Each pathway structures the journey from observation to evidence. Submissions from any pathway are reviewed, curated, and translated into cases, micro-frameworks, governance prompts, and practice briefs.
Small groups test AI against real teaching, training, assessment, and feedback scenarios. These produce documented cases.
Participants define a learner/trainer problem, prototype an AI-supported approach, test it against responsible AI criteria, and document what changed.
Practitioners test a focused question in their own context, collect light evidence, reflect, and submit findings.
Structured sessions where participants validate whether AI-supported processes demonstrate responsible capability, not just awareness or adoption.
Workshops that examine whether AI-supported workforce preparation, upskilling, and credentialing meet responsible standards for competency development.
Focused sessions examining assessment integrity, auditability, and proof-of-control when AI participates in evaluation, grading, feedback, or credentialing.
Multi-stakeholder discussions that surface real tensions at the intersection of education, workforce, technology, and governance.
Online lab sessions supported by the Online Learning Consortium infrastructure, enabling distributed participation in governance stress-testing and evidence collection.
Lab sessions supported by Celonis process intelligence, using process mining to trace where AI participates in teaching, training, and credentialing workflows and to ground governance findings in observed process data rather than reported impressions.
International dialogues examining how responsible AI governance in education and workforce varies across regions, cultures, and regulatory environments.