Assessing AI-generated feedback quality: Instructor perspectives on fine-tuned GenAI for higher education student writing
Year: 2025
Type: book-chapter
Abstract: This convergent mixed-methods study investigates the integration of Generative Artificial Intelligence (GenAI) for providing formative feedback in higher education. The study explores the perspectives of eleven experienced instructors regarding GenAI-generatedfeedback in an online graduate program. Participants reviewed sample feedback and shared their evaluations via a numerical questionnaire, an open-ended survey, and a focus group discussion.Results indicated that instructors generally viewed GenAI feedback positively, noting its relevance, clarity, actionability, usefulness, and comprehensiveness, highlighting its effective alignment with course rubrics and comprehensive guidance. However, challenges were alsonoted, including redundancy and overly lengthy suggestions that could overwhelm students. The study concludes with recommendations for refining GenAI to enhance its feedbackeffectiveness and improve the learning experience of higher education students
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7222384228