Modeling the Continuous Intention to Use Generative AI as an Educational Tool for EFL Learners among Vocational College Students in Guangzhou, China

Abstract

This study aims to model the continuous intention to use generative AI (GenAI) as an educational tool among English as a Foreign Language (EFL) learners in vocational colleges in Guangzhou, China, by integrating the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT). Data were collected from 419 vocational college students in Guangzhou enrolled in EFL courses. A structured questionnaire measured autonomy, relatedness, competence, perceived usefulness, and continuous intention. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that autonomy, relatedness, and competence significantly influence perceived usefulness, which, in turn, positively affects students' continuous intention to use GenAI. Furthermore, perceived usefulness mediates the relationships between the three independent variables (autonomy, relatedness, and competence) and continuous intention. The study is limited to vocational college students in Guangzhou, which may restrict the generalizability of the findings. Future research could expand the sample to different educational contexts. The findings provide valuable insights for educators and policymakers on how to enhance GenAI adoption in vocational education by fostering student autonomy, relatedness, and competence. This study extends TAM by incorporating SDT to explain both external and intrinsic factors influencing the continuous intention to use GenAI in educational settings, focusing on vocational students in a Chinese context.