Rethinking Feedback: Student Perceptions of Instructor-Created and AI-Generated Instant Feedback in Higher Education Courses

• In Event: AERA Roundtable Session Saturday 1:45 pm - Gold 4
  In Roundtable Session: Feedback and Assessment in Digital Learning Environments

Abstract

AI technologies are rapidly advancing and increasingly being integrated into educational tools, particularly classroom response systems, to enhance both quantity and quality of feedback. This study explores student perceptions of two implementations of the classroom response system (CRS), ExplainIt, in an undergraduate computer science course. The first implementation used static feedback based on instructor-created sample responses on student self-explanations, while the second incorporated AI-generated adaptive feedback. Survey data from two semesters of same course with different student groups revealed that both valued real-time feedback and found the feedback useful. Students experiencing AI-generated feedback reported greater clarity and learning support. The findings show instructional potential of AI-supported intelligent feedback systems and suggest improvements for enhancing student trust and sustained engagement.

Authors

• Xiaoying Zheng, Indiana University

• Gamze Ozogul, Indiana University - Bloomington

• Seung Lee, North Carolina State University

• Wookhee Min, North Carolina State University

• Dan Carpenter, North Carolina State University

• Jordan Esiason, North Carolina State University

• James C. Lester, North Carolina State University