AI assistance in peer feedback provision: Pedagogically sound, but minimally adopted

Engaging students in peer feedback offers significant learning benefits by promoting collaboration, critical thinking, and skill development. However, challenges persist because many students struggle to provide constructive and actionable feedback due to gaps in disciplinary knowledge and pedagogical skills. This study investigates whether Generative AI (GenAI) can help address these challenges by supporting students in delivering high-quality peer feedback. To examine this potential, we implemented human-led, GenAI-powered assistance for feedback provision in an educational platform using structured prompt instructions and a tailored user interface. We then examined the characteristics of this AI assistance (AI-A) and how students engaged with it during peer feedback. Our analysis draws on data from 433 students who engaged in 7670 instances of peer feedback supported by AI assistance as part of a large undergraduate semester-long course. Our results indicate that AI-A was typically positive, well structured, and focused on pedagogical strengths (79% of all instances). However, uptake remained relatively low, with just 9% (690) of AI assistance suggestions leading to revisions. AI-A that was less accurate, less complete in describing the original peer feedback, or less positively worded was more likely to prompt revision, with small-to-medium effect sizes. One possible explanation is that these instances drew students’ attention because they appeared to require correction. These findings offer valuable insights for designing AI-A in peer feedback platforms that promote learning, encourage reflection, and preserve student autonomy.