Anastasia Tzirides
Exploring Instructors' Views on Fine-Tuned Generative AI Feedback in Higher Education
Tzirides, Anastasia; Zapata, Gabriela; Bolger, Patrick; Cope, Bill; Kalantzis, Mary; Searsmith, Duane
Authors
Dr GABRIELA ZAPATA GABRIELA.ZAPATA@NOTTINGHAM.AC.UK
ASSOCIATE PROFESSOR IN EDUCATION
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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