Generative artificial intelligence as an enabler of student feedback engagement: a framework

Zhan, Y; Boud, D; Dawson, P; Yan, Z

Generative artificial intelligence as an enabler of student feedback engagement: a framework

Zhan, Y Boud, D

Dawson, P Yan, Z

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Publisher:: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Higher Education Research and Development, 2025, 44, (5), pp. 1289-1304
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Zhan, Y
dc.contributor.author	Boud, D https://orcid.org/0000-0002-6883-2722
dc.contributor.author	Dawson, P
dc.contributor.author	Yan, Z
dc.date.accessioned	2025-08-06T04:17:43Z
dc.date.available	2025-08-06T04:17:43Z
dc.date.issued	2025-01-01
dc.identifier.citation	Higher Education Research and Development, 2025, 44, (5), pp. 1289-1304
dc.identifier.issn	0729-4360
dc.identifier.issn	1469-8366
dc.identifier.uri	http://hdl.handle.net/10453/189210
dc.description.abstract	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.
dc.language	English
dc.publisher	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
dc.relation.ispartof	Higher Education Research and Development
dc.relation.isbasedon	10.1080/07294360.2025.2476513
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	13 Education
dc.subject.classification	39 Education
dc.title	Generative artificial intelligence as an enabler of student feedback engagement: a framework
dc.type	Journal Article
utslib.citation.volume	44
utslib.for	13 Education
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Design and Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-08-06T04:17:42Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	44
utslib.citation.issue	5

Abstract:

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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http://hdl.handle.net/10453/189210