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Exploring Instructors' Views on Fine-Tuned Generative AI Feedback in Higher Education

Tzirides, Anastasia; Zapata, Gabriela; Bolger, Patrick; Cope, Bill; Kalantzis, Mary; Searsmith, Duane

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Authors

Anastasia Tzirides

Patrick Bolger

Bill Cope

Mary Kalantzis

Duane Searsmith



Abstract

This paper explores the integration of Generative Artificial Intelligence (GenAI) feedback into higher education. Specifically, it examines the views of 11 experienced instructors on fine-tuned GenAI formative feedback of student works in an online graduate program in the United States. The participants assessed sample GenAI reviews, and their perspectives were recorded through a numerical questionnaire and an open-ended survey. The findings revealed positive views overall, pervasive across the AI feedback. Numerical survey results showed that the feedback was generally deemed relevant, clear, actionable, useful, and comprehensive. Open-ended responses supported these findings, suggesting that GenAI feedback aligned well with course rubrics and provided actionable suggestions. Nevertheless, some limitations were identified, such as redundancy and lengthy suggestions that could overwhelm students. The study concludes with suggestions for the improvement of fine-tuned GenAI feedback to improve its effectiveness and enhance higher education students’ learning experiences, especially in online settings.

Citation

Tzirides, A., Zapata, G., Bolger, P., Cope, B., Kalantzis, M., & Searsmith, D. (2024). Exploring Instructors' Views on Fine-Tuned Generative AI Feedback in Higher Education. International Journal on E-Learning, 23(3), 319-334

Journal Article Type Article
Acceptance Date Dec 23, 2024
Online Publication Date Dec 30, 2024
Publication Date Dec 30, 2024
Deposit Date Jan 13, 2025
Publicly Available Date Jan 17, 2025
Journal International Journal on E-Learning
Print ISSN 1537-2456
Publisher Association for the Advancement of Computing in Education
Peer Reviewed Peer Reviewed
Volume 23
Issue 3
Pages 319-334
Public URL https://nottingham-repository.worktribe.com/output/44225492
Publisher URL https://www.learntechlib.org/primary/p/225173/

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