Exploring Instructors' Views on Fine-Tuned Generative AI Feedback in Higher Education
Anastasia Olga Tzirides, Gabriela Cecilia Zapata, Patrick Bolger, Bill Cope, Mary Kalantzis, Duane Searsmith · International journal on e-learning · 2024
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.