Evaluate AI outputs using trusted educational rubrics
Measure against research-backed rubrics to improve AI outputs.
Track and iteratively refine AI performance to ensure it’s aligned with classroom needs.
Measure what matters
Grounded in learning science to measure AI outputs
Quality is a non-negotiable in education. Developers can use AI to work faster, but have to maintain high educational standards. Learning Commons’ automatic evaluators are grounded in learning science to measure AI outputs. Developers can build with rigor, so that teachers and districts get tools they can trust.
AI evaluators designed for education
Every AI product needs evaluation. While most teams focus on general-purpose checks like hallucinations and baseline guardrails, Learning Commons provides education-specific evaluators, grounded in learning science.
Our evaluators are expert-built and measure pedagogical quality, so developers can benchmark AI outputs against standards that matter for learning.
Expert-Built Evaluators
Rigor by Design
Evaluators aren’t just built and shipped. They’re designed, tested, refined, and tested again. Subject matter experts inform rubrics, annotations, and datasets so developers can build with confidence.
Investing in Quality Data
We fund and support the researchers and organizations creating high-quality datasets that underpin the evaluators we build for developers working to elevate AI with learning science.
Scaling expert knowledge
Domain knowledge should be shared, not siloed. We work with expert organizations to build evaluators end-to-end, tapping into their domain knowledge to cover pedagogical dimensions like grade level accuracy, assessment quality, and more.
Learning Never Stops
Research and data constantly evolve. As learning science advances and experts at the forefront of educational science produce new validated datasets, Learning Commons identifies those resources and converts them into production-ready evaluators.
