Exploring factors influencing AI-powered E-learning system adoption intention: An empirical study on mediation and moderation effects

The prevalence of artificial intelligence (AI) technology in modern society has profoundly changed traditional communication and learning methods. As the application of AI technology in e-learning systems becomes increasingly pervasive, there is an urgent need for research on issues related to the behavioral intention of AI-powered e-learning systems. This study employs an integrated framework combining Innovation Diffusion Theory (IDT), the Technology Acceptance Model (TAM), and self-efficacy theory to analyze factors that empirically examine factors influencing college students' behavioral intentions in e-learning. It identifies the mediating mechanism underlying the relationship between adoption intentions and its antecedents and examines the moderating effect of self-efficacy. A purposive questionnaire was distributed online among college students. A total of 298 responses were drawn. A quantitative survey methodology included Chi-square analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The results show that college students' adoption intention determinants are AI-powered e-learning system traits (relative advantage, complexity, observability) and satisfaction. Furthermore, the impacts of AI-powered e-learning system traits on adoption intention are mediated by satisfaction. Self-efficacy positively moderates the impact of innovation traits on adoption intention. The discussion and implications present theoretical advancements in elucidating the mechanism of adoption intention and putting forward instructive recommendations for improving the adoption intention of technology-driven innovations in the digitalized education era.