edAIArk is the documentation and evidence infrastructure of the Collaborative Intelligence Campus — gathering practitioner-generated evidence across nine structured pathways to document what happens when AI participates in teaching, training, assessment, and workforce preparation.
The AI 2030 Collaborative Intelligence Campus is a structured ecosystem for convening, testing, documentation, validation, and responsible AI capability development. edAIArk serves as the documentation and evidence layer — the place where everything tested, observed, and learned across the Campus gets captured, curated, and made available.
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.
International dialogues examining how responsible AI governance in education and workforce varies across regions, cultures, and regulatory environments.
Contributions document how AI affects teaching, training, assessment, feedback, learner agency, and responsible capability development.
Every submission — regardless of pathway or type — must address these eleven dimensions to ensure evidence is credible, comparable, and actionable.