Engaging with AI writing tools: a reflection-based study of student feedback literacy
Andreas, Natalie Bidnick
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https://hdl.handle.net/2142/132679
Description
Title
Engaging with AI writing tools: a reflection-based study of student feedback literacy
Author(s)
Andreas, Natalie Bidnick
Issue Date
2025-12-04
Director of Research (if dissertation) or Advisor (if thesis)
Magee, Liam
Doctoral Committee Chair(s)
Magee, Liam
Committee Member(s)
Cope, William
Zhu, Xinran
You, Yu-Ling
Department of Study
Educ Policy, Orgzn & Leadrshp
Discipline
Educ Policy, Orgzn & Leadrshp
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ed.D.
Degree Level
Dissertation
Keyword(s)
ai in education, feedback literacy
Abstract
This dissertation examines how graduate students engage with feedback from artificial intelligence (AI) writing tools and what these interactions reveal about feedback literacy. Using the Feedback Literacy Framework (Carless & Boud, 2018) and a qualitatively driven mixed-methods design, data were collected from 80 graduate students via the AI Feedback Reflection Survey and follow-up workshops that included elicitation tasks. Quantitative results showed frequent use of ChatGPT and Grammarly for clarity and surface-level improvement, while qualitative findings highlighted students’ interpretive, evaluative, and emotional engagement with AI feedback. Students described AI as both a cognitive scaffold that supports revision and an affective companion that reduces anxiety and builds confidence. Across datasets, participants demonstrated growing critical judgment and adaptability in managing AI feedback. The study concludes that AI feedback, when framed reflectively and ethically, can enhance feedback literacy and autonomy within contemporary writing instruction.
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