Research Article
AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education
Synthesis: AICoFE (AI-based Collaborative Feedback) is a multi-Large Language Models (LLMs) feedback generation system for higher education that combines independently fine-tuned language models with teacher-in-the-loop mediation, producing diverse feedback perspectives while preserving pedagogical authority through Learning Analytics dashboards.
Key Findings
- Multi-LLM diversity improves feedback quality. AICoFE uses three independently fine-tuned LLMs (GPT-4.1-mini, Gemini 2.5 Flash, Llama 3.1) to generate diverse feedback perspectives from the same input data — quantitative rubric scores, validated qualitative observations, rubric level descriptions, and instructional materials. Each model produces independent drafts that the teacher can inspect and curate.
- Teacher mediation is central, not an afterthought. Rather than automating feedback end-to-end, the system positions educators as active curators. The teacher dashboard enables instructors to compose final feedback by selecting individual sentences or paragraphs from AI outputs, with a visual legend indicating the proportion of content contributed by each LLM. This preserves pedagogical judgment while reducing feedback workload.
- Dual-purpose transparency supports both practice and research. The system tracks the extent of teacher modification and curation, serving simultaneously as (a) a reflection tool for individual instructors examining their curation patterns, and (b) a dataset for large-scale analysis of the teacher mediation role in AI-assisted feedback.
- Role-specific dashboards close the feedback loop. The student dashboard provides access to video recordings of presentations, self-evaluation rubrics, visual comparisons of self vs. external evaluations, teacher-curated AI feedback, and the ability to rate perceived agreement and usefulness — completing a full assessment-for-learning cycle.
System Architecture
| Module | Purpose |
|---|---|
| Visualization | Role-specific dashboards (student, teacher, evaluator) |
| Management | Hybrid SQL + MongoDB data infrastructure (traceability + semi-structured feedback versions) |
| Feedback Generation | Multi-LLM pipeline synthesizing rubric scores + qualitative comments |
| Recording | Video/audio capture of student presentations (opt-in, GDPR-compliant) |
The system treats AI as a draft generator, not a final deliverer — educators curate and compose the final feedback before it reaches students, so AI augments rather than replaces peer and teacher judgment.
What this means for practice
- Students. Rate the AI-assisted feedback you receive for agreement and usefulness: those responses are the loop that tells instructors whether multi-model drafts add anything.
- Students. Compare your self-evaluation against the external evaluation in the dashboard and use your presentation recording as evidence for that reflection.
- Students. Treat the AI text you receive as teacher-composed feedback rather than raw model output, since sentences and paragraphs are selected and edited before delivery.
- Students. Check the legend showing each model's contribution when a comment seems off-target, and say so in the embedded questionnaire.
Limitations
- The reported deployment involves approximately 80 students and 5 teachers across one undergraduate and one master's course at Universidad Autónoma de Madrid.
- Evaluation evidence is student-perceived coherence and usefulness of the feedback plus system usability scores; effects on feedback quality, reflective learning, or presentation performance are stated as future analyses.
- Comparison between AI-mediated and traditional manual feedback is also deferred, so the paper cannot yet show that AICoFe improves on existing practice.
- Presentation recording is opt-in and GDPR-compliant, so only consenting participants' data enter the pipeline.
Citation
Becerra, Á., Palma, A., & Cobos, R. (2026). AICoFe: AI-Based Collaborative Feedback System for Higher Education.