AI Peer Feedback Systems

Peer feedback develops critical reflection and evaluative judgment, yet:

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