{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132679"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132679","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Engaging with AI writing tools: a reflection-based study of student 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Andreas, Natalie Bidnick"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ed.D.","degree_level":"Dissertation","degree_discipline":"Educ Policy, Orgzn & Leadrshp","degree_department":null,"school":null,"contributors":["Magee, Liam","Cope, William","Zhu, Xinran","You, Yu-Ling"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["ai in education, feedback literacy"],"languages":["en"],"rights":["Copyright 2025 Natalie Andreas"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132679","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Magee, Liam","Cope, William","Zhu, Xinran","You, Yu-Ling"]},{"key":"dc:creator","label":"Author","values":["Andreas, Natalie Bidnick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-04"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educ Policy, Orgzn & Leadrshp"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ed.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ai in education, feedback literacy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Natalie Andreas"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132679"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Natalie Andreas, accepted the attached license on 2025-12-01 at 19:30.","The student, Natalie Andreas, submitted this Dissertation for approval on 2025-12-01 at 19:38.","This Dissertation was approved for publication on 2025-12-04 at 11:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23015 on 2026-02-19 at 18:46:29"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Engaging with AI writing tools: a reflection-based study of student feedback literacy"]}]}],"canonical_facts":{"dc:contributor":["Magee, Liam","Cope, William","Zhu, Xinran","You, Yu-Ling"],"dc:creator":["Andreas, Natalie Bidnick"],"dc:date":["2025-12","2025-12-04"],"dc:description":["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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Natalie Andreas, accepted the attached license on 2025-12-01 at 19:30.","The student, Natalie Andreas, submitted this Dissertation for approval on 2025-12-01 at 19:38.","This Dissertation was approved for publication on 2025-12-04 at 11:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23015 on 2026-02-19 at 18:46:29"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132679"],"dc:language":["en"],"dc:rights":["Copyright 2025 Natalie Andreas"],"dc:subject":["ai in education, feedback literacy"],"dc:title":["Engaging with AI writing tools: a reflection-based study of student feedback literacy"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Educ Policy, Orgzn & Leadrshp"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ed.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}