AI is reshaping how education and workforce systems operate. However, most of this ecosystem is not prepared to use AI safely or consistently.
Education runs on data—but interoperability standards and implementation guides are fragmented across organizations, sectors, and technical stacks. Institutions that need to exchange learner, program, credential, and outcomes data must navigate multiple specifications that do not map cleanly to each other.
The result:
- Duplicated integration work
- Inconsistent meaning
- Limited reuse
All of which create barriers to innovation, implementation, and scalability.
EDUcore’s mission is to establish an AI-ready “source of truth” for interoperability—built by aligning existing standards rather than replacing them.
Through:
- Active participation from standards bodies
- Support from organizations like the Gates Foundation, Strada, and the U.S. Chamber Foundation
- Collaboration across the Data Standards United initiative
EDUcore will provide:
- A shared semantic backbone
- A practical mapping layer
This enables organizations to:
- Continue using existing systems
- Achieve consistent, governed interoperability
- Scale innovation more effectively
We are not creating “one new standard to rule them all.”
We are building shared infrastructure that makes existing standards easier to understand, implement, and connect across the PK20W+ ecosystem (early childhood through workforce).
Establish a baseline linked-data model (RDF / JSON-LD) grounded in:
- CEDS (Common Education Data Standards)
- Credential transparency frameworks (e.g., CTDL)
Includes core entities such as:
- Person
- Organization
- Credential
- Learning Event
- Assessment
- Employment Event
Where gaps exist, we will propose governed extensions through relevant communities.
Define and prioritize cross-sector use cases, then map:
- Standards
- Profiles
- Data elements
to the baseline model.
Outcome:
- Explicit, versioned, and auditable crosswalks
- AI-assisted implementation without inventing meaning
Publish a working reference library that is:
- Searchable
- Structured for implementers
- AI-consumable
Includes:
- Canonical definitions
- Mappings
- Implementation notes
- Openness / availability status
- Governance metadata
This lowers the cost of adoption for:
- School districts
- Colleges
- Workforce boards
- Employers
Engage technical leaders, including major platform providers, to ensure:
- AI-ready architecture
- Privacy protections
- Provenance tracking
- Auditability
- Human oversight in high-stakes contexts
EDUcore follows a targeted universalism approach:
Universal interoperability outcomes with targeted adoption pathways for organizations with different constraints.
This work establishes the planning and design foundation for subsequent build phases.
With support from forward-looking partners like the Gates Foundation, EDUcore will:
- Lower the cost of interoperability
- Improve safety and consistency in AI use
- Accelerate the development of tools that support learners and workers
We’re building this in the open—across standards bodies, institutions, and implementers.
If you're working on interoperability, AI, or education/workforce data systems, we want to collaborate.