Learning Commons Platform • Docs
We’re building AI infrastructure tools to help edtech tools align more closely with teaching and learning best practices. Our tools focus on improving and evaluating LLM output to ensure AI-powered products are pedagogically sound, standards-aligned, and grounded in research.
Knowledge Graph: Improve the accuracy of AI outputs through a structured network of datasets across curricula, state academic standards, and learning science research. Developers can leverage the knowledge graph to improve accuracy, comprehensiveness, and instructional coherence.
Evaluators: Assess the instructional quality of educational AI outputs through research-backed educational rubrics and expert-designed scoring systems. With standardized evaluation frameworks and metrics, these tools help assess whether AI-generated content meets high standards of accuracy, pedagogical soundness, and effectiveness.
Learning Commons Platform: Get API keys for Knowledge Graph's API and MCP server, see how datasets connect in the Knowledge Graph Explorer, and run your own text through the Evaluators Playground.
Learn more about our developer tools.
Check out our technical docs.
Whether it's a prototype, a product feature, or an experiment, your use case gives us valuable insight into real-world applications — and helps shape what we build next. We’re excited to learn from what you’re building.
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The resources provided in this repository are made available "as-is", without warranties or guarantees of any kind. They may contain inaccuracies, limitations, or other constraints depending on the context of use. Use of these resources is subject to our Terms of Use.
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- You are responsible for evaluating their suitability for your specific use case.
- Learning Commons makes no representations about the accuracy, completeness, or fitness of these resources for any particular purpose.
- Any use of the materials is at your own risk, and Learning Commons is not liable for any direct or indirect consequences that may result.
Please refer to each resource’s README, license, and associated docs for any additional limitations, attribution requirements, or guidance specific to that resource.