AICoFe Demo: AI-based Collaborative Feedback System

Abstract

Peer feedback promotes active learning, critical reflection, and skill development, but its effectiveness is often limited by the quality of feedback students provide. Recent advances in LLMs offer new opportunities to support peer feedback by generating more coherent and actionable feedback while preserving human oversight. This paper presents AICoFe, an AI-based collaborative feedback system designed to support teacher, peer, and self-assessment in higher education. AICoFe integrates rubric-based evaluations, GenAI-supported feedback, Learning Analytics dashboards, and video recordings to foster reflective learning. The system combines quantitative scores and qualitative observations to generate structured feedback focused on strengths, areas for improvement, and actionable recommendations, which teachers can review and curate. An evaluation with 65 undergraduate and master's students shows high satisfaction with the coherence and usefulness of the feedback, as well as the excellent usability, indicating that AICoFe effectively supports peer feedback in authentic educational settings.

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