The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
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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In the citing paper
“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
Citations
Cites
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches
Creswell, J. W. (1994) · Cited by 35,213
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches
ภู่พันธ์, ศ. (2018) · วารสารวิธีวิทยาการวิจัย (Journal of Research Methodology: JRM) · Cited by 12,776
School Engagement: Potential of the Concept, State of the Evidence
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004) · Review of Educational Research · doi:10.3102/00346543074001059 · Cited by 12,581 · cited 1×
“The widely used categorisation of feedback engagement is based on the tripartite model of engagement proposed by Fredricks et al. (2004), namely, behavioural, cognitive and affective dimensions (e.g., Ellis, 2010; Shen & Chong, 2023; Zhang, 2022).”
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 3×
“Furthermore, GenAI provides personalised suggestions for students in non-judgemental language through a user-friendly interface (Huang et al., 2023; Wongvorachan et al., 2022) and could even directly revise students’ work or correct their mistakes if requested (Kasneci et al., 2023).”
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1×
“GenAI has been trained on datasets that dominantly reflect US standardised English conventions (Bender et al., 2021).”
Zimmerman, B. J. (2000) · Handbook of Self-Regulation · doi:10.1016/b978-012109890-2/50031-7 · Cited by 6,069 · cited 1×
“A pathway is suggested here based on a cyclical self-regulation learning model, which consists of three sequential phases, namely forethought, performance control and self-reflection (Zimmerman, 2000).”
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631
The development of student feedback literacy: Enabling uptake of feedback
Carless, D., & Boud, D. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1463354 · Cited by 2,428 · cited 3×
“The stage of enacting feedback requires students to act upon their interpretation of the comments that they have received to produce subsequent work that closes the feedback loop (Carless & Boud, 2018).”
Self-citationChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806
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Research works printed but not linked: 2
- 10.3102/00346543029848 · DOI
- Wongvorachan, T., Lai, K. W., Bulut, O., Tsai, Y.-S., & Chen, G. (2022). Artificial intelligence: Transforming the future of feedback in education. Journal of Applied Testing Technology, 23(1), 95–116. http://jattjournal.net/index.php/atp/article/view/170387
Cited by
Sorted by citations
Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2502582 · Cited by 211
“From a conceptual perspective, Zhan et al. (2025) propose that feedback engagement is a means of reconciling these feedback skills with their environment in terms of eliciting, processing and enacting feedback.”
Han, X., Peng, H., & Liu, M. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100714 · Cited by 70
Xie, X., Zhang, L. J., & Wilson, A. (2025) · Behavioral Sciences · doi:10.3390/bs15070884 · Cited by 25
Yin, X., Ye, J., Yu, S., Li, H., Liu, Q., & Zhao, G. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105434 · Cited by 16
Lin, X. Y. (2025) · The Australian Educational Researcher · doi:10.1007/s13384-025-00856-y · Cited by 13
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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