AI Feedback Literacy in Higher Education: Understanding, Measuring, and Predicting Student Feedback Uptake

Summary

Liu and Deris (2025) introduce and validate the construct of AI feedback literacy (AIFL) — students' capacity to critically engage with, evaluate, and apply AI-generated feedback — in the context of English as a foreign language (EFL) writing in higher education. Drawing on Carless and Boud's (2018) feedback literacy framework and motivational models (self-determination theory, feedback-seeking theory, cost-value models), they developed a psychometric scale and tested it with 486 Chinese undergraduate students. Using confirmatory factor analysis, PROCESS-macro mediation modelling, and multiple regression, the authors show that AIFL significantly predicts uptake of AI-generated feedback, both directly and indirectly through motivational appraisals of perceived value and perceived cost. Behavioural engagement (practices) outperformed attitudinal disposition (attitudes) as a predictor of uptake, and AIFL development was driven by frequency of AI tool use rather than demographic background — raising equity concerns about access and opportunity in increasingly AI-mediated assessment environments.

Key Findings

AIFL is a measurable, two-dimensional construct. The 16-item AIFL scale (Attitudes and Practices subscales) showed strong psychometric properties: Cronbach's alpha 0.78–0.85, composite reliability above 0.70, AVE above 0.50 (0.52–0.60), and factor loadings of 0.58–0.83, supporting convergent validity and construct integrity.

AIFL predicts feedback uptake both directly and indirectly. The total effect of AIFL on uptake was significant (β = 0.42, p < 0.001), with a still-significant direct effect after adding mediators (β = 0.28, p < 0.001), indicating partial mediation. AIFL raised perceived value and lowered perceived cost, which in turn positively and negatively predicted uptake respectively; both bootstrapped indirect effects were significant.

Behavioural engagement outweighs attitudes. In a multiple regression explaining 46% of variance in uptake (R² = 0.46), perceived value was the strongest predictor (β = 0.32), followed by AIFL-Practices (β = 0.27), while AIFL-Attitudes was non-significant (β = 0.08, p = 0.091). The authors interpret this as evidence that feedback literacy develops through embodied, situated practice rather than belief alone.

Value matters more than cost. Perceived value was a stronger predictor of uptake than perceived cost, aligning with self-determination theory accounts in which value appraisals connect to autonomous motivation; the authors argue for designing feedback experiences that raise perceived relevance and learning value.

AIFL is shaped by use, not demographics. Only frequency of AI tool use significantly predicted AIFL (β = 0.29, p < 0.001); neither academic major nor year of study mattered. This shifts the equity conversation from individual disposition to structural access, warning that integration of AI feedback could widen digital literacy gaps for students with less exposure.

High-AIFL students differ meaningfully. Median-split comparisons showed high-AIFL students had significantly higher uptake (d = 0.80), higher perceived value (d = 0.64), and lower perceived cost (d = 0.60) than low-AIFL peers.

Implications

This work reframes debates about AI feedback from mere access to actionable literacy: simply providing AI tools does not guarantee learning gains, because the pedagogical impact of AI feedback depends on learners' readiness, confidence, and experience. For assessment design, the finding that behavioural engagement predicts uptake better than attitudes argues for tasks that require students to act on AI feedback — revising drafts, comparing against rubrics, engaging in peer discussion — rather than merely acknowledging it. For equity, the result that exposure drives AIFL means institutions should deliberately scaffold and broaden access to AI-mediated feedback for students unlikely to engage autonomously, and should embed AI feedback within iterative drafting cycles aligned to disciplinary standards. For educators, the paper positions teachers as critical mediators of automated input — modelling reflective interpretation, facilitating dialogue about AI strengths and limits, and maintaining relational trust — rather than being displaced by AI. The authors call for reimagining feedback ecosystems where AI tools are embedded within pedagogically inclusive, reflective, and feedback-literate environments.

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Citation

Liu, K., & Deris, F. D. (2025). AI feedback literacy in higher education: Understanding, measuring, and predicting student feedback uptake. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2025.2587924