Proof Point, Pressure Point: Why Signals Will Make or Break What Comes Next

Every learning innovation eventually runs into the same question: how do we show what learners truly know and can do?  Microcredentials, badges, digital wallets, and Learner Employment Records (LERs) all promise an answer, but the challenge runs deeper than design. From personalized pathways to AI-driven assessments, every innovation depends on whether learning is visible, valuable, and verifiable across classrooms, communities, and careers. Signals are the proof point of whether a new learner-centered ecosystem can actually work, and the pressure point where it may crack.

Proof Point: Making Learning Visible and Valuable 

Done well, new signals reduce real friction by improving alignment and translating what learners can know and do into evidence that education institutions and employers can confidently act on. They connect learners to opportunity not through degrees or seat time, but through demonstrated capabilities and authentic experiences.

As Dr. Isaac Agbeshie-Noye recently described, our challenge is not a talent shortage; it is a coordination problem. A functional signaling ecosystem would enable employers to see skills, not just job titles. Better signals in K-12 would make career-connected learning, community projects, and learner portrait frameworks more meaningful by giving students clear, portable ways to demonstrate readiness and contribution. In higher ed, they would translate courses, research, and pre-professional experiences into recognized evidence of growth and capacity.

As Dr. Tony Wagner and Dr. Ulrik Christsen emphasize in conversation with Tom Vander Ark, making learning visible is not just a technical exercise; it’s a transformation in how we define, measure, and communicate mastery. And, mastery requires developing judgment, motivation, and context through experience. The skills-based future cannot become another checklist.

Pressure Point: Building Trust and Coherence

Even with strong vision and design, adoption remains the most significant barrier to a trusted, functioning signaling ecosystem. Signals are the system’s most fragile and consequential layer. They depend not only on data standards and digital infrastructure, but also on trust among learners, families, employers, educators, and policymakers.

• Leadership is diffuse. Who is responsible for stewarding this system—schools, employers, states, or learners themselves? As Dr. Agbeshie-Noye explains, new signals will require a coalition of actors to define leadership, policy, and purpose. We already see early movement through state wallet pilots in places like Indiana’s Achievement Wallet and North Dakota’s Digital Credential, where governments are experimenting with ways to store and share verified learning and employment records. But no single entity yet “owns” coherence across this growing ecosystem.
• Adoption headwinds are strong. Especially in today’s chaotic educational landscape and labor market, there is little incentive—or capacity—for transformation. Without clear value propositions, shared incentives and accessible tools that make new signals easy to use, even strong models struggle to scale. Too often, credentialing innovation still collides with calcified legacy systems—from transcript infrastructure to outdated funding formulas—that reward compliance over change. Creating “pull” for adoption will require lowering friction and raising perceived value for every participant, from individuals to employers. 
• AI changed everything. As Expanding Access previewed, AI may help translate experiences into validated skills, but it also raises questions about privacy, sovereignty, and authenticity. Beyond translation, AI introduces issues of algorithmic bias and authorship: Who vouches for the accuracy of AI-inferred skills? What happens when evidence is machine-summarized rather than human-witnessed? Clear roles for human validation from educators, mentors, and supervisors will be essential anchors for credibility. These tensions surfaced in Getting Smart’s “Catching Up: Ambient AI and the Educator of the Future” episode with Nate McClennen and Mason Pashia, which underscored how educators’ human judgment will remain vital in a future mediated by AI.
• Cross-sector collaboration is challenging, but critical. As Kerry McKittrick shared on a recent episode of the Getting Smart Podcast, creating better pathways to economic mobility depends on bridging the languages and logics of education, the workforce, and industry. That kind of coordination—across policy, practice, and research—is the connective tissue of a skills-first future.
• Local ecosystems matter. Real trust starts locally. The fastest path to legitimacy is not through national mandates but through local efforts and pilots that prove what works, build evidence, and earn belief. These local ecosystems become the laboratories for systemwide coherence. (See the LER Pilot Directory for a range of pilots from individual schools to large consortia approaches.)
• Trust matters most. Implementation at scale will require socialization and storytelling. People must know what these tools are, why they exist, and how they serve them. Stakeholders need plain-language explanations, use cases they recognize, and clear rights over their data: What’s in my record? Who sees it? Who can contribute to it? Past efforts, like inBloom and Common Core, offer cautionary tales: even the most logical systems can collapse if they outpace public understanding or threaten users’ sense of ownership and autonomy. The lesson is clear: trust is not a byproduct of innovation; it is its precondition. 

Tension Point: Experience vs. Skills

Across the field, there is also an unresolved debate about what exactly to credential.

Most of the field focuses on skills as the primary unit of value. That logic makes sense—skills are measurable, comparable, and directly connected to workforce needs. But skills need context and evidence to truly be valuable. For this reason, we’ve been exploring the role of experiences in the credentialing ecosystem. Experiences integrate skills, context, and human judgment. They reveal not only what someone can do, but how and why they do it. 

Context matters because capability is conditional: the same “collaboration” skill strength on one student’s record looks very different if earned on a high-stakes client project than on a short classroom activity. This nuance and contextual dependability are why Getting Smart developed  a a draft of Experience Quality Indicators—Responsibility, Complexity, and Novelty—which offer a way to measure and validate the quality of learning experiences by emphasizing depth, authenticity, and impact, rather than simply completing tasks. Learners build robust portfolios of experiences that merge multiple skills, foster social capital, and reveal growing responsibility and challenge. In this model, experiences become the evidence behind competencies, competencies form the foundation of credentials, and AI-powered systems help translate that accumulated evidence into a set of visible, valuable signals recognized by schools and employers alike.

This work is supported by the research being conducted at the skills level. Education Design Lab (EDL) and others continue to advance the assessment of durable skills. In its Leveled Durable Skills Framework, EDL identifies Autonomy, Complexity, and Influence as the three performance dimensions that determine how these skills are demonstrated in practice, from foundational awareness to advanced mastery. Autonomy measures how independently a learner applies a skill, Complexity reflects the sophistication of the task or context, and Influence captures the degree to which a learner’s actions affect others or outcomes. The goal is to make skills more portable, measurable, and meaningful across contexts and career stages. 

This echoes insights from Wagner and Christensen’s Mastery: Why Deeper Learning Is Essential in the Age of Distraction. They remind us that real mastery blends skill, will, and purpose—an idea deeply aligned with both EDL’s dimensions and Getting Smart’s experience indicators. Assessment, in this view, is not about compliance or completion but about evidence, reflection, and trust.

Together, these approaches highlight a shared goal: connecting what is learned and what is valued. As the ecosystem evolves, the question may not be “Which is right?” but “How can experiences and skills inform and strengthen each other?”

That question is where Getting Smart’s work continues: exploring how to design, validate, and scale a unified system of signals that honors both the rigor of skill and the authenticity of experience. Some of the questions include:

• How can credentialing systems bridge the gap between skills and experiences, producing signals that are both visible and valuable to multiple users?
• How can AI help translate learning while safeguarding privacy, mitigating bias, and preserving credible human validation?
• What can we learn from state wallet pilots and local ecosystems that are already experimenting with new trust models, governance, and interoperability?
• Which approaches and models have the potential to accelerate and incentivize adoption across K–12, higher education, and workforce systems? 
• What gaps in understanding, communication, or shared language will the field need to close to build trust and confidence in new signals?

If the last few years have been about designing the new architecture for learning, this next chapter is about testing whether it can carry the load, scale with integrity, and stand the test of time in an ever-evolving future of learning. Signals are the connective tissue of that architecture. They are what will make the next decade of education transformation a reality—or continue to reveal where it still needs to evolve. Signals are the proof point. But they are also the pressure point. And that is exactly why they matter.