AI-Supported Feedback Literacy Among First-Year University Students

Sophie Williams

AI-Supported Feedback Literacy Among First-Year University Students

Author(s)	Dr. Sophie Williams
Country	United States
Abstract	Feedback is central to university learning, yet first-year students frequently enter higher education without fully developed capabilities for interpreting evaluative comments, judging the quality of their own work, regulating emotional responses to criticism, and converting feedback into concrete academic improvement. The rapid availability of generative artificial intelligence introduces a new feedback environment in which students can obtain explanations, revision suggestions, examples, rubric interpretations, and conversational guidance almost immediately. The educational value of such access, however, depends upon students' ability to question, verify, contextualize, and selectively act upon AI-generated advice. This study conceptualizes AI-Supported Feedback Literacy (AI-SFL) as the capacity to engage critically and productively with AI-mediated feedback while retaining learner judgment, disciplinary awareness, ethical responsibility, and appropriate reliance on human feedback. The framework builds upon Carless and Boud's established feedback-literacy model and recent research on AI feedback literacy, GenAI-supported learning analytics, and first-year feedback interventions. Carless and Boud conceptualize feedback literacy through appreciating feedback, making judgments, managing affect, and taking action, while recent work by Liu and Deris shows that AI feedback literacy can predict students' uptake of AI-generated feedback. Because no authentic first-year student dataset was supplied, the quantitative component employs a transparent simulation of 300 hypothetical first-year university students. The synthetic mean AI Feedback Literacy score was 60.5 on a 100-point scale, and AI feedback literacy showed a simulated positive correlation of r = 0.749 with feedback uptake. Students represented within the advanced literacy group achieved substantially higher simulated feedback-uptake and verification scores than those in the developing group. These numerical results are methodological demonstrations rather than empirical population estimates. The study concludes that universities should teach students not merely how to obtain AI feedback, but how to interpret it, compare it with assessment criteria, detect questionable advice, preserve academic voice, manage emotional responses, and translate credible feedback into independently reasoned revisions.
Keywords	AI feedback literacy; first-year university students; generative artificial intelligence; student feedback; feedback uptake; higher education; formative assessment; AI literacy; self-regulated learning
Field	Engineering
Published In	Volume 3, Issue 5, September-October 2022
Published On	2022-09-13

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Supported Feedback Literacy Among First-Year University Students

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