EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration

Innovation Summary

EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration: We present EduPanel, a rubric-grounded, learner-conditioned LLM judge that decomposes evaluation across specialized agents to produce interpretable assessments for different aspects of teaching quality.

Executive Summary

EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration: We present EduPanel, a rubric-grounded, learner-conditioned LLM judge that decomposes evaluation across specialized agents to produce interpretable assessments for different aspects of teaching quality. Why it matters: Overall signal 90/100 driven by novelty 95 and practical impact 100. It maps to cross-cutting AI systems work even without explicit category metadata. Community signal includes 3 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity. Implementation angle: Implementation potential scores 87/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows. No linked repository is present, so expect more translation work before the ideas are production-ready. Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work. Caveat: No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Why It Matters

• Overall signal 90/100 driven by novelty 95 and practical impact 100.
• It maps to cross-cutting AI systems work even without explicit category metadata.
• Community signal includes 3 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity.

Implementation Angle

• Implementation potential scores 87/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows.
• No linked repository is present, so expect more translation work before the ideas are production-ready.
• Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work.

Caveat

No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Estimated Reading Priority

High - 90/100 signal; read before acting on adjacent agent, evaluation, inference, or ML systems work.

Score Breakdown

Novelty

95

Practical Impact

100

Technical Depth

100

Implementation

87

Relevance

100

Community

38

Confidence

85