What is the future of feedback in higher education in the age of GenAI? This week's episode features an open-access Assessment & Evaluation in Higher Education article that posits 4 key values for integrating GenAI feedback into care-full feedback encounters.
What is the future of feedback in higher education in the age of GenAI? This episode features an open-access article that posits four key values for integrating GenAI feedback into care-full feedback encounters:
Winstone, Naomi E., Karen Gravett, Margaret Bearman, Christy Noble, Lasse X Jensen, Anna Jones, and Kelli Nicola Richmond. 2026. “The Care-full Craft of Feedback in an Age of Generative AI.” Assessment & Evaluation in Higher Education. https://doi-org.elon.idm.oclc.org/10.1080/02602938.2026.2643333
This episode was hosted, edited, and produced by Jessie L. Moore, Director of the Center for Engaged Learning and Professor of Professional Writing & Rhetoric.
60-Second SoTL is produced by the Center for Engaged Learning at Elon University.
Music: “Cryptic” by AudioCoffee.
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Jessie L. Moore:
What is the future of feedback in higher education in the age of GenAI? That’s the focus of this week’s 60-second SoTL from Elon University’s Center for Engaged Learning. I’m Jessie Moore.
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In “The Care-Full Craft of Feedback in an Age of Generative AI,” Naomi Winstone, Karen Gravett, Margaret Bearman, Christy Noble, Lasse Jensen, Anna Jones, and Kelli Nicola-Richmond outline four key values for feedback, acknowledging likely entanglements of human and non-human feedback while also promoting care-full—C-A-R-E-dash-F-U-L-L—feedback practice and research. Their article appears in Assessment & Evaluation in Higher Education and is available open-access.
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The authors first offer a brief—and helpful—summary of feedback research and practice, noting a recent shift from an information-centric cognitivist paradigm to a learning-centric process orientation in which feedback is central to assessing performance and guiding improvement. GenAI tools risk replicating information-centric orientations, producing copious amounts of feedback that hold little value if the comments aren’t used in some way to adjust future performance.
At the same time, GenAI tools have the potential to increase feedback encounters if students see GenAI feedback as more accessible than requesting educator feedback.
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Within this context, feedback processes must be understood as matters of care. The authors write, “Relationality and care include the relations we have to each other, but also to and with[in] a much broader, material, world that includes all the spaces, objects, and technologies of education. Thinking about feedback process as matters of care, and as care-full, prompts educators to consider teaching and learning not as a mode of information transmission, but as affective and relational.”
Feedback encounters can include both human and non-human feedback, but that entangled feedback also must be managed with care, and a care-full approach to feedback aligns with four core values.
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First, feedback processes should support meaning-making. Any integration of GenAI feedback should be grounded in the understanding that feedback is a complex, situated, iterative, affective, and relational process. Absent student sense-making and uptake, speeding up or outsourcing feedback with GenAI won’t support student learning.
Second, feedback processes should build relationships. Although GenAI feedback might minimize the perceived risk of seeking help from human educators, human feedback has the potential to be more relational and foster trust. Moreover, learning to engage with human feedback encounters could contribute to career-readiness.
Third, feedback processes should be trustworthy. The authors summarize studies suggesting that students place more trust in feedback identified as human feedback than in GenAI-identified feedback, and students’ trust in GenAI can grow slowly and erode quickly depending on the accuracy and reliability of feedback, with less tolerance for GenAI errors than human errors.
Fourth, feedback is a professional craft. While GenAI might offer efficiency, it falls short on crafting feedback encounters as relational, dialogic, and iterative. A care-full approach to integrating GenAI feedback keeps human educators in the feedback loop and recognizes that as a professional craft, feedback skills require practice and development.
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With these core values in mind, the authors argue that a care-full approach to the integration of GenAI feedback must demonstrate
To learn more about this essay, visit our show notes for a link to the open access article where we also link to Winstone and colleagues’ “Manifesto for feedback in the age of generative artificial intelligence.”
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Jessie L. Moore:
Join us for our next episode of 60-second SoTL from Elon University’s Center for Engaged Learning for another snapshot of recent scholarship of teaching and learning. Learn more about the Center at www.CenterForEngagedLearning.org.
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