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Data handling terms
Education agreements that keep student and research data out of training pipelines, in writing.
Our relationships with leading model providers give students and faculty access to production-grade AI tooling for coursework and research, without vendor lock-in or unvetted black boxes.
Why this partnership exists
We evaluate model providers the way we evaluate faculty candidates, on capability, safety track record and how confidently their systems perform under real coursework and research conditions.
The best model for a project changes every few months. Our commitment to students shouldn't.
Building coursework and research tooling around a single model provider is a bet students didn't sign up for. We saw programs elsewhere get boxed in by that choice.
So we built the ability to swap the underlying model without rearchitecting coursework or research tools, which lets us hold providers to a real standard instead of accepting whatever they ship next.
Every provider is evaluated against the same bar before student or research data touches their models.
Education agreements that keep student and research data out of training pipelines, in writing.
Providers with a real track record of catching failure modes before they reach a classroom.
Production-grade uptime and response times for the tools students rely on during a term.
Architecture that lets us swap providers without disrupting a running course.
The same evaluation rigor our AI faculty apply to any system used in the classroom.
Candidate models tested against real coursework and research tasks, not a generic leaderboard.
Structured evals and human review built around whichever model is chosen for a course or tool.
Rollout with cost, latency and safety monitoring from day one of a term.
Regular reassessment as new models ship, so coursework keeps the best available tooling.
AI tooling that's chosen on merit, reviewed on a schedule, and never a black box.
A student should never have to wonder which model is behind a course tool, or why.
That means clear documentation of which provider handles which task, what data it sees, and what happens when its confidence drops.
It also means the freedom to move to a better model the moment one exists, instead of being stuck with whichever provider was integrated first.
For the rest of our partner network, see all partners or talk to us.