Llm judge
A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work.
LLM judge. Short answer: you test whether the AI actually helps students learn, not whether the model just sounds right. I bring education-domain judgment first, then use runnable AI-quality checks to make that judgment measurable. This page walks through the method and links to one you can run against your own system.
This is the method behind Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. You can read it and run it yourself.
1. Make the failure observable.
Pick the exact way this can go wrong and build the smallest input that triggers it. If you can’t make it fail on purpose, you can’t prove it works. Example: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
2. Measure against a baseline, not a feeling.
Compare to a neutral control so the number means something. A score with nothing to compare it to is theater. Example: Same answer, different grade: measuring when an AI grader can’t hold a verdict.
3. Check it a second, independent way.
Re-run with a different model family or a held-out set. Agreement across independent checks is the only verdict worth trusting. Example: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
The point is the overlap. I bring deep classroom, curriculum, and assessment judgment, and I make it measurable with AI and eval tooling. Every claim here maps to a public, reproducible check, not a slide. If you want this run against your own system, the method transfers directly.