2026 Badge Summit

With over 1.8 million digital credentials currently available in the U.S., the landscape is characterized by rapid growth but deep fragmentation. Many state-funded initiatives remain "islands of recognition," lacking the cross-institutional portability that modern learners and employers demand. While technical standards like Open Badges 3.0 provide a framework for verification, their utility is often undermined by proprietary data silos and the absence of an interoperable skills language.


Traditional efforts to mandate unified taxonomies frequently fail due to high administrative costs and resistance from institutions seeking to maintain continuity with established practices. AI-driven mapping tools, such as LAiSER, resolve this tension by automating the translation of localized curricula into standardized frameworks. By utilizing AI to align diverse curricular materials to broadly adopted skills taxonomies, these tools eliminate the manual burden of alignment and lower transition costs. This functional approach allows institutions to maintain their unique educational identities while achieving the machine-readability required for recognition in a broader ecosystem.


In this session, presenters will explore systemic barriers to badge adoption and offer a theory of change for state-level policy. The presentation provides a roadmap for transitioning from isolated pilots toward unified "skills data ecosystems." By adopting open-source standards and AI-assisted alignment, state leaders can ensure micro-credentials function as transferable assets that provide verifiable value throughout a learner's career. The authors will present evidence on how top-down state action can dismantle data silos, enhance social mobility, and create a responsive, skills-based economy