A proposed model for predicting the intention to use the metaverse in computer programming education

Abstract

The advent of new technologies has significantly increased digitalization in education by accelerating the shift to digital products and tools. In particular, notable innovations in artificial intelligence and Metaverse technologies hold promising potential to transform educational and training formats globally, demanding swift reforms in education systems. This study explored the factors influencing the adoption of Metaverse-based software among 640 students pursuing associate degrees in computer programming within the framework of the extended Technology Acceptance Model (TAM). While previous research has explored general factors influencing technology adoption, gaps remain regarding the specific role of perceived complexity, enjoyment, and student self-efficacy within technical education contexts. The model designed to explain students’ behavioral intentions toward Metaverse-based technologies was analyzed and validated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study findings indicated that students’ perceived ease of use (PEOU) and perceived usefulness (PU) significantly influenced their behavioral intentions (BI) regarding the Metaverse. Among the external factors included in the model, it was determined that social influence (SIE), peer influence (PIE), and personal innovativeness (PI) variables significantly affected PU. In contrast, the perceived complexity (PC) variable significantly impacted PEOU and PU. This article examines the opportunities and challenges that Metaverse technologies bring to learning in computer programming studies and discusses the proposed research model for explaining students’ behavioral intentions. The study aims to develop new approaches that can offer significant opportunities in this field by establishing a framework for future research on the applications of the Metaverse in education.