Technologist. Polyglot Programmer. Computer Science Educator. Investigating how AI tools reshape learning, thinking, and what it means to truly understand code.
What happens when students use AI to learn programming? They get answers faster. Code appears. Problems get solved. But something crucial is lost.
My research investigates student alienation from their own learning when AI becomes the intermediary between problem and solution. Students lose the struggle—the essential cognitive work of developing pattern thinking that transforms novices into programmers who truly understand.
This isn't anti-AI rhetoric. It's a philosophical examination of what learning actually is, what cognition requires, and how we preserve the depth of understanding while teaching in an AI-saturated world. The tools that promise to make learning easier might be making genuine learning harder.
This work matters because we're training the next generation of technologists. If they never develop the mental models that come from wrestling with complexity, genuine learning doesn't occur—alienation takes root and understanding fails to materialize.
15+ years building software at scale. Mobile, web, backend. Shipped products used by millions. Led teams. Taught students. Every line of code informs the philosophy.
Leading mobile development for healthcare technology. Building systems that matter.
Teaching computer science. Shaping how students think about technology, ethics, and craft.
Long-form essays on AI, pedagogy, and technology culture—working out in public what happens when we offshore cognition to machines.
Essays on learning, alienation, and the tools reshaping how we think—written from the intersection of the classroom and the codebase.
I don't just write about technology—I build it. That dual perspective matters. You can't understand what AI does to learning without understanding both philosophy and the actual mechanics of how these systems work.