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.
R et al. (Wed,) studied this question.