I set out to teach. The rest followed from one stubborn question — what happens to the people in the room when the machine walks in?
Before any of the AI work, there was a classroom. I'm a Professor of AI & Computer Studies at Long Beach City College, and for years the job was simple to describe and hard to do well: take something complicated and make it land for the person in front of you.
That instinct — make it make sense for the human — turned out to be the only lens I'd need for everything that came next.
When generative AI arrived, the conversation rushed straight to the tools — which model, which prompt, which feature. I kept getting stuck on a slower question: are the people who teach ready to meet it?
The bottleneck was never the technology. It was readiness. So I started drawing a line that still guides the work — the difference between AI literacy, knowing what it is, and AI fluency, knowing how to work alongside it without losing your judgment.
Rather than wait for a path to exist, I built one. It became the first associate degree in AI for digital transformation approved in the California Community College system — a six-course pathway that moves learners from literacy to fluency.
Ethics first. No-code and low-code by design, so the door stays open to people who were never told this field was for them. That last part matters more to me than any of the rest.
One campus became nineteen. As Principal Investigator of the LARC LA-25 AI Literacy & Innovation Project, the work now reaches community colleges across Los Angeles County — research, curriculum, instructional practice, and faculty development, held together by a shared community of practice.
Along the way: an MIT Applied GenAI Fellowship; TandemAI, where I think out loud about this in public through a newsletter, podcast, and series; and doctoral research at CSU Long Beach that grew into the ESAI framework — AI integration that starts with the learner's context, not the technology.
The throughline hasn't moved since that first classroom. The technology will keep accelerating — that part takes care of itself. My job is to make sure the people aren't left behind by it: faculty, students, whole institutions learning to meet AI on human terms.
Build for the human side. The rest follows.