Engaging with AI writing tools: a reflection-based study of student feedback literacy

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.

Name thesis:degree_name

Ed.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Educ Policy, Orgzn & Leadrshp

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Andreas, Natalie Bidnick

Contributors dc:contributor

• Magee, Liam
• Cope, William
• Zhu, Xinran
• You, Yu-Ling

• ai in education, feedback literacy

Statement dc:rights

• Copyright 2025 Natalie Andreas

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132679

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132679