Feedback futures: beyond the limits of human and GenAI capacities

Synthesis: This editorial synthesizes 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 prioritize learning over efficiency, and the purposeful development of human judgment that cannot be delegated to the tool.

The issue's papers include four also ingested into this knowledge base: Enhancing learner-centered feedback with AI: teachers'' practices and perceptions, Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence, Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness, and The care-full craft of feedback in an age of generative AI.

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.
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 Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence's finding of weak metacognitive engagement and one-off interactions.
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.
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.
5. Teacher judgment and labor redistribution. GenAI redistributes rather than removes teacher labor — teachers still assess accuracy, tone, relationality, and pedagogical value, and poorly designed tools can increase workload (see Enhancing learner-centered feedback with AI: teachers'' practices and perceptions).

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").
• The subtler risk is displacement, not replacement: the speed of GenAI feedback may gradually displace the slower, careful work of educational judgment 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, standardized feedback from comment banks and templates), arguing GenAI may entrench the latter if ungoverned (see The care-full craft of feedback in an age of generative AI).

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.
• Existing feedback literacy frameworks (Carless & Boud 2018; Molloy et al. 2020) must be extended with GenAI-specific capacities: evaluative judgment (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).
• Teacher feedback literacy (design, relational, pragmatic dimensions; Carless & Winstone 2023) is less well theorized: design requires workable GenAI+human feedback workflows; relational oversight cuts both ways (teacher feedback is often perceived as more negative/risky than GenAI — see Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness); pragmatically, what must remain human-led is not only connection but accountable judgment.

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, individualizing 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, artifactual) that differ in credibility and value. Equity research should track not just tool access but educationally productive use.

What this means for practice

• Instructors. Design assessment sequences in which GenAI and human input are deliberately sequenced — drafting, GenAI-supported reflection, and revision cycles that require students to interpret and decide rather than simply accept generated output.
• Instructors. Teach students to orchestrate feedback across human, GenAI, and artifactual sources that differ in credibility, and to extend feedback literacy with evaluative judgment, metacognitive monitoring, and ethical decision-making about when GenAI use substitutes for their own work.
• Faculty developers. Build professional development around the design, relational, and pragmatic dimensions of teacher feedback literacy, which this editorial finds remain less well theorized in GenAI contexts than student feedback literacy.
• Administrators. Supply clear institutional direction on what GenAI support is acceptable, rather than leaving ambiguous policy to migrate downward and individualize the challenge for individual teachers.
• Researchers. Move beyond self-report toward in-situ methods — think-aloud, trace or log data, screenshot elicitation, stimulated recall — to study how learners and teachers actually work with feedback.

Limitations

• This is an editorial synthesizing one special issue (AEHE 51(5)); it reports no primary data of its own, and its claims are interpretive rather than empirically tested.
• The evidence base is the issue's seven papers, several of them small studies, so the editorial offers no sample statistics or effect sizes of its own.
• Some of the evidence it leans on is itself self-report — for example survey perceptions of trust in Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness — so claims about uptake and trust inherit that limitation.
• The editorial notes that the human feedback quality underpinning assumptions of relational superiority was rarely verified in the 41 studies of Kaliisa et al.'s meta-analysis, leaving that premise theoretical.

Citation

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