The Impact of AI-Based Learning Feedback on Academic Writing Competence and Learner Autonomy Among English Learners: A Self-Regulated Learning Perspective

Abstract

AI-based Learning Feedback (AIF) is increasingly used to improve academic writing in higher education; nonetheless, the relationships among AIF, writing competency, and learner autonomy are not yet sufficiently clarified. This study aims to examine the relationship between AI-based Learning Feedback (AIF) and Academic Writing Competence (AWC) and Learner Autonomy (LA) via two mediating processes of self-regulated learning: metacognitive self-regulation (MSR) and strategic motivational regulation (SMR). This study used a cross-sectional, quantitative survey method, involving 350 university students who were enrolled in academic writing courses. Data were collected using a 5-point Likert scale questionnaire, which assessed 27 observable characteristics; the data were then analyzed using the PLS-SEM method. The results showed that AI feedback (AIF) didn't directly affect academic writing competence (AWC) and learning autonomy (LA). In contrast, its effect was indirect, working through metacognitive self-regulation (MSR) and self-motivation regulation (SMR). These findings clarify the role of self-regulated learning as a mediator in the context of AI feedback, which has important implications for the design of AI feedback systems. Furthermore, the key purpose of these systems is to support learners advance as more effective writers and more independent in higher education.