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Feedback Literacy

created 2026-08-15 · updated 2026-09-30

Feedback literacy — the capabilities and dispositions students need to understand, evaluate, and act on feedback to improve their learning. It is the learner-side counterpart to feedback provision: whereas AI Feedback Quality and Feedback Loop concern the quality and mechanics of the feedback system, feedback literacy concerns the learner's capacity to seek, make sense of, judge, and use feedback productively.

Questions to Consider

  • Well-designed feedback only helps students who can interpret and act on it. If two students receive identical feedback and learn different amounts, where does the difference live — and is it the student's fault or the system's?
  • Research finds that students with stronger feedback literacy benefit more from AI feedback, while weaker-literacy students show minimal or even negative effects. What does that suggest about simply adding AI feedback to a course without also building students' capacity to use it?
  • Feedback literacy includes seeking feedback, making judgments, managing affect, and acting on feedback — not just receiving it. When did you last seek out feedback rather than wait for it, and what made you brave enough (or not) to do so?
  • If AI can now generate abundant, instant feedback, has the bottleneck shifted from feedback provision to the learner's ability to use it? What might change about how you design feedback if you saw students' feedback literacy as the real constraint?

Introduction

Feedback literacy matters because well-designed feedback only helps students who can interpret and act on it. A student who cannot evaluate whether AI-generated feedback is accurate, or who does not know how to turn feedback into a concrete revision, learns far less from the same feedback than a more feedback-literate peer. As AI reshapes feedback provision, feedback literacy has become a central boundary condition for whether AI feedback improves learning.

What feedback literacy is

Feedback literacy is widely framed as a set of interrelated capabilities — the capacity to appreciate feedback, make judgments, manage affect, and take action (after Carless & Boud). The knowledge base's articles cluster feedback literacy around several capabilities:

  • Seeking and eliciting feedback — proactively requesting feedback rather than passively receiving it.
  • Making judgments — evaluating the accuracy and usefulness of feedback, including feedback produced by AI.
  • Sense-making — interpreting feedback in relation to task goals and criteria, and understanding what it implies for improvement.
  • Managing affect — engaging productively with feedback without being discouraged or over-inflated by it.
  • Acting on feedback — translating feedback into concrete revisions or changes in approach (Feedback Loop, Self-Regulated Learning).
  • Judging evaluative authority — a further capacity proposed by AlGhamdi (2026): alongside appreciating feedback, making judgments, managing affect and taking action, students reasoning about AI-produced feedback needed to judge which source holds evaluative authority, separately from judging feedback quality. Because participants were told ChatGPT generated both score and feedback, their feedback literacy was exercised on the source and legitimacy of the evaluation, not only its content, and it produced demands for instructor validation rather than passive acceptance.

How feedback literacy appears in the research

  • Feedback literacy as a moderator of AI feedback value: Mendoza et al. (2026) show that feedback literacy moderates the link between ChatGPT acceptance and Self-Regulated Learning: students with stronger literacy perceive greater SRL benefit from AI feedback, while weaker-literacy students show minimal or even negative (Over-Reliance) effects. Feedback literacy is a boundary condition for whether students can "make sense of" AI feedback.

  • Feedback literacy as the pathway from GenAI use to critical thinking: in 421 Chinese undergraduates, GenAI use's association with self-reported critical thinking ran almost entirely through GenAI Feedback Literacy (β=0.185, 71.98% of the total effect), and that pathway strengthened as reflective thinking rose (index of moderated mediation = 0.030) (Yan et al. (2026)).

  • Feedback literacy predicts learning from AI-assisted writing: Hawkins et al. (2026) find that feedback literacy was the only significant positive predictor of essay grade in an AI-enhanced essay-writing task, while Liu & Deris (2025) develop and validate an AI Feedback Literacy (AIFL) scale and show it predicts feedback uptake.

  • Frameworks for GenAI-enabled feedback engagement: Zhan, Boud, Dawson & Yan (2025) (Boud and Dawson are leading feedback-literacy scholars) argue GenAI can enable student feedback engagement, mapping a cyclical self-AI Regulation in Education feedback model onto the eliciting/processing/enacting phases.

  • AI feedback arrives without the teacher's framing: Brunnström and Palmqvist (2026) argue that GenAI makes feedback literacy more demanding, because responses are generated interactively and without a teacher's immediate framing: students must interpret, evaluate, engage with and use them — including deciding when a response is too abstract, when to persist, and when to return to conventional resources. Across their eight-exchange demonstration, simplification and usable structure appeared only after learner interventions, making the eliciting/processing/enacting cycle of feedback engagement something the student has to drive (Zhan, Boud, Dawson & Yan mapped that cycle for GenAI; Self-Regulated Learning).

  • Feedback literacy in AI-assisted writing and EAP: Feedback literacy scripts and second-rater mechanisms train students to engage critically with AI feedback during writing revision, shifting revision toward argument-level improvement rather than surface edits.

  • Automated feedback tools for literacy development: Tubino & Adachi (2025) argue AI automated feedback tools should be reframed as instruments for developing students' feedback literacy, not just providing more feedback.

  • Peer feedback and feedback literacy: Irwin & Muller (2025) position GenAI within EFL peer feedback to train feedback literacy and enable uptake in speaking classes, and scaffolding studies compare GenAI vs. human peers in fostering self-regulated feedback.

  • Feedback stored as durable, consultable data. Pairing ChatGPT with an e-portfolio raised speaking performance and feedback literacy together (partial η² = 0.218 for the latter), as students treated archived feedback from lecturer, peers, and AI as data to reconcile rather than a one-off correction (Laksana et al. (2026)).

  • Feedback literacy in learning analytics and GenAI dashboards: Jin et al. (2025) examine how students perceive GenAI-powered Learning Analytics feedback from a feedback-literacy perspective.

  • Feedback literacy as a goal of AI-literacy and assessment design: Richmond & Nicholls (2025) use a process-over-artifact assessment in which students critique ChatGPT output against a rubric to build feedback, psychological, and AI literacies together; learner-centered AI feedback and care-full feedback design link feedback quality to the learner's capacity to engage.

Why feedback literacy matters for AI in education

AI changes feedback in two directions that both raise the stakes of feedback literacy. First, AI dramatically increases the volume and immediacy of feedback (AI Feedback Quality, Feedback Loop), so students confront far more feedback they must triage and evaluate. Second, AI-generated feedback carries distinct risks — inaccuracy, hallucination, and the "illusion of mastery" — that demand critical evaluation skills and vigilance against Over-Reliance and misattribution. Feedback literacy therefore becomes a core component of AI Literacy: knowing not only how to prompt an AI for feedback, but how to judge whether the feedback is worth acting on and how to convert it into genuine learning rather than task completion.

Bearman and colleagues (2026) add a further demand to that list. In their workshop study, students were not managing one feedback source but a network: they weighed educator comments against AI output, rubrics, course materials and peers, and when human feedback was slow they kept working with AI as a holding pattern, revising later if it had misled them. The authors suggest students can act as their own human-as-the-loop by managing those relations from the granular task to the broader trajectory, which is itself a feedback-literacy capability. Their warning is that thin or late educator comments are what push students toward unverified sources.

Feedback literacy connects to AI Feedback Quality and Feedback Loop (the provision side it complements), Formative Assessment (the assessment cycle it feeds), and Self-Regulated Learning (the Self-Assessment and adaptation it supports). It is a subset of AI Literacy when applied to AI-generated feedback, intersects with Peer Assessment in collaborative contexts, and is particularly consequential for Writing. It also connects to Metacognition and Trust Calibration — the ability to judge whether feedback is trustworthy.

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Metadata

DisciplineWriting Education
Learning and instructionSelf-Regulated Learning
Assessment and measurementAI Feedback Quality, Feedback, Feedback Literacy, Formative Assessment, Peer Assessment
Foundations of AI in educationAI Literacy
SourceHistoryEditChat

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