Interactive AI tools, personalized feedback, and EFL learners’ intention to use AI: an extended TAM study in Egyptian higher education

Introduction Despite the growing use of generative artificial intelligence (AI) in English as a Foreign Language (EFL) education, how interactive AI features and personalized learning feedback shape learners' acceptance remains insufficiently understood. This study examines their influence on Egyptian university students' behavioral intention to use AI tools through an extended Technology Acceptance Model (TAM), focusing on the mediating roles of perceived usefulness and perceived ease of use. Methods An explanatory sequential mixed-methods design was employed. Survey data from 596 Egyptian university students were analyzed using structural equation modeling (SEM), followed by thematic analysis of semi-structured interviews with eight purposively selected participants. Results Perceived usefulness and perceived ease of use significantly predicted behavioral intention. Interactive AI features influenced intention indirectly through perceived usefulness, whereas personalized learning feedback influenced intention indirectly through perceived ease of use. Interviews revealed that interactivity supported engagement, clarification, and learner control, while personalized feedback facilitated error correction, reduced ambiguity, and alleviated anxiety. Participants viewed AI tools as flexible learning support rather than replacements for human instruction. Discussion The findings highlight the importance of pedagogically meaningful interaction and personalized feedback in shaping EFL learners' acceptance of AI tools. Responsible integration should emphasize learner agency, critical evaluation of AI-generated responses, and continued human pedagogical guidance.