AI-Facilitated Feedback
Designing and evaluating AI-facilitated feedback systems using Feedback Triangle Theory to enhance teacher implementation and student learning outcomes in higher education settings.
This project centers on the design, implementation, and evaluation of AI-facilitated feedback in higher education settings. Grounded in Feedback Triangle Theory, the research explores how instructors can leverage generative AI to deliver individualized, responsive feedback that addresses both cognitive and social-affective dimensions of student learning. A key focus is understanding how students perceive and respond to AI-generated versus human-written feedback, and whether AI-facilitated feedback can match or complement the motivational qualities of instructor feedback.
The project also examines practical implementation challenges, including how instructors integrate AI feedback tools into their teaching practice and how linguistic cues in AI-generated feedback relate to student motivation and engagement outcomes.
Related Publications:
- He, J., & Xie, K. (2026). Who wrote this? College students' perceptions and evaluations of human and AI-facilitated feedback. Educational Psychology.
- He, J., Li, T., Xu, Z., & Xie, K. (2025). Leveraging Generative AI in Designing and Delivering Individualized Responsive Feedback for Pre-service Teachers in Higher Education. Book Chapter.
- He, J., Jiang, Z., & Sun, Z. (2026). Linguistic Cues in Instructor and AI Feedback: Linking Language to Student Motivation. ISLS 2026 (Short Paper).