Ying Zhan, James Wood, David Carless & Zi Yan (2026) — Assessment & Evaluation in Higher Education 51(5), 811–820. Editorial introducing the AEHE special issue on feedback and generative AI. doi:10.1080/02602938.2026.2672830.
📄 Full text (Taylor & Francis, OA)
Summary
This editorial synthesises the seven papers of the AEHE 51(5) special issue on feedback in the age of generative AI. Its central claim: the question is not whether GenAI feedback is useful, but how human and GenAI feedback can be combined to sustainably support learning rather than merely improve immediate performance. Teacher and student feedback literacy are necessary but not sufficient — what is also required is deliberate pedagogic design, institutional conditions that prioritise learning over efficiency, and the purposeful development of human judgement that cannot be delegated to the tool.^[raw/papers/tandf-2026-feedback-futures-genai.md]
The issue's papers include four also ingested into this wiki: learner-centered-feedback-ai, chatgpt-feedback-engagement-genai, genai-teacher-feedback-comparison, and care-full-feedback-genai.
Five tensions in GenAI feedback
The editors distill five recurring tensions from the special issue:
1. Usefulness, trust, and uptake. GenAI is valued for speed, clarity, and accessibility, while teacher feedback is trusted for contextual understanding, disciplinary expertise, accountability, and human connection. Uptake depends on experienced care, recognition, and presence — relational conditions GenAI struggles to reproduce.^[raw/papers/tandf-2026-feedback-futures-genai.md] 2. Immediate task achievement vs longer-term learning. GenAI excels at helping students complete the task at hand but may orient them toward performance/avoidance goals rather than mastery — echoed in chatgpt-feedback-engagement-genai's finding of weak metacognitive engagement and one-off interactions.^[raw/papers/tandf-2026-feedback-futures-genai.md] 3. Agency vs dependency. Agency can be extended via iterative prompting and comparison, but may become "thinner" when students stay active at the level of interaction while ceding evaluative work to the system.^[raw/papers/tandf-2026-feedback-futures-genai.md] 4. Access, avoidance, and advantage. GenAI is unlikely to benefit all students equally; non-users cite trustworthiness, preference for human feedback, and academic integrity. Access alone does not guarantee educative uptake.^[raw/papers/tandf-2026-feedback-futures-genai.md] 5. Teacher judgement and labour redistribution. GenAI redistributes rather than removes teacher labour — teachers still assess accuracy, tone, relationality, and pedagogical value, and poorly designed tools can increase workload (see learner-centered-feedback-ai).^[raw/papers/tandf-2026-feedback-futures-genai.md]
Conditions of partnership
- GenAI and human feedback are complementary but only if the complementarity is specified — via sequencing, comparison, editing, and governance across multiple sources and moments ("an ecology of configured encounters").^[raw/papers/tandf-2026-feedback-futures-genai.md]
- The subtler risk is displacement, not replacement: the speed of GenAI feedback may gradually displace the slower, careful work of educational judgement without anyone consciously deciding it should.
- Human agency must be proactively maintained rather than assumed — humans remain responsible for judging, adapting, and rejecting GenAI responses.
- The editors warn against both "AI slop" and "human slop" (low-quality, standardised feedback from comment banks and templates), arguing GenAI may entrench the latter if ungoverned (see care-full-feedback-genai).^[raw/papers/tandf-2026-feedback-futures-genai.md]
Feedback literacy for GenAI contexts
- In a GenAI-enhanced essay-writing study in the issue, feedback literacy predicted performance while frequency of GenAI use, trust, and prior knowledge did not — the learner's capacity to seek, evaluate, and act on feedback shapes GenAI's educational value.^[raw/papers/tandf-2026-feedback-futures-genai.md]
- Existing feedback literacy frameworks (Carless & Boud 2018; Molloy et al. 2020) must be extended with GenAI-specific capacities: evaluative judgement (GenAI output can look authoritative while being hallucinatory), metacognitive skill (monitoring when GenAI supports vs narrows vs substitutes thinking), and ethical decision-making (when and how GenAI use supports rather than substitutes for students' own intellectual work).^[raw/papers/tandf-2026-feedback-futures-genai.md]
- Teacher feedback literacy (design, relational, pragmatic dimensions; Carless & Winstone 2023) is less well theorised: design requires workable GenAI+human feedback workflows; relational oversight cuts both ways (teacher feedback is often perceived as more negative/risky than GenAI — see genai-teacher-feedback-comparison); pragmatically, what must remain human-led is not only connection but accountable judgement.^[raw/papers/tandf-2026-feedback-futures-genai.md]
Governance and assessment design
- Policy cannot be purely top-down: acceptable GenAI support varies across courses and disciplines, and ambiguous institutional direction pushes responsibility down to individual teachers, individualising the challenge.
- Assessment reform should make learning processes visible rather than treating final products as direct evidence; institutions must be deliberate about when GenAI use is itself part of what is being assessed.
- Research should move beyond self-report toward in-situ methods (think-aloud, trace/log data, stimulated recall) and toward how learners orchestrate feedback across sources (human, GenAI, artefactual) that differ in credibility and value. Equity research should track not just tool access but educationally productive use.^[raw/papers/tandf-2026-feedback-futures-genai.md]
Related Pages
- agency-gap-ai-writing — Empirical evidence on the agency vs dependency tension
- learner-centered-feedback-ai — Teacher-facing AI feedback tools (PolyFeed) as reflective scaffolds
- chatgpt-feedback-engagement-genai — Student engagement with ChatGPT feedback across four dimensions
- genai-teacher-feedback-comparison — Large-scale student perception comparison of GenAI vs teacher feedback
- care-full-feedback-genai — Values-based, care-full approach to feedback in an age of GenAI
- feedback-loop — Feedback as a dynamic, relational process
- ai-literacy — Literacy capacities needed to use GenAI feedback productively
- over-reliance — The dependency side of the agency–dependency tension
- equity-in-ai-education — Access, avoidance, and advantage in GenAI feedback
- higher-ed — Deployment context (universities)
- human-in-the-loop-ai — Human agency and oversight in AI-supported feedback
Citation
APA: Zhan, Y., Wood, J., Carless, D., & Yan, Z. (2026). Feedback futures: beyond the limits of human and GenAI capacities. Assessment & Evaluation in Higher Education, 51(5), 811–820. https://doi.org/10.1080/02602938.2026.2672830