Knowledge Graph is a structured collection of datasets that connects academic standards, curricula, and learning science data. By bringing quality datasets together, enriching them, and unifying the schema, Knowledge Graph enables edtech developers to focus on building instructional tools and AI-powered experiences that can improve teaching and learning. Knowledge Graph comes as CSV and JSON exports, so it can be used in graph or relational databases, in-memory tools, or AI pipelines without special infrastructure. These files are structured into four core datasets that serve as the foundation for standards alignment, instructional planning, and cross-state comparison. At this time we do not provide an API, live graph database, or MCP server access. We are exploring those options with private beta customers and welcome feedback on access patterns that would be most useful for your workflows.

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What you can do with this data

  • Standards alignment: Identify how your content supports specific academic standards and create content rooted in learner competencies across all key subjects.
  • Instructional planning: Create dependencies, learning progressions, and content coverage, starting with math in the Common Core State Standards.
  • Compare state standards: Adapt content aligned to one state standard to other states, initially in math across Common Core State Standards and 15+ additional states.
  • Curriculum alignment: Align your content or create additional materials aligned to curriculum (private-beta access only - reach out to us to join). To support these use cases, Knowledge Graph organizes educational information into a small set of structured datasets.

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Datasets in Knowledge Graph

Knowledge Graph organizes educational information into structured datasets. These fall into four main groups:
Dataset TypeWhat It RepresentsExamples
CurriculumLessons, activities, materials, and assessments from publishers.Illustrative Mathematics’ IM360
Academic standardsHierarchical structures of state or national learning goals.State standards from CASE Network 2
Learning progressionsLogical and usually sequential ordering of learning targets.Student Achievement Partners’ Coherence Map
Learning componentsAcademic standards broken down into concrete skills or concepts.ANet’s learning component for K12 Math

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How it works

Datasets are delivered as an entity (node) file (for example, StandardsFramework.csv) plus a relationships (edge) file that connects all the different entities together.
  • Entity (node) files: Each row represents a single node in the graph, such as a standard or learning component.
  • Relationship (edge) files: Each row represents a directed connection between two nodes, expressed as a triple (source UUID (universally unique identified)→ relationship type → target UUID). This is similar to a join table in a relational database, but instead of foreign keys, UUIDs are used to connect nodes.
Every entity and relationship carries a UUID. Some UUIDs come from external sources such as the CASE Network, which helps ensure interoperability. UUIDs make it easy to join files, reference entities, and preserve links across datasets. The consistent use of UUIDs across files allows deterministic joins and avoids tight coupling to any one database system. You can think of this as “graph-friendly join keys” that make connections explicit without relying on schema-level constraints. This release is designed to be database-neutral, lightweight, and interoperable. The files mirror the structure of a graph model, with entities as nodes and relationships as edges, while remaining easy to ingest in common environments. This design supports both deterministic joins and AI workflows such as embeddings or RAG. We have provided a list of example queries to help you get started, as well as tutorials that walk through typical use cases and how to slice data to meet your needs.