Modeling Students’ Intention to Use Generative AI in EFL Learning: The Roles of Prompt Engineering Competence, Motivational Identity, and Perceived Usability

Abstract

The increasing integration of generative artificial intelligence (AI) in English as a Foreign Language (EFL) education requires a clearer understanding of the factors associated with students’ adoption intentions. Guided by the Technology Acceptance Model (TAM), this explanatory sequential mixed-methods study examines how prompt engineering competence and motivational identity are associated with students’ behavioral intention (BI) to use generative AI tools, with perceived usability conceptualized as a second-order construct comprising perceived usefulness (PU) and perceived ease of use (PEU). Survey data from 470 undergraduate students were analyzed using structural equation modeling. The results indicate that both prompt engineering competence (PEC) and motivational identity (MI) are associated with the perceived usability of AI tools (PUAI), which in turn predicts BI. Mediation analysis yielded findings consistent with a full mediation pattern under the bootstrapped estimation procedure, suggesting that perceived usability mediated the relationships between prompt engineering competence, motivational identity, and behavioral intention. Follow-up interviews with 10 students provided explanatory insights, indicating that competence and motivation contribute to AI use primarily when they enhance perceptions of usefulness and ease of use. Overall, the study provides context-specific evidence extending TAM within AI-supported EFL learning and offers practical implications for fostering effective prompting skills and learner engagement.