Redefining Theoretical Frameworks for Educational Technology Adoption: A Meta-Analytic Synthesis of AI/VR/AR Continuance Intentions and Cross-Sector Model Transferability

Abstract

This meta-analysis examines users' continuous intentions toward using AI, VR, and AR technologies in educational contexts. Combining the data from studies in mobile learning, mobile banking, and ChatGPT, this paper discusses the applicability of various theoretical frameworks in user behavior. The review reveals that specific theories, including the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model (ECM), hold high value for understanding the long- term use of such technologies, steadily influenced by factors like perceived usefulness, system quality, and users' satisfaction. These findings add to knowledge in technology adoption by introducing modifications to existing models into new integrated forms. In addition, the study has implications for enabling the persistent use of AI, VR, and AR in education; it could help educators, developers, and policymakers create a climate that supports the ongoing use of educational technologies.