Feedback Literacy and EFL Learner Engagement With ChatGPT Feedback: Predicting Feedback Uptake and Perceived Usefulness
Open accessAbstract
The proliferation of generative AI tools such as ChatGPT has transformed feedback provision in EFL writing, offering scalable and immediate support to learners. However, learner engagement with AI-generated feedback remains highly variable, raising questions about the internal mechanisms that shape feedback uptake. This study investigates how feedback literacy predicts both the behavioral adoption and perceived usefulness of ChatGPT-generated feedback among EFL learners, while also examining whether perceived ease of use mediates this relationship. Data were collected from 51 Chinese university students through questionnaires and revision-based tasks across three ChatGPT-supported writing assignments. Results from linear regression and bootstrapped mediation analyses revealed that feedback literacy significantly predicted both successful feedback uptake (R² = .56) and perceived usefulness (R² = .42). Moreover, perceived ease of use partially mediated this relationship, suggesting a layered cognitive-affective mechanism underlying learners’ engagement with algorithmic feedback. These findings extend feedback literacy theory beyond interpersonal contexts to AI-mediated, non-dialogic writing environments. They also refine the Technology Acceptance Model by highlighting learner competence as a critical determinant of usability and value perceptions. Pedagogically, the study underscores the need to cultivate feedback literacy as a prerequisite for meaningful engagement with AI tools in writing instruction.
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In learning environments where AI-generated feedback comments may appear authoritative even when they require critical scrutiny, the requirement to judge before acting provided a structural prompt for reflective evaluation that feedback literacy research identifies as a prerequisite for productive feedback use.
“In learning environments where AI-generated feedback comments may appear authoritative even when they require critical scrutiny, this requirement to judge before acting provided a structural prompt for the kind of reflective evaluation that feedback literacy research identifies as a prerequisite for productive feedback use [21, 65].”
From Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment (2026) · cites this paper as [65]
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Cited by
Sorted by citations
LLM-Generated Feedback in L2 Writing: A Scoping Review
Craven, L., & Fredrick, D. R. (2026) · Education Sciences · doi:10.3390/educsci16081196 · Cited by 4 · see citing works
Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment
Alsaiari, O., Baghaei, N., Lodge, J. M., Gaševi'c, D., Winstone, N., & Khosravi, H. (2026) · arXiv · doi:10.48550/arxiv.2608.11625
“Yang et al. [65] found that feedback literacy predicted both the uptake and perceived usefulness of AI-generated feedback comments.”
Sheng, N. Y., & Yaccob, N. S. (2026) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2026.100601289
Elyacoubi, H., Baytar, E. M., Saqri, N., & Ouchaouka, L. (2026) · Computers · doi:10.3390/computers15100665
- Authors
- Yang, Y., Hussin, S., & Hashim, H.
- Affiliations
- National University of Malaysia (MY)
- Venue
- Theory and Practice in Language Studies
- Publisher
- Academy Publication
- Published
- 2025-11-03
- Volume
- 15
- Issue
- 11
- Pages
- 3760-3771
- ISSN
- 1799-2591 (ISSN-L)
- 2053-0692
- OpenAlex
- W4415786367
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 7.14
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Theory and Practice in Language Studies (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
- feedback literacy, ChatGPT, AI-generated feedback, EFL writing, student engagement, feedback uptake, perceived usefulness, perceived ease of use, technology acceptance model, generative AI in language teaching, Chinese university students
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