Bridging AI and pedagogy: how AI-adaptive feedback shapes Chinese EFL students’ writing engagement, metacognitive writing strategies, and writing performance
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…
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Cognitive engagement is conceptualized as students' investment in the cognitive work of learning rather than as a learning outcome score; performance outcomes should not be treated as direct measures of engagement.
“Cognitive engagement is commonly conceptualized as students’ investment in the cognitive work of learning (e.g., depth of processing, strategic effort, and self-monitoring/metacognitive regulation) rather than as a learning outcome score; accordingly, performance outcomes should not be treated as direct measures of engagement [25].”
From Comparing predictive, simulation-based and hybrid assessments in undergraduate process engineering (2026) · cites this paper as [25]
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- Authors
- Luo, L., & Yusuf, A.
- Affiliations
- Wuhan University of Engineering Science (CN)
- Sokoto State University (NG)
- Corresponding author
- Yusuf, Abdullahi
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2025-08-27
- Volume
- 51
- Issue
- 3
- Pages
- 508-531
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W4413770294
- Cited by
- 21
- References
- 77
- Citations by year
- 2026: 20 · 2025: 1
- FWCI
- 74.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Keywords
- EFL writing, writing engagement, writing performance, feedback literacy, Chinese university students, writing instruction, personalized feedback
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