Enhancing VR-Based Language Learning: Linking Task-Technology Fit, Expectancy Disconfirmation, and EEG Insights

This study investigates the structural relationships between task characteristics, technological features, task–technology fit, performance, satisfaction, and expectation within a Virtual Reality (VR) environment for English as a Foreign Language (EFL) learning. Grounded in the task-technology fit and expectancy disconfirmation theories, we further explore the moderating role of Electroencephalography (EEG)-detected Frontal Alpha Asymmetry (FAA) in these relationships. A total of 159 participants engaged in a 20-minute VR-based EFL course, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrated the reliability and validity of the measurement model, and six of ten hypothesized pathways in the structural model were supported. Notably, FAA significantly moderates the relationship between expectation and satisfaction, highlighting the potential of physiological measures in understanding user experiences. This study offers a novel contribution by integrating EEG data into SEM analysis to examine EFL learners’ perceptions of VR-based language learning, providing critical insights into the interplay between cognitive and technological factors in immersive educational environments.