Factors influencing the actual use of generative AI in college English learning: integrating TAM and innovativeness

This study aims to produce empirical insights into how students in Chinese higher vocational colleges view and use generative AI in EFL learning. This quantitative study uses the technology acceptance model (TAM) to analyze EFL learners' attitudes, intentions, and behaviors about utilizing generative AI in EFL learning with Structural Equation Modelling. 911 English as a foreign language(EFL) learners completed a questionnaire measuring personal innovativeness, perceived ease of use, perceived usefulness, attitude, behavioral intention, and actual use. Structural equation modeling investigations show that Perceived Ease of Use does not directly predict learners' attitudes but can impact them through the full mediator Perceived Usefulness. Positive attitudes towards generative AI and personal innovativeness are associated with increased behavioral intention. Behavior intention predicts the actual application of generative AI in English learning. This study demonstrates the effectiveness of generative AI as a language-learning tool for EFL learners, promoting personalized and sustainable learning.