Problem
Research questions and friction points this paper is trying to address.
Peer Feedback
Feedback Quality
Higher Education
Large Language Models
AICoFe Demo: AI-based Collaborative Feedback System
🤖 AI Summary
This study addresses the challenge of low-quality peer feedback by developing a human-AI collaborative feedback system. The proposed system integrates large language models, learning analytics dashboards, and video recording technologies, employing a mechanism that combines quantitative scoring with qualitative observation to support multidimensional assessment while enhancing feedback depth under human supervision. Empirical validation involving 65 students demonstrates that the feedback generated by the system exhibits high coherence and practical utility, alongside excellent usability. By effectively balancing efficiency and reliability, this research offers an innovative, AI-empowered solution for educational assessment.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
Peer Feedback
Learning Analytics
Rubric-based Evaluation
Collaborative Learning
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