Skills Validation Solutions: Observations, Lessons Learned, and Recommendations for Action

These reflections were prepared by Emily Scheines (C-BEN), Tara Laughlin (Education Design Lab), and Kathryn Green (Unicon).

Over the past several months, C-BEN’s Partnership for Skills Validation sought  to understand what quality looks like in skills validation, and how we might enable it at scale.

Skills Validation  The process of proving that someone can use a skill they claim to have, at a clearly defined level. This process shows not only that the person has the skill, but also how well they can use it and in what context  Validation occurs when real evidence of an individual’s knowledge, skills and behaviors is produced, that evidence is assessed against recognized criteria, and the performance-based outcomes are met and documented  Quality Skills Validation is NOT:  Skills Inference – Making assumptions or educated guesses about an individual’s skills based on work experience and education Self-Assertion – An individual’s statements, often through a resume or self-evaluation, about the skills they believe they have

Within this broader effort was a collaboration to develop a set of Principles for Skills Validation Quality at Scale. These principles articulate a set of best practices around transparency, inclusivity, relevance, and scalability in skills validation, informed by a diverse group of practitioners and stakeholders. To pressure-test these principles, we launched a Request for Information (RFI), inviting real-world examples from across the field. Over two dozen solution providers submitted responses, and eight were selected to participate in a public showcase event. Take the time to watch these solution providers embody the quality principles!

But this effort wasn’t just about featuring solutions. It also provided an opportunity for reflection, to see where the field is gaining traction, where further opportunities still exist, and what that means for the future of skills validation.

Now, we’re sharing what we’ve observed through this process – about the current state of the field, where we see exciting innovation, and what we believe is needed next.

What We Observed

Where the Field Is Gaining Traction

Across the full set of solutions we reviewed, we were encouraged to see broad strengths in several areas.

• Data, Security & Interoperability
  This was the most consistently well-supported principle across solutions. Providers shared compliance documentation (e.g., FERPA, GDPR) and evidence of interoperability with learning and employment systems. Technical infrastructure and trust mechanisms are clearly maturing.
• Performance-Based Evidence
  A number of solutions stood out for how they enabled learners to demonstrate skills in context, using simulations, workplace-based tasks, or real-time feedback loops to validate what learners can do, not just what they know.
• Scalability & Adaptability
  Many solutions were intentionally designed with scale in mind, offering modular systems, multilingual interfaces, or AI-powered efficiencies. While not all are fully scaled yet, the ambition and infrastructure for change-making scale were often present.

 

Opportunities for Further Innovation

While the solutions we reviewed revealed exciting signs of progress, they also surfaced several important opportunities: areas where further innovation and investment could help bring the principles of quality skills validation more fully to life. 

• Bias Mitigation
  This remains one of the most important frontiers for innovation. While many providers expressed sincere commitments to fairness and equity, we recognize the continued need for detailed strategies to proactively identify and mitigate bias within validation processes. This presents a clear opportunity for solutions to embed bias audits, inclusive design practices, and human-in-the-loop safeguards in AI-assisted approaches to validation.
• Transparency & Trust
  As validation methods grow in complexity, the need for clear, explainable evaluation models becomes even more critical. Opportunities remain for tools to more explicitly surface scoring criteria and logic, feedback mechanisms, resubmission approaches, and validation thresholds in ways that empower l/earners, educators, and employers to act on the results with confidence.
• Accessibility, Fairness & Inclusivity
  Future-forward design will require consistent integration of multi-lingual access, assistive technology, variety in demonstration modalities, and mobile-first user experiences. This will be a valuable space for solutions that can turn intent for inclusivity into operational reality. 
• Recognition of Prior Learning (RPL)
  Perhaps the most under-explored area was the recognition of informal, self-directed, or previously unrecognized  learning. RPL plays an essential role in supporting career mobility, especially for historically underserved learners, and needs to be addressed in structured and actionable ways. This represents a high-impact opportunity for innovators to create pathways that honor and validate learning wherever it happens.

What This Tells Us About the State of the Field

These findings suggest that skills validation is an emerging, rapidly evolving domain, one which is gaining traction while continuing to take shape. 

We’re seeing a few key dynamics:

• The field is on the cusp of clarity, but still lacks a shared mental model for what skills validation actually entails and how it’s done well. We still see skills validation conflated with related activities like credentialing, technical verification, and skill inference, revealing a strong need to clarify distinctions. Without a shared language, it’s hard to build a shared foundation.
• Innovation is outpacing trust. While AI, simulations, and analytics are being used in increasingly sophisticated ways, many solutions have yet to fully operationalize the transparency, bias safeguards, and human judgment needed to fully earn the confidence of employers and learners.
• In skills validation, the goal may not be absolute certainty but increased stakeholder confidence. Achieving this will likely require blended models that layer self-assertion, inference, and various forms of performance-based evidence to provide a fuller, more trustworthy picture of learner capabilities.
• The field is beginning to integrate AI in meaningful ways, particularly to automate evidence analysis and deliver real-time feedback. While some use cases appear more mature and trustworthy, others underscore the need for greater transparency, clearer validation logic, and appropriate human oversight.

It’s worth noting that our principles were written with certain assumptions: digital-first delivery, multilingual access, equity-forward design. In practice, we encountered analog solutions, U.S. government constraints on language (English is required in some contexts), and inconsistent attention to bias mitigation. This forced us to confront an important tension: How do we balance learner-centered expectations with readiness, pushing the field forward without leaving promising innovations behind?

 

Recommendations for Action

Based on these observations, we’ve identified a set of key priorities for the field:

1. Adopt Shared Language and Standards
   We must move forward with a common understanding of what skills validation is — and isn’t — along with consistent ways to describe methods and outcomes. Without these, even promising solutions struggle to communicate their value and role in the talent management lifecycle, and stakeholders can’t evaluate them reliably.
2. Support Ecosystem-Level Collaboration
   No single solution can do it all, and fortunately, many providers are already recognizing this. Some of the strongest solutions demonstrated active partnerships with employers, education providers, and other tech platforms, signaling that an ecosystem approach is emerging as a core strategy. To scale validation, we need systems that are built for connection, not ones that stand alone.
3. Build Equity into the Foundation
   As innovation continues to accelerate, there is a vital need to embed equity more deeply into the design of validation systems. Rather than treating equity as an add-on, solutions should embed bias mitigation strategies and inclusive design principles from the outset, ensuring that validation advances opportunity for all learners
4. Invest in Storytelling and Field Readiness
   Helping solution providers articulate their models clearly, including the types of evidence collected and how that evidence is evaluated, will unlock trust and accelerate adoption. Many providers already have promising methods in place; the next step is supporting them in translating those methods into clear, accessible narratives that resonate across stakeholders. 

This effort offered more than a snapshot; it served as a mirror and a signal. It showed us where the field is advancing, highlighted gaps that need to be closed, and revealed how we might guide the next phase of growth. We’re grateful to the stakeholders who co-developed the principles, the solution providers who shared their work, and the broader community working to make skills validation more effective, equitable, and scalable.

This work is far from done, but building the future we want to see everyday. 

Join the Partnership here and tell us what excites you most in this field 

We want to sincerely thank all the solutions providers who took the time to share their valuable contributions with us in the course of this RFI process. Take some time to learn more about them!