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Generative artificial intelligence as an enabler of student feedback engagement: a framework
Open accessAbstract
Despite the recognised importance of feedback in enhancing student learning, feedback practices in higher education have not achieved the expected effects. A primary issue lies in student disengagement, exacerbated by contextual constraints such as large classes and limited curriculum space and time. The advent of Generative Artificial Intelligence (GenAI) may help overcome these contextual constraints. However, GenAI also poses substantial challenges and ethical dilemmas during the feedback process. Meanwhile, it is essential to recognise that the feedback environment created by GenAI inevitably interacts with students’ personal factors, especially their feedback literacy, to jointly influence feedback engagement. Therefore, a question remains whether GenAI can be an effective enabler of student feedback engagement. To answer the question, based on a literature review and theoretical synthesis, we scrutinise student engagement with GenAI in three stages of the feedback process and discuss the interplay of student feedback literacy and the GenAI context. We suggest that the extent to which students are engaged with feedback depends on their degree of feedback literacy as orchestrated in the GenAI context. Finally, we propose a cyclical feedback framework consisting of feedback forethought, feedback control and feedback retrospect to enable student feedback engagement in a GenAI world.
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“This is reflected in the empirical literature, which has predominantly evaluated AI-generated feedback comments as static products, comparing their perceived quality or outcomes with human or peer-generated alternatives, rather than examining how the design of the feedback encounter itself shapes engagement [22, 23].”
From Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment (2026) · cites this paper as [23]
In the citing paper
“more recent scholarship increasingly argues that GenAI should be treated as a catalyst for much-needed pedagogical innovation particularly in assessment and feedback, rather than merely resisting or only mitigating the risks (Corbin et al., 2025b; Zhan et al., 2025).”
From Co-creating custom GPTs: an autoethnographic study of undergraduate students as partners in generative AI innovation (2026) · cites this paper as Zhan et al., 2025
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Metadata
- Authors
- Zhan, Y., Boud, D., Dawson, P., & Yan, Z.
- Affiliations
- Education University of Hong Kong (HK)
- Deakin University (AU)
- Corresponding author
- Zhan, Ying
- Venue
- Higher Education Research & Development
- Publisher
- Informa UK Limited
- Published
- 2025-03-19
- Volume
- 44
- Issue
- 5
- Pages
- 1289-1304
- ISSN
- 0729-4360 (ISSN-L)
- 1469-8366
- OpenAlex
- W4408647652
- Cited by
- 94
- References
- 57
- Citations by year
- 2026: 79 · 2025: 15
- FWCI
- 290.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Higher Education Research & Development (publishedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- The Education University of Hong Kong (#04A47)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Higher Education Practises and Engagement
- Keywords
- generative AI, feedback literacy, student engagement, feedback in higher education, feedback framework, feedback control, large classes
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