A Study on the Impact of Self-Efficacy on College Students’ Acceptance of Generative AI-empowered English Learning: Based on the Technology Acceptance Model

This study, grounded in the Technology Acceptance Model (TAM), analyzes questionnaire data from 226 university students using Structural Equation Modeling (SEM). It investigates the predictive effects of self-efficacy (SE) on students’ perceived usefulness (PU), perceived ease of use (PEOU), attitude toward use (ATT), and behavioral intention (BI). Additionally, it explores the direct and indirect mediating roles of the aforementioned three variables between self-efficacy and behavioral intention. The results indicate that university students generally show a high level of acceptance toward generative artificial intelligence in English learning. The predictive relationships among the core variables of the Technology Acceptance Model are validated, with self-efficacy having a significant positive impact on all four variables. Furthermore, the mediating effects of perceived usefulness, perceived ease of use, and attitude toward use between self-efficacy and behavioral intention are significant, with a continuous mediation path observed. Among these, the mediation effect of attitude toward use is the most prominent. These findings provide insights into how universities can optimize the implementation of generative artificial intelligence in English teaching practices and enhance learners’ acceptance of technology.