AI-Adaptive Virtual Reality for STEM: Engagement, Retention, and Learning Outcomes

Abstract

Virtual Reality (VR) presents a com pelling opportunity to transform STEM pedagogy; however, empirical evidence evaluating AI-driven adaptivity, objective engagement metrics, and long-term knowledge retention remains sparse. This paper proposes and evaluates an AI-adaptive VR based solar system learning module deployed on Meta Quest 2, wherein a machine learning model dynamically adjusts in structional difficulty based on real-time learner interaction logs including dwell time, gaze patterns, and response latency. A controlled between-subjects experiment was conducted involving 120 secondary school students (mean age 14.3 years) across three schools in India, randomly assigned to three conditions: AI adaptive VR, standard VR, and traditional classroom instruction. Engagement was measured objectively through eye-tracking fixation density and heart rate variability (HRV), alongside pre-test, immediate post-test, and delayed post-tests at one week and one month intervals. Statistical analyses encompassing paired t-tests, one-way ANOVA, and Structural Equation Modeling (SEM) reveal that the AI-adaptive VR cohort demonstrated significantly higher knowledge improvement scores (mean gain = 38.4 vs. 31.2 vs. 10.7), superior one-month retention (p < 0.001), and measurably elevated engagement across all biometric indicators. SEM confirmed immersion as a significant mediator between adaptive instruction and knowledge acquisition (\b{eta} = 0.63, p < 0.001). Post-session self-directed learning exploration was recorded at 80% among AI-adaptive VR participants versus 40% in the traditional group. These findings establish an evidence based framework for deploying AI-adaptive immersive learning environments in resource-varied STEM educational contexts.