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Feedback Literacy

● high · created 2026-08-15 · updated 2026-08-15 · feedback-literacy, feedback, ai-feedback-quality, formative-assessment, self-regulated-learning, ai-literacy, peer-review, writing-education

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.

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 wiki'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).

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 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-regulation feedback model onto the eliciting/processing/enacting phases.
  • 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 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.

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 Over-Reliance LLM Fallacy 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.

Connections to related concepts

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-evaluation and adaptation it supports). It is a subset of AI Literacy when applied to AI-generated feedback, intersects with Peer Review in collaborative contexts, and is particularly consequential for Writing Education. It also connects to Metacognition and Trust Calibration — the ability to judge whether feedback is trustworthy.

Connected Concepts

Connected Articles