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

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 perspec tives were recorded through numerical, best-adjective, and open-ended surveys. The findings revealed pervasively positive views across the AI feedback. Numerical survey results showed that the feedback was generally deemed relevant, clear, actionable, useful, and comprehensive. The best-adjective survey further specified the nature of these views. Openended responses supported both findings, suggesting that GenAI feedback aligned well with course rubrics and provided actionable suggestions. Nevertheless, some limitations were identified, such as redundancy and how lengthy suggestions could overwhelm students. The study offers suggestions for the improvement of fine-tuned GenAI feedback to improve its effectiveness and enhance higher education students’ learning experiences, especially in online settings.