Naomi E. Winstone, Karen Gravett, Margaret Bearman, Christy Noble, Lasse X Jensen, Anna Jones & Kelli Nicola-Richmond (2026) — Assessment & Evaluation in Higher Education 51(5), 911–927. doi:10.1080/02602938.2026.2643333.
📄 Full text (Taylor & Francis, OA)
Summary
A conceptual/position paper arguing that feedback in an age of GenAI must be understood as "matters of care" — ethical, relational practices rather than information transmission. It builds on a ten-principle Manifesto for Feedback in the Age of GenAI (Winstone et al. 2025, Copenhagen Feedback Symposium) and distils four core values for integrating GenAI into a multimodal feedback landscape: (1) feedback processes should support meaning-making, (2) build educative relationships, (3) be trustworthy, and (4) be respected as a professional craft.^[raw/papers/tandf-2026-care-full-feedback-genai.md]
Core arguments
- Feedback as more than comments. Feedback information ("bits and bytes") is only input; without student sense-making and uptake it does not constitute feedback. GenAI comment-generators risk replicating Sadler's "dangling data" critique — and may entrench an information-centric view the field spent decades moving past.^[raw/papers/tandf-2026-care-full-feedback-genai.md]
- "AI slop" and "human slop." GenAI's mass-produced, speedy content (Madsen & Puyt 2025's "AI slop") has a human analogue: comment banks, quick marks, and standardised templates that already weaken feedback practice. Quantity and speed ≠ quality.
- Relational recognition. Only human feedback can be recognitive — "the mutual acknowledgement of agency, vulnerability, and shared humanity" (Corbin, Tai & Flenady 2025); GenAI feedback is "extra-recognitive." Students in Otaki et al. (2026) described GenAI interactions as "each interaction feels like a new beginning," lacking the continuous timeline of human relationships.
- The safe-space paradox. GenAI feedback reduces perceived personal risk (asking "stupid" questions, exposing weaknesses) — a genuine benefit, especially in power-hierarchical cultures — but may come at the cost of priming students for the friction of professional feedback encounters.^[raw/papers/tandf-2026-care-full-feedback-genai.md]
- Epistemic trust. Trust is dynamic and relational ("a verb, not a noun"): students ascribe less trust to AI-generated than human feedback (Ruwe & Kuklick 2026; Henderson et al. 2025 — see genai-teacher-feedback-comparison), algorithm aversion makes AI trust decline sharply after errors, and GPT-4 limitations (outdated data, over-generalisation, hallucinations) strike at expertise, integrity, and benevolence — the dimensions of epistemic trust.
- Feedback as professional craft. Tuck's ethnography shows marking is non-linear, context-sensitive, and entangled with "readings of students" — including their "back stories." Multimodal feedback (written + dialogic + digital) is where this craft lives; GenAI lacks genuine dialogue. Risk: outsourcing feedback production erodes the craft (skill atrophy, "unthinking") and even human-in-the-loop approval can internalise AI bias.^[raw/papers/tandf-2026-care-full-feedback-genai.md]
Agenda for the future — four respects
- Respect for scholarship: design GenAI integration starting from known feedback challenges (e.g. ipsative feedback across modules) rather than from comment generation; research beyond self-report.
- Respect for equity: equitable access to meaningful feedback encounters; scaffold feedback literacies and evaluative judgement to avoid amplifying Matthew effects; respect conscientious objectors to GenAI.
- Respect for professional craft: distinguish what GenAI cannot replicate (relationship-rich, multimodal, dialogic feedback); protect against de-prioritising care-full feedback because it is labour-intensive.
- Respect for human connection: preserve situated, meaningful, care-full elements of feedback encounters; study how trust, connection, and engagement evolve as GenAI enters the ecosystem.^[raw/papers/tandf-2026-care-full-feedback-genai.md]
Related Pages
- feedback-futures-genai — Editorial foregrounding this paper's values (scholarship, craft, equity, connection) and the "human slop" concept
- learner-centered-feedback-ai — Empirical evidence that teachers calibrate AI tone to protect authenticity
- chatgpt-feedback-engagement-genai — Students' affective calm and selective trust with ChatGPT feedback
- genai-teacher-feedback-comparison — Trust gap (90.5% vs 60.1%) and "less risky" perception data
- feedback-loop — Feedback as process and relational encounter
- teacher-role — The professional craft and ethical judgement of educators
- equity-in-ai-education — Matthew effects and equitable access to feedback encounters
- human-in-the-loop-ai — Care-full oversight of AI-generated feedback
- educational-theory — Matters of care and affirmative ethics framing (theory anchor)
- higher-ed — Deployment context
Citation
APA: Winstone, N. E., Gravett, K., Bearman, M., Noble, C., Jensen, L. X., Jones, A., & Nicola-Richmond, K. (2026). The care-full craft of feedback in an age of generative AI. Assessment & Evaluation in Higher Education, 51(5), 911–927. https://doi.org/10.1080/02602938.2026.2643333