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

Engaging students in peer feedback offers significant learning benefits by promoting collab
oration, 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