Bridging AI and pedagogy: how AI-adaptive feedback shapes Chinese EFL students’ writing engagement, metacognitive writing strategies, and writing performance
Li Luo, Abdullahi Yusuf
Abstract
In EFL writing contexts, providing timely and individualized feedback remains a persistent challenge. Recent advances in AI-enhanced adaptive feedback offer new possibilities to support learner engagement, metacognitive strategy use, and writing performance. However, empirical evidence explaining the multi-dimensional effects of such feedback remains limited. This study employed a cross-sectional design to investigate how AI-adaptive feedback influences Chinese university EFL learners’ writing engagement, metacognitive writing strategies, and writing performance, while also examining the moderating role of feedback literacy. Data were collected from 829 participants across multiple Chinese higher education institutions using validated instruments measuring AI-adaptive feedback, writing engagement, metacognitive strategies, feedback literacy, and writing performance. Structural equation modeling revealed significant direct effects of AI-adaptive feedback on engagement (β = 0.54), metacognition (β = 0.47), and writing performance (β = 0.32), alongside mediating roles of engagement (β = 0.16) and metacognition (β = 0.23) on writing performance. Furthermore, feedback literacy strengthens relationships between AI-adaptive feedback on writing engagement (β = 0.18), metacognition (β = 0.22), and writing performance (β = 0.15). The findings highlight the importance of integrating adaptive feedback systems with feedback literacy instruction to optimize learning outcomes in EFL writing pedagogy.