AI is reshaping education and workforce systems, but fragmented data standards hold the ecosystem back. EDUcore is building the shared semantic backbone that makes existing standards easier to understand, implement, and connect without replacing them.
Use Cases
Enable AI systems to work with education and workforce data using consistent, governed semantics, ensuring interoperable standards underpin AI-driven tools rather than proprietary data silos. A shared semantic backbone enables portable AI memory: learner context that travels with the individual across institutions, platforms, and life stages with full provenance and human oversight.
Help adult learners assemble a comprehensive Learning and Employment Record from fragmented sources, including community colleges, workforce programs, employer training, and professional certifications, into a single, portable, standards-based credential profile.
Support the secure exchange of learner records that include disability accommodation information, with fine-grained privacy controls ensuring sensitive data is only shared with authorized parties on a need-to-know basis.
Enable small and rural districts to meet state and federal reporting requirements using existing systems. EDUcore provides standards-aligned mappings so districts can share and report data without adopting new platforms or infrastructure.
Why EDUcore?
Institutions need to exchange learner, program, credential, and outcomes data across systems, but the specifications behind those exchanges do not always map cleanly. EDUcore aligns existing standards into a shared semantic backbone so organizations can reduce duplicated integration work, preserve consistent meaning, and support governed AI use without replacing the systems they already depend on.
Phase 1 Deliverables
Stakeholder letters, signatories, and engagement with standards bodies and technology partners.
Vertical-specific demos followed by business alignment sessions with stakeholder leadership.
User's guide, stakeholder registry, use case registry, resource catalog, and standards registry.
RDF foundation with mappings to all specifications, starting with CEDS and SIF, the Special Education Data Model (SEDM), and other high-impact areas.
Standards Alignment
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, combined resources, and a broad coalition of stakeholders, EDUcore provides a shared semantic backbone and a practical mapping layer so organizations can continue using current systems while achieving consistent, governed interoperability.
We are not creating one new standard to rule them all. We are building shared infrastructure that makes the standards already in use easier to understand, implement, and connect across the PK20W+ ecosystem: lifelong learning ranging from early childhood through workforce.
EDUcore AI Bakeoff
Community demonstrations showcasing AI-powered tools built on education data standards.
Demonstrates the EDUcore Reference Library with an AI-powered search engine grounded in a CEDS-based RDF/JSON-LD ontology. Users can query natural-language questions and receive standards-aligned implementation roadmaps. Also features a Needs Explorer for tag-based spec discovery without AI.
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AI-driven labor market intelligence tool covering 1,016 O*NET occupations. Provides three tailored views: job seekers get portfolio guidance and AI-disruption exposure; hiring managers get assessment rubrics and 30/60/90-day plans; education providers get curriculum alignment and capstone ideas.
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An MCP server that connects Claude directly to a CEDS database, letting AI autonomously discover schemas, identify populated tables, and write analytical SQL. Demonstrated on a 50,000-student synthetic dataset, the tool surfaces equity gaps in assessment proficiency across racial and income groups.
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Shows how EDUcore's semantic backbone transforms static transcripts into actionable, linked data. GoldCheck is a free AI-enabled tool that plugs into institutional web catalogs, revealing transfer credit pathways, remaining coursework, and competency mappings.
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A real implementation of EDUcore concepts showing how RDF endpoints and CEDS mapping turn static academic records into living competency profiles. Courses map to CIP codes, then to knowledge, skills, and abilities via SOC classifications, creating a continuous thread from learning to career.
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A visual presentation on Access 4 Learning's role in the EDUcore ecosystem, highlighting how A4L's data standards and community efforts connect with the broader initiative to improve education data interoperability.
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Live knowledge graph integrating nine education and workforce data standards at full field depth: CEDS, SIF, Ed-Fi, PESC, CTDL, SEDM, JEDx, EdMatrix, and CIP, with cross-standard mappings anchored to CEDS throughout. Features tree-based data model navigation, AI-powered cross-standard mapping, natural-language search, and visual graph diagrams. The implementation includes SEDM, linking IDEA special education data to the broader PK20W interoperability stack.
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A one-take ChatGPT session demonstrating how AI can read the CEDS assessment ontology web page, construct SPARQL queries, locate the official RDF file on GitHub, and execute queries against real data.
Watch on DriveReference Library
Browse use cases, standards references, implementation notes, and AI-assisted discovery tools from the EDUcore project.