Alvaro Becerra, Alejandra Palma, Ruth Cobos (2026) — LASI Spain 2026.
System Architecture
AICoFe orchestrates a multi-LLM pipeline using GPT-4.1-mini, Gemini 2.5 Flash, and Llama 3.1 to synthesize quantitative rubric data and qualitative observations into actionable feedback for higher education students. The key innovation is a teacher-in-the-loop mediation workflow: educators use specialized Learning Analytics dashboards to curate and refine AI-generated feedback drafts before delivery.
Technical Design
- Multi-LLM orchestration: Three different models contribute complementary perspectives
- Hybrid storage: SQL for traceability and structured metadata + MongoDB for semi-structured feedback versions
- Learning Analytics dashboards: Dedicated educator interfaces for feedback curation
Connection to Broader AIED
AICoFe extends prior work on ai-peer-feedback-systems from experimental prototypes to a deployed system with educator mediation. The multi-LLM approach addresses single-model bias concerns in automated-grading. It sits at the intersection of feedback-loop design and learning-analytics — keeping educators as active curators through human-in-the-loop-ai principles.
Related Pages
- ai-peer-feedback-systems — Predecessor systems and conceptual foundations
- feedback-loop — Closing the feedback quality-consistency gap
- learning-analytics — Educator dashboards and oversight
- higher-ed — University deployment context
- automated-grading — Multi-LLM approach to quality assessment
- human-in-the-loop-ai — Teacher-in-the-loop mediation paradigm
Citation
APA: Becerra, A., Palma, A., & Cobos, R. (2026). AICoFe: Implementation and deployment of an AI-based collaborative feedback system for higher education. Proceedings of the Learning Analytics Summer Institute Spain 2026 (LASI Spain 2026). arXiv:2605.04740.