AI-ding Peer Feedback: A Randomized Study of Self-Generated vs AI-Assisted Peer Feedback

Purpose Providing and receiving peer feedback is an essential professional skill and plays an important role in collaborative learning. However, providing effective feedback is challenging and time-consuming. This study investigates whether generative AI can improve the quality of peer feedback and influence students' perceptions of the feedback received. Methods An experimental design was employed involving 129 third-year Doctor of Pharmacy (PharmD) students. Participants were randomized into two groups: self-generated (SG) feedback and AI-assisted (AI) feedback. The AI group utilized generative AI to create feedback based on a given prompt. The prompt was partially completed, requiring students to add particular behaviors or professional skills related to each student they were evaluating. The SG group provided feedback independently (i.e., the feedback was crafted on their own, without the use of an AI prompt). Feedback was coded and analyzed using the task, gap, action (TGAP) framework, and students' perceptions were measured using the Feedback Perceptions Questionnaire (FPQ). Results The AI-assisted feedback group produced significantly higher quality comments across each of the three feedback criteria. Specifically, the AI group provided more detailed and specific feedback on peers' strengths, areas for improvement, and actionable suggestions for future performance. Students in the AI group received the feedback positively. Conclusions The study demonstrates how the strategic use of AI can be used to improve the quality of peer feedback. Future research should explore the long-term effects of AI-assisted feedback on students' independent feedback skills and investigate behavioral changes resulting from improved feedback quality.