Exploring the Impact of AI Enhanced Feedback on EFL Learners’ Motivation and Engagement

Abstract

This study investigates how AI-enhanced feedback influences the motivation and engagement of English as a Foreign Language (EFL) learners in Guangzhou, China. Grounded in Self-Determination Theory (SDT) and the multidimensional framework of engagement, the study conceptualizes AI feedback as encompassing autonomy, competence, and relatedness, based on data collected from 286 undergraduates aged 17-23 across four academic levels with varying English proficiency. Data were analyzed using SPSS for descriptive statistics, reliability checks, exploratory factor analysis (EFA), and correlation testing, and AMOS for confirmatory factor analysis (CFA), structural equation modeling (SEM), and mediation analysis. The results showed that AI-enhanced feedback significantly strengthened both intrinsic and extrinsic motivation, with intrinsic motivation serving as a stronger predictor of behavioral, cognitive, and emotional engagement. Motivation, acting as a partial mediator between AI feedback and engagement, suggests that well-designed feedback systems can foster learner involvement by enhancing the motivational quality of feedback. Theoretically, these findings extend SDT’s explanatory power in technology-mediated learning. At the same time, in practice, they offer insights for educators, system designers, and policymakers seeking to promote sustainable engagement and personalized feedback in AI-supported EFL contexts.