AICoFE (AI-based Collaborative Feedback) is a multi-LLM 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.
Authors: Alvaro Becerra, Alejandra Palma, Ruth Cobos (GHIA Group, Universidad Autónoma de Madrid) · arXiv:2605.04740 · Accepted at LASI Spain 2026
Key Findings
1. 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.
2. 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.
3. 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.
4. 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.
Implications
AICoFE addresses a critical tension in automated-feedback: the trade-off between efficiency and pedagogical authority. By adopting a human-in-the-loop-ai architecture rather than full automation, it demonstrates that AI can accelerate feedback workflows without displacing the teacher's interpretive role. This aligns with emerging best practices in ai-feedback-quality research, which increasingly emphasizes teacher agency over raw automation.
The multi-LLM pipeline is a practical response to the observation that different models excel at different dimensions of feedback — tone, specificity, actionability. Rather than selecting a single "best" LLM, AICoFE treats model diversity as a feature, surfacing complementary perspectives that the teacher can synthesize. This approach parallels work in ai-peer-feedback-systems that leverages multiple AI-generated perspectives for richer formative assessment.
The learning-analytics-dashboards component of AICoFE is notable for its role-specific design: teacher and student dashboards serve fundamentally different functions within the same platform, and the system's transparency features (source attribution, curation tracking) support both reflective practice and learning-analytics at scale.
For higher-ed institutions facing growing assessment loads, AICoFE models a path where AI-assisted feedback scales without sacrificing the relational and interpretive dimensions of effective formative-assessment.
Related Pages
- automated-feedback — Broader landscape of AI-generated feedback in education
- human-in-the-loop-ai — Architectures that keep human judgment central to AI workflows
- ai-feedback-quality — Research on what makes AI-generated feedback effective
- ai-peer-feedback-systems — AI systems that augment peer feedback processes
- learning-analytics-dashboards — Visual interfaces for educational data and feedback
- higher-ed — Institutional context for large-scale feedback challenges
- formative-assessment — Assessment paradigms emphasizing learning over grading
- ai-generated-feedback-higher-ed — AI feedback specifically in university contexts