Connecting learners and workers to credentials and careers depends on trusted, transparent data, yet much of the information needed to support those decisions remains difficult to find, compare, and use as open data. Credential Engine is working to change that. As part of a Google.org-supported initiative to expand credential transparency through the AI-enabled tool CTDL xTRA and open data standards, we are announcing four state partners to use this tool.
Arkansas, California, Georgia, and New Jersey, each a long-term partner with Credential Engine, will demonstrate the value of CTDL xTRA to expand their credential registries to be both comprehensive and of greater depth. The work will use CTDL xTRA to find, extract, and transform data into the Credential Transparency Description Language (CTDL) and prepare it for human review and approval before being published. These select states will help ensure the tool effectively meets state priorities through high-volume extraction and publishing, before scaling toward broader national and international adoption.
Built on the CTDL, this tool is designed to support, not replace, human expertise by reducing the time spent on repetitive data collection while preserving the accuracy, context, and trust needed for credential and skills data. Through CTDL xTRA, Credential Engine will significantly expand its ability to efficiently publish high-quality CTDL-structured data across classes of information critical to learning-to-work pathways: credentials, programs, courses, skills, support services, organizations, jobs, and connections among them. The work will demonstrate not only how AI can help scale CTDL publishing, but what richer, more comprehensive CTDL data makes possible: better insights into education and workforce ecosystems, stronger connections across systems and pathways, more useful tools and services for learners and workers, and a trusted data foundation for AI-powered applications.
This phase of the work is made possible through funding from Google.org and additional support from Anthropic via Claude credits. The initiative strengthens the shared foundation for how credential and skills information is published and used, supporting learners, workers, employers, educators, and policymakers with more connected, useful, and trustworthy data.
CTDL xTRA supports the extraction and transformation of public learn and work data into CTDL data. If you’re interested in CTDL xTRA, please fill out this interest form or reach out to info@credentialengine.org.