AI competency, attitudes, and experience as predictors of overall learning interaction via AI integration and creative tasks in GenAI-supported EFL classrooms

Abstract

Although generative AI is increasingly integrated into higher education, its impact on student’s learning experience remains unclear. This study examined factors predicting learning interactions in EFL (English as a Foreign Language) contexts, focusing on student’s AI competency, attitudes, and experience. Grounded in constructivist theory and the WEST model (Will, Experience, Skill, and Tools), a questionnaire was administered to 884 students at a Chinese higher vocational college. Structural equation modeling shows that AI integration and involvement in creative tasks directly predict learning interaction, while competency, attitudes, and experience exert indirect effects via these variables. Theoretically, this study provides quantitative evidence that student’s AI-related characteristics may contribute to learning interaction through AI-supported learning practices and creative task involvement. In practice, students can be more active in classroom interactions by adopting AI tools and participating in appropriate creative tasks designed by their teachers. Consequently, teachers play an important role in determining how AI tools are integrated and what types of tasks are assigned to students in the English classroom to support a better interactive learning environment.