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.
@article{arxiv.2605.04740,
title = {AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education},
author = {Alvaro Becerra and Alejandra Palma and Ruth Cobos},
journal= {arXiv preprint arXiv:2605.04740},
year = {2026}
}
Comments
Accepted in LASI Spain 26: Learning Analytics Summer Institute Spain 2026