Faculty spotlight: building the AI curriculum from scratch.
Designing a program around a field that changes every semester means the curriculum can never be finished.
When the School of Artificial Intelligence's founding faculty set out to design the first-year curriculum, the hardest constraint wasn't content, it was half-life. A syllabus built around a specific model architecture risks being outdated before the students who learned it graduate.
The approach that stuck was teaching the durable layer first: the math, the evaluation discipline, the debugging instincts, and only then the current tools. Students who understand why a model fails a certain way can diagnose the next architecture too, not just the one covered in lecture.
That means the curriculum is reviewed every term, with faculty rewriting assignments as the field moves. It's more work than a static syllabus, but it's the only way to keep the degree relevant to the field students will actually graduate into.