Feedback futures: Beyond the limits of human and GenAI capacities
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Generative artificial intelligence (GenAI) disrupts university assessment systems by offering students tools that produce plausible text on demand. It shifts feedback into a new phase, not only by ...
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GenAI can increase learner control and access to immediate, tailored support.
“It can increase learner control and access to immediate, tailored support (Zhan et al., 2026), but it can also produce an apparently competent answer before the learner has undertaken the effort needed to build a durable knowledge structure or professional judgement (Balart et al., 2026).”
From Between Co-Intelligence and Cognitive Offloading: Educators’ Perspectives from Dutch Academic Medical Settings on Generative AI, Critical Thinking, and Adult Professional Learning (2026) · cites this paper as Zhan et al., 2026
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Cites
Developing a learning-centred framework for feedback literacy
Molloy, E., Boud, D., & Henderson, M. (2020) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1667955 · Cited by 555 · see citing works
Luo (Jess), J. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2309963 · Cited by 366 · cited 1× · see citing works
“These kinds of threats to trust, student-teacher relationships and institutional reputation are readily apparent when originality of student work becomes contested (Luo 2024).”
The challenges of feedback in higher education
Henderson, M., Ryan, T., & Phillips, M. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1599815 · Cited by 335 · see citing works
Developing evaluative judgement for a time of generative artificial intelligence
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2335321 · Cited by 312 · cited 2× · see citing works
“What matters is whether students retain control of the judgments that count: whether they can question GenAI outputs, decide what role they should play, and take genuine responsibility for the work they submit (Bearman et al. 2024).”
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 145 · see citing works
Talk is cheap: why structural assessment changes are needed for a time of GenAI
Corbin, T., Dawson, P., & Liu, D. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2503964 · Cited by 107 · see citing works
Generative artificial intelligence as an enabler of student feedback engagement: a framework
Zhan, Y., Boud, D., Dawson, P., & Yan, Z. (2025) · Higher Education Research & Development · doi:10.1080/07294360.2025.2476513 · Cited by 94 · see citing works
Self-citationThe GenAI divide among university students: A call for action
Beckman, K., Apps, T., Howard, S. K., Rogerson, C., Rogerson, A., & Tondeur, J. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.101036 · Cited by 49 · see citing works
Walton, J., Bearman, M., Crawford, N., Tai, J., & Boud, D. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2570328 · Cited by 28 · cited 3× · see citing works
“Metacognitive skills matter because students need to monitor when GenAI support is helping them think more effectively, when it is narrowing that thinking, and when it is beginning to substitute for their own reflective work (Zhan and Yan 2026; Walton et al. 2025).”
InfluentialFeedback encounters in doctoral supervision: the role of generative AI chatbots
Jensen, L. X., Bearman, M., Boud, D., & Konradsen, F. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2478155 · Cited by 20 · see citing works
Hawkins, B., Taylor-Griffiths, D., & Lodge, J. M. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2492070 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
- Aldino, A. A., B. Maheshi, Y. Li, Y. Zhou, Y. S. Tsai, D. Gašević, and G. Chen. 2026. "Enhancing Learner-Centered Feedback with AI: Teachers' Practices and Perceptions. " Assessment & Evaluation in Higher Education 51 (5):1-18. https://doi.org/10.1080/02602938. 2026.2638920. · DOI
- Buckley, A. 2025. "What's so New about the "New Paradigm" of Feedback?" Assessment & Evaluation in Higher Education 51 (1): 1-15. https://doi.org/10.1080/02602938.2025.2588386. · DOI
- Carless, D., and D. Boud. 2018. "The Development of Student Feedback Literacy: Enabling Uptake of Feedback. " Assessment & Evaluation in Higher Education 43 (8): 1315-1325. https://doi.org/10.1080/02602938.2018.1463354. · DOI · Paper page
- Carless, D., and N. Winstone. 2023. "Teacher Feedback Literacy and Its Interplay with Student Feedback Literacy. " Teaching in Higher Education 28 (1): 150-163. https://doi.org/10.1080/13 562517.2020.1782372. · DOI · Paper page
- Chen, S., and D. Carless. 2026. "Teacher Feedback Dilemmas and the Use of GenAI: Challenges or Opportunities?" Innovations in Education and Teaching International 1-14. https://doi.org/ 10.1080/14703297.2026.2648040. · DOI
- Chung, J., M. Henderson, C. Slade, Y. Liang, N. Pepperell, T. Corbin, et al. 2026. "The Use and Usefulness of GenAI in Higher Education: Student Experience and Perspectives. " Computers and Education Open 10: 100347. https://www.sciencedirect.com/science/article/ pii/S2666557326000182. https://doi.org/10.1016/j.caeo.2026.100347. · DOI
- Corbin, T., J. Tai, and G. Flenady. 2025. "Understanding the Place and Value of GenAI Feedback: A Recognition-Based Framework. " Assessment & Evaluation in Higher Education 50 (5): 718-731. https://doi.org/10.1080/02602938.2025.2459641. · DOI · Paper page
- Cui, Y., Y. Meng, X. Qian, and L. Tang. 2026. "Developing a College Student Feedback Literacy Scale for the GenAI Context. " Assessment & Evaluation in Higher Education 51 (5): 1-14. https://doi.org/10.1080/02602938.2025.2495048. · DOI
- Kaliisa, R., K. Misiejuk, S. López-Pernas, and M. Saqr. 2026. "How Does Artificial Intelligence Compare to Human Feedback? A Meta-Analysis of Performance, Feedback Perception, and Learning Dispositions. " Educational Psychology 46 (1): 80-111. https://doi.org/10.1080/01443 410.2025.2553639. · DOI
- Lee, M., D. T. Chen, R. J. Y. Tan, and W. L. D. Hung. 2024. "Re-Conceptualising Learner Feedback Agency: A Situational, Deliberative and Entangled Perspective. " Assessment & Evaluation in Higher Education 49 (5): 680-692. https://doi.org/10.1080/02602938.2024.2318281. · DOI
- Orfanidis, C. 2025. "Moral Diversity in Institutional Policies Governing the Student Usage of Generative AI: An International Comparison. " Higher Education Quarterly 79 (4): e70051. https://doi.org/10.1111/hequ.70051. · DOI
- Tlili, A., K. Saqer, S. Salha, and R. Huang. 2025. "Investigating the Effect of Artificial Intelligence in Education (AIEd) on Learning Achievement: A Meta-Analysis and Research Synthesis. " Information Development 41 (3): 825-842. https://doi.org/10.1177/02666669241304407. · DOI
- Tsao, J. 2025. "Trajectories of AI Policy in Higher Education: Interpretations, Discourses, and Enactments of Students and Teachers. " Computers and Education: Artificial Intelligence 1-15:100496. https://doi.org/10.1016/j.caeai.2025.100496. · DOI
- Weidlich, J., F. Gotsch, K. Schudel, C. Marusic-Würscher, J. Mazzarella, H. Bolten, … K. M. Merki. 2025. "Teacher, Peer, or AI? Comparing Effects of Feedback Sources in Higher Education. " Computers and Education Open 100300: 1-16. https://doi.org/10.1016/j. caeo.2025.100300. * · DOI · Paper page
- Winstone, N. E., K. Gravett, M. Bearman, C. Noble, L. X. Jensen, A. Jones, and K. Nicola-Richmond. 2026. "The Care-Full Craft of Feedback in an Age of Generative AI. " Assessment & Evaluation in Higher Education 51 (5): 1-17. https://doi.org/10.1080/02602938 .2026.2643333. · DOI
- Wood, J. 2023. "Enabling Feedback Seeking, Agency and Uptake through Dialogic Screencast Feedback. " Assessment & Evaluation in Higher Education 48 (4): 464-484. https://doi.org/10. 1080/02602938.2022.2089973. · DOI
- Wood, J., and E. Pitt. 2025. "Empowering Agency through Learner-Orchestrated Self-Generated Feedback. " Assessment & Evaluation in Higher Education 50 (1): 127-143. https://doi.org/10 .1080/02602938.2024.2365856. · DOI
- Zhan, Y. 2022. "Developing and Validating a Student Feedback Literacy Scale. " Assessment & Evaluation in Higher Education 47 (7): 1087-1100. https://doi.org/10.1080/02602938.2021.20 01430. · DOI
- Zhan, Y., and Z. Yan. 2026. "Students' Engagement with ChatGPT Feedback: Implications for Student Feedback Literacy in the Context of Generative Artificial Intelligence. " Assessment & Evaluation in Higher Education 51 (5): 1-14. https://doi.org/10.1080/02602938.2025.2471821 . · DOI · Paper page
Cited by
Sorted by citations
Bruijn, K., & Vlachopoulos, D. (2026) · Education Sciences · doi:10.3390/educsci16091470
Fong, C. J., Zarei, P., Schallert, D. L., Fard, S. M., & Dezhkameh, A. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100429
- Authors
- Zhan, Y., Wood, J., Carless, D., & Yan, Z.
- Affiliations
- Education University of Hong Kong (HK)
- Durham University (GB)
- University of Hong Kong (HK)
- Corresponding author
- Zhan, Ying
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2026-07-04
- Volume
- 51
- Issue
- 5
- Pages
- 811-820
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W7172272114
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 42.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- generative AI, university evaluation, student feedback, academic integrity, AI-generated text, assessment design, future of assessment
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