Explaining VR/AR Learning in Medical Education: A Comparative PLS-SEM Analysis of TAM, SDT, TTF, and Flow Theory

Introduction: Virtual Reality (VR) and Augmented Reality (AR) are increasingly integrated into medical education, offering immersive and interactive environments for safe clinical training. Several theoretical frameworks—Technology Acceptance Model (TAM), Self-Determination Theory (SDT), Task-Technology Fit (TTF), and Flow Theory—can explain technology adoption and learning effectiveness. However, no comprehensive empirical comparison has been conducted within the context of VR/AR-based medical education. Methods: A cross-sectional survey was conducted with 329 undergraduate medical and health sciences students who had prior experience using VR/AR for learning activities. Validated instruments representing each theoretical framework were employed. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate reliability, validity, and structural relationships, followed by a comparative assessment using R², Q², f², and path coefficients. Results: Flow Theory demonstrated the strongest explanatory power (R² up to 0.72), with immersion and engagement as critical predictors of learning outcomes. SDT also showed high predictive strength (R² up to 0.63), emphasizing the role of intrinsic motivation. TTF was effective in predicting task-related learning effectiveness (R² = 0.67), whereas TAM provided only moderate explanatory power (R² ≈ 0.41–0.46). Conclusions: Flow Theory and SDT offer the most comprehensive explanations of student engagement and learning outcomes in VR/AR medical education. TTF remains valuable for task-specific alignment, while TAM primarily captures initial usability perceptions. Overall, immersive and motivational factors are key drivers of effective VR/AR learning, providing guidance for both theoretical development and instructional design in medical training