The Achievement Wallet – Unlocking Opportunity Through Transparent Credential Data

In today’s complex learning and career landscape, learners often struggle to clearly communicate the full scope of their verified skills and credentials to employers and other institutions. Without a unified, trusted system for managing and sharing this information, opportunities can be missed and achievements overlooked. That’s where the Achievement Wallet comes in, a solution that empowers individuals to securely collect, manage, and share their learning records in a way that’s portable, meaningful, and recognized.

Developed by iQ4, the Achievement Wallet uses Credential Transparency Description Language (CTDL) data to help bridge the gap between education and employment. By using CTDL and the Credential Registry, Western Governors University (WGU) is ensuring its credential and skill data is structured, machine-readable, and linked to real-world job requirements, making it easier for learners to demonstrate their competencies to employers and systems that matter through the use of the WGU Achievement Wallet. 

The Achievement Wallet exemplifies the value of open, transparent data in building a more navigable, equitable credentialing ecosystem. It’s not just a tech solution, it’s a step toward a learner-centered future.

Read the WGU Achievement Wallet story here and watch our webinar on the Achievement Wallet here. 

Learn more about solutions that use CTDL to empower people.

Op-ed: Policymakers Must Lead the Charge to Fix America’s Credential Chaos: In a new op-ed for Work Shift, CEO Scott Cheney makes the case for why structured, open credential data must be a cornerstone of federal and state workforce strategy. He lays out four clear, actionable steps policymakers should take now to bring order to the credential chaos, and to ensure AI helps, not harms, learners and job seekers. Read the full op-ed

Expanding Access to Education Data: Join Credential Engine on April 30 at 2 PM ET for an insightful webinar on how AI-driven solutions are rapidly increasing the amount of data in CTDL. This session is about CTDL xTRA, an open-source, AI-powered tool developed by Credential Engine in collaboration with Learning Tapestry. CTDL xTRA leverages large language models to extract and transform public web content into structured, machine-readable data.

Speakers from Bristol Community College and Houston Community College will share how they are using CTDL xTRA to enhance transparency and better support learners. If you're interested in how AI is reshaping the education and workforce landscape, and the role CTDL xTRA can play, register today. Learn more about this opportunity here. 
 

Unlocking the Future of Credential Transparency: At Credential Engine, we're committed to making the credential and skill landscape clearer, more connected, and more useful for everyone, from learners and workers to educators and employers. Our latest three-part blog series illustrates the importance of creating a transparent, data-rich credential ecosystem that empowers decision-making and drives equity in education and employment. 

1. The Power of a Common Language
   Learn how establishing a shared language for credentials and skills lays the foundation for clarity and consistency in an otherwise complex landscape.
    

2. Building Solutions for Better Learning and Career Opportunities
   Explore the innovative tools and strategies we're enabling to help individuals navigate learning pathways and unlock new career options.
    

3. Create Lasting Change with Scalable Credential Transparency
   Discover how we’re working to ensure that credential and skill transparency is not just impactful, but sustainable and scalable for the long term.
    

Read the full series here and see how this work is helping shape the future of learning and work.

Advancing CTDL to Describe Global Credential Types: The CTDL team is currently working to expand how the CTDL describes credential and qualification types defined within national and regional qualification systems around the world. This effort builds directly on CTDL updates completed in early 2025 by the global Qualifications Frameworks as Data Task Group, which introduced new terms to support transparent, structured descriptions of qualifications frameworks and their components.

Now, a global focus group is taking the next step—exploring how CTDL can reflect distinctions between specific types of credentials and qualifications, particularly those formally defined by public authorities. While CTDL already supports many credential classes such as degrees, certificates, licenses, and micro-credentials, this proposal enhances its ability to describe credential types that are uniquely defined and embedded in different systems—such as the distinction between an Honours Degree and an Ordinary Degree in the UK, or the equivalencies between South African and UK Honours Degrees.

In most jurisdictions, credential types are defined within qualification systems and aligned to levels of progression frameworks. They are shaped by national structures and policies and can be offered and issued by a range of education, training, or assessment providers. Accurately describing these types helps ensure global transparency, comparability, and mobility for learners.

The result of this work will be a proposed expansion to CTDL. A public webinar will be held on May 6th at 12 PM ET to present the proposal and gather broader community feedback. Final CTDL updates will follow the feedback period.

U.S. Department of Labor O*NET Occupations and Industry Model Competency Frameworks in the CTDL:  Credential Engine published an updated blog highlighting expanded data included in the integration of O*NET Occupations and Industry Model Competency Frameworks in the Credential Registry, an update to the original 2020 post about O*NET data in the Registry. O*NET data links competencies to real-world occupations, enhancing the utility of the CTDL. These connections improve data interoperability and help people better understand career pathways and required skills. Read more about these important updates and their implications here.