality (VR), tend to be non-personalized, restrictive in inclusivity, and thus ineffectual. The study proposes HCAVR-PLE, a Human-Centered Adaptive Virtual Reality Personalized Learning Environment that combines the Cognitive Affective Model of Immersive Learning (CAMIL), multi-armed bandit algorithms, explainable AI (XAI), and reflective prompts to provide clear, bias-free teaching. HCAVR-PLE had enormous effects, where the AI-VR (Real) had an increase in PAT of 29.2% compared to the non-adaptive groups ( =0.39, p 0.05). CAMIL engagement was greatest in adaptive AI-VR ( =0.52) and gender/SES equity gaps were least in the AI-VR (Real) condition. The framework was validated using the dual-phase method: 300 synthetic learner profiles (Phase I) based on PISA and OULAD data sets, and 221 Grade 8 students (Phase II) in the state of Tamil Nadu, India and compared to using Teacher dashboards, built based on HG-SCM-based explainable AI, made delivery of personalized experiences interpretable and equitable, whereas Phase II had established efficacy of framework without intervention of dashboard. These results reveal the potential of HCAVR-PLE to revolutionise STEM education by delivering greater engagement and equity solutions, potentially providing a scalable, universal classroom model of learning.