AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education
For educators and students in higher education, this system addresses inconsistent peer feedback quality, though the approach is incremental.
AICoFe deploys a multi-LLM pipeline with teacher-in-the-loop mediation to improve peer feedback quality in higher education, achieving consistent and actionable feedback through modular architecture and hybrid data infrastructure.
Effective peer feedback is essential for developing critical reflection in higher education, yet its impact is often limited by the inconsistent quality of student-generated comments. This paper presents the implementation and deployment of AICoFe (AI-based Collaborative Feedback), a system designed to bridge this gap through a human-centered AI approach. We describe a modular architecture that orchestrates a multi-LLM pipeline, utilizing GPT-4.1-mini, Gemini 2.5 Flash, and Llama 3.1, to synthesize quantitative rubric data and qualitative observations into coherent, actionable feedback. Key to the system is a "teacher-in-the-loop" mediation workflow, where educators use specialized Learning Analytics dashboards to curate and refine AI-generated drafts before delivery. Furthermore, we detail the underlying data infrastructure, which employs a hybrid SQL and MongoDB strategy to ensure traceability and manage semi-structured feedback versions.