Trial reportBMC medical education2025
AI-ding peer feedback: a randomized study of self-generated vs. ai-assisted peer feedback.
Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- AI-Based Assessment Tools in Pharmacy Education.Research square · 2026Article
- Exploring a Metacognitive Scaffolding-Based GenAI-Assisted Peer Feedback Provision Approach to Enhance Feedback Engagement Among Nursing Students.Nursing reports (Pavia, Italy) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeProviding effective peer feedback plays an important role in collaborative learning and is an essential professional skill for students to develop. This study investigates whether generative AI can improve the quality of peer feedback, and whether it influences students’ perceptions of the feedback received.
methodsAn 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 SG group provided feedback independently (i.e., the feedback was crafted on their own, without the use of an AI prompt) using the task, gap, action (TGAP) framework. The AI group utilized generative AI to create feedback based on a given prompt that aligned with the TGAP framework. The prompt was partially completed, requiring students to add behaviors, professional skills (e.g., communication, interpersonal skills), or knowledge related to each student they were evaluating. Feedback was coded and analyzed, and students’ perceptions were measured using the Feedback Perceptions Questionnaire.
resultsA total of 353 peer feedback comments were analyzed (162 self-generated, 191 AI-assisted). The AI-assisted group achieved significantly higher median scores across all TGAP criteria (Task: H(1) = 27.32, p < 0.001; Gap: H(1) = 89.32, p < 0.001; Action: H(1) = 86.86, p < 0.001). Specifically, 37.0% of students in the SG group compared to 61.3% of students in the AI group provided specific feedback on what their peers did well. For areas of improvement, 13.6% of students in the SG group provided specific areas to improve upon compared to 55.0% of students in the AI group. Only 22.8% of students in the SG provided feedback on how their peers could improve moving forward, compared to 72.8% in AI group. Student perceptions of feedback were positive, with only 3% of the AI group reporting negative feelings about the feedback they received compared to 12.7% in the SG group; this difference was not statistically significant (χ²(1) = 2.97, p = 0.085).
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.