Understanding inhibitions in the adoption of metaverse technologies in education: insights from interpretive structural modelling

Metaverse technologies are innovative tools that create immersive, interactive, and engaging learning environments. While previous studies have primarily offered conceptual insights into adoption, limited empirical research has systematically examined the interdependent barriers in the education sector. The purpose of this research is to bridge this gap by investigating these barriers using Interpretive Structural Modeling (ISM) as the research method. The analysis confirmed that the most significant barriers are privacy and security concerns, limited research and knowledge, pedagogical and educational challenges, technical and compatibility issues, cultural and social inclusivity, and challenges in integration. Lower-level barriers were identified as technological infrastructure, lack of training and support, ethical and regulatory issues, financial constraints, implementation and maintenance costs, user adoption and engagement, lack of comprehensive frameworks, student engagement and knowledge gaps, and accessibility issues. These findings highlight the layered and systemic nature of barriers, where surface-level obstacles are rooted in deeper structural challenges. The implications of the study, stated clearly, are that strengthening institutional capacity through training, infrastructure, ethical guidelines, and financial investment can accelerate the effective adoption of metaverse technologies in education.