📄 Full text: arXiv:2605.04740 · local
Student peer feedback is often superficial or inconsistent. AICoFe (AI-based Collaborative Feedback) uses a multi-LLM pipeline (GPT-4.1-mini, Gemini 2.5 Flash, Llama 3.1) to generate diverse perspectives on student presentations, but the critical design feature is teacher-in-the-loop mediation—educators curate and compose final feedback before delivery.^becerra-aicofe-feedback-2026
The Problem
Peer feedback develops critical reflection and evaluative judgment, yet:
- Students have limited feedback literacy
- Generated comments are often superficial or lack actionability
- Quality is inconsistent across evaluators
AI-generated feedback alone risks replacing shallow peer comments with shallow AI comments. AICoFe addresses this by treating AI as a draft generator, not a final deliverer.
System Architecture
Four components:^becerra-aicofe-feedback-2026
| Module | Purpose |
|---|---|
| Visualization | Role-specific dashboards (student, teacher, evaluator) |
| Management | Hybrid SQL + MongoDB data infrastructure |
| Feedback Generation | Multi-LLM pipeline synthesizing rubric scores + qualitative comments |
| Recording | Video/audio capture of student presentations (opt-in, GDPR-compliant) |
The Multi-LLM Pipeline
Three independently fine-tuned models receive:
- Quantitative rubric scores
- Validated qualitative observations from evaluators
- Rubric level descriptions
- Instructional materials for the assessed skill
Each model produces an independent draft. The diversity of models is intentional—GPT, Gemini, and Llama have different stylistic biases and blind spots; teacher curation selects the best fragments.^becerra-aicofe-feedback-2026
Teacher-in-the-Loop Mediation
The Teacher Dashboard is the central mediation interface:
- Inspect scores, comments, and videos from all evaluators
- Review drafts from all three LLMs
- Compose final feedback by selecting individual sentences or paragraphs from AI outputs
- Visual legend shows proportion of content contributed by each LLM
- Track extent of teacher modification/curation
- Audio review via text-to-speech for long comments
This preserves pedagogical authority while reducing teacher workload.^becerra-aicofe-feedback-2026
Transparency & Analytics
- Feedback history log: Every sent feedback entry displays LLM contribution proportions and teacher modification levels
- Dual purpose: Supports (1) teacher reflection on curation patterns and (2) large-scale analysis of how teacher mediation shapes feedback quality
Student Experience
- View video recordings of own presentations
- Complete self-evaluations using the same rubric
- See visual comparisons of self vs. external evaluations
- Receive teacher-curated (not raw AI) feedback
- Rate perceived agreement and usefulness^becerra-aicofe-feedback-2026
Relationship to Human-in-the-Loop AI
AICoFe represents a human-centered AI paradigm for education: AI augments rather than replaces human judgment. This contrasts with fully automated grading or feedback systems that remove the teacher from the loop. The key insight is that pedagogical authority resides with the teacher, and AI's role is to expand the range and depth of actionable comments they can craft.^becerra-aicofe-feedback-2026
Related Pages
- mindcopilot-llm-co-writing — Hierarchical acceptance rates offer template for evaluating collaborative feedback
- moodle-ai-tutoring-deep-learning — Shares strategy of embedding AI tools into existing educational workflows
- multimodal-ai-feedback-learning — Zhao et al.: extends feedback systems to multimodal delivery with equivalent learning outcomes
- sequenced-ai-feedback-learning — Cao et al. RCT: feedback sequencing matters — layered hints can induce gaming behavior
- psyscore-essay-scoring-zpd-feedback -- PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
- student-perception-ai-use-collaboration -- Misalignment in partners' beliefs about each other's AI use early in a project predicts lower final scores — especially for low-performing teams — and face-to-face sessions don't fix it.
- aicode-collaborative-feedback-system — Multi-LLM deployed system with teacher-in-the-loop- llm-reasoning-traces-metacognition — AI feedback traces may inflate confidence without improving performance
- humanlike-ai-collaborative-writing — Social dynamics of humanlike AI in writing feedback
- codify-socratic-programming-tutor — Codify complements feedback systems with Socratic discovery approach