PeerReli-AI: AI-Enhanced Guidance for Boosting Peer Feedback Reliability

Abstract

Advances in artificial intelligence (AI) offer new opportunities to deliver scalable and personalized formative feedback in higher education, yet robust empirical evidence of their effectiveness remains limited. Although peer feedback can promote active learning, reflective thinking, and evaluative judgment, inconsistencies and inaccuracies in student assessments often undermine its impact.

This study proposes PeerReli-AI, an AI-supported peer feedback framework that integrates automated quiz administration, peer evaluation, and AI-generated formative guidance. A messaging-bot infrastructure was used to deliver quizzes and manage anonymous peer feedback, while the Gemini large language model generated context-aware feedback on students’ peer evaluations. The framework was implemented in an undergraduate Data Structures and Algorithms course across multiple iterative assessment cycles.

Results from Hotelling’s T² tests and linear mixed-effects models demonstrate a significant reduction in discrepancies between peer and instructor evaluations over successive iterations when AI guidance was provided. Survey findings further indicate that students perceived the AI-generated feedback as engaging, useful, and supportive of their learning.

Overall, the findings suggest that PeerReli-AI enhances the reliability and quality of peer feedback and offers a scalable approach for improving formative assessment practices in large higher education classrooms.