How LER Standards Enable the Human Intelligence Layer
The Learning and Employment Record ecosystem has spent years making learning and workforce data portable, verifiable, and individually owned. This article explores how that infrastructure can now support a Human Intelligence Layer that helps people, organizations, and AI continuously learn together.
Why Talent Data Still Cannot Speak Across Systems: STAMP and Semantic Interoperability
Talent systems can exchange data without understanding it. Explore how STAMP approaches semantic interoperability by connecting skills with experiences, evidence, provenance, context, and external frameworks.
Microcredentials Need Translation—Not One Universal Taxonomy
Microcredentials emerge from different industries, institutions, cultures, and competency frameworks. Instead of forcing them into one master taxonomy, the ecosystem needs semantic bridges that preserve local meaning while making credentials, skills, and experiences understandable across systems.
People Need Systems of Trust: Why Verifiable Credentials Matter to Universal Talent Passports
Verifiable credentials provide an essential foundation for Universal Talent Passports by preserving the source, integrity, status, and machine-readable meaning of trusted records. But real trust also requires evidence, issuer authority, consent, privacy, transparency, governance, and responsible human judgment.