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What evaluators do

Evaluators measure the quality of AI-generated educational content by assessing specific dimensions of text and identifying areas for improvement. Evaluators help edtech developers reliably assess their LLM outputs and build evidence-based tools that reinforce student learning and whole child development.
Explore Use cases for evaluators while testing, refining, or scaling AI-generated content.

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How to access evaluators

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Our approach

Learning Commons collaborates closely with pedagogical experts to define, test, and build our evaluators. We follow a research-informed process to develop evaluators that are firmly anchored in learning science:
Diagram showing the AIDT process for designing and validating evaluators
  • We build alongside experts in learning science and rubric development (e.g. Student Achievement Partners (SAP) ↗, CAST ↗, and Achievement Network (ANet) ↗)
  • We translate expert insight into ground-truth datasets that reflect real teaching and learning principles.
  • We develop, validate, and ship software that evaluates text the way an expert would.

Use cases

Explore product, model selection, runtime, and trust workflows for evaluators.

Core concepts

Learn how evaluators, dimensions, rubrics, and accuracy work together.

Quickstart

Run an evaluator in the Playground, SDK, or a Python notebook.