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Tzirides, AO, Zapata, GC, Bolger, P 
ORCID: https://orcid.org/0000-0002-5991-4013, Cope, B, Kalantzis, M and Searsmith, D,
2024.
Exploring instructors' views on fine-tuned generative AI feedback in higher education.
International Journal on E-Learning, 23 (3), pp. 319-334.
ISSN 1537-2456
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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.
| Item Type: | Journal article |
|---|---|
| Publication Title: | International Journal on E-Learning |
| Creators: | Tzirides, A.O., Zapata, G.C., Bolger, P., Cope, B., Kalantzis, M. and Searsmith, D. |
| Publisher: | Association for the Advancement of Computing in Education |
| Date: | 2024 |
| Volume: | 23 |
| Number: | 3 |
| ISSN: | 1537-2456 |
| Identifiers: | Number Type 10.70725/022533mcqtxp DOI 2422342 Other |
| Rights: | Copyright by AACE. Reprinted from with permission of AACE (https://aace.org). |
| Divisions: | Schools > School of Social Sciences |
| Record created by: | Melissa Cornwell |
| Date Added: | 29 May 2025 10:20 |
| Last Modified: | 29 May 2025 10:20 |
| URI: | https://irep.ntu.ac.uk/id/eprint/53663 |
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