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📄 Research Article

The care-full craft of feedback in an age of generative AI

● high · 2026-08-03generative-ai, feedback-loop, higher-ed, educational-theory, equity, teacher-role, equity-in-ai-education, human-in-the-loop-ai, ethics, ai-education

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.

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.
  • "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.
  • 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Teacher Feedback Comparison), algorithm aversion makes AI trust decline sharply after errors, and GPT-4 limitations (outdated data, over-generalisation, hallucinations) strike at expertise, integrity, and benevolence — the dimensions of epistemic trust.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.

    • 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.

    • Connected Concepts

  • Equity In AI Education
  • Higher Ed
  • Human In The Loop AI
  • Teacher Role
  • AI Education
  • Ethics
  • Generative AI

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    • Citation

    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