North American Artificial Intelligence Association
Build the foundations that help AI move forward.
Our mission brings education standards, community organization, global competitions, and AI proficiency together with owned computing infrastructure.
Build with the association
A shared mission with practical foundations
The North American Artificial Intelligence Association is focused on the structures that help people learn, organize, and demonstrate AI capability. Our work is directed toward developing education standards, supporting a well-governed North American AI club ecosystem, organizing global competitions, and building an AI proficiency and certification framework.
These areas reinforce one another. Education defines useful learning goals. Communities give people a place to practice and exchange ideas. Competitions create opportunities to test an approach. Proficiency assessment asks what the evidence actually demonstrates.
We also own a computing facility with 240 GPUs across 30 eight-GPU servers: 40 H100 GPUs, 40 A100 GPUs, and 160 A6000 GPUs. That infrastructure gives future programs a practical resource base. Access arrangements and the suitability of particular workloads will be considered separately.
Build governance alongside the programs.
Clear responsibilities matter wherever the association develops standards, recognizes a community, or evaluates someone’s work.
Organizational development: the advisory and committee structure below is proposed. Confirmed appointments, participation terms, and formal policies will be published when available. No officer or committee membership is announced here.
| Area | Intended responsibility |
|---|---|
| Education and standards | Review learning outcomes, curriculum guidance, consultation feedback, and revisions. |
| Communities and chapters | Develop recognition criteria, organizer responsibilities, and chapter review processes. |
| Competitions and evaluation | Review challenge rules, assessment integrity, conflicts of interest, and result-review procedures. |
| Proficiency and certification | Develop competency criteria, evidence expectations, assessor guidance, and appeals design. |
| Infrastructure and responsible use | Consider project suitability, access principles, operational responsibilities, and permitted use. |
Principles that should show up in the work
- Make the basis for decisions clear.
Criteria should be understandable before people participate. We will work toward documented requirements and explanations for material changes.
- Evaluate evidence honestly.
A claim should reflect what has been demonstrated. Participation, completion, proficiency, and formal recognition should each be described accurately.
- Take responsible use seriously.
Privacy, attribution, appropriate access, and the limitations of AI systems will be considered within educational and technical activities, not treated as an afterthought.
- Leave room for review.
Programs will need ways to receive feedback, identify errors, manage conflicts, and revise guidance as experience and AI practice develop.
Different perspectives make the foundation stronger.
Educators can identify gaps between a curriculum and actual learning. Club organizers can explain what helps a local community endure. Researchers and practitioners can test whether a proposed competency or challenge reflects meaningful work. Partners can contribute resources, practical problems, and informed review.
We welcome these conversations while the programs take shape. Membership categories, chapter terms, and formal partnership arrangements are under development. Expressing interest is a starting point for discussion, rather than enrollment or a commitment by either party.
Tell us where you can contribute.
Share your experience, your community or organization, and the part of the mission you would like to help develop.
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