About

My work sits at the intersection of AI innovation, learning experience design, and higher education. I’m a learning experience designer and AI strategist, grounded in learning science, focused on the future of education: closing the gap between learning and real-world practice, in curriculum, in tools, and in the systems that support students and faculty. I build with frontier AI every day; it’s how I think now. What most AI work skips is the learning science, and grounding what I build in how people actually think and learn is my edge.

I start from measurable outcomes: what students need to be able to do when they graduate, including the AI skills their industries already expect. That outcomes-first framing shapes everything I design, courses, career tools, and the systems that connect students to support. Because the goal is demonstrated skill, students show what they can do through authentic, performance-based work: portfolios, presentations, real job searches, networking. My method doesn’t change with the size of the audience. Start with the people and the problem, never the technology. Write the requirements down before anything gets built. Prototype, put it in front of real users, watch where it fails, revise, and don’t scale until the evidence says it works.

Real-world experience is central to how I teach. I built a design studio where students take on real client work with live briefs and hard deadlines, which grew past the course into a grant-funded paid studio I now advise, and I oversee the program’s internship, placing and mentoring students in real work with local businesses and industry partners. Giving young people genuine ownership, and watching them rise to it, is some of the most important work I do.

The question I keep returning to is what still counts as evidence of learning now that an AI model can produce the artifact. A portfolio piece, a reflective essay, a client rationale: an AI model will write any of them, and they are all authentic tasks. What resists substitution is the record of making, the iteration, the response to critique, the decision a student can defend. So I design assessment around process evidence rather than the finished thing, and I test whether it holds before asking anyone else to adopt it. Closing that loop matters more to me than any single result: when a measure is not doing its job, I change the design and measure again. In one course the data showed a vocabulary quiz was not discriminating, so I replaced it with an applied task tied to each student’s own work and re-checked the next term.

This fall I’m building an AI-simulated client into my branding course, one students consult throughout for feedback while their design decisions stay theirs, making the assessment itself a simulation. My goal is to extend that toward immersive simulation in our new campus XR lab.

Community colleges have remarkable resources; what breaks down is the connection between them and the students who need them most, most of them working adults. When students feel connected and supported, they persist, and that’s the problem I focus on now. I co-chair the Student Support and Success domain of the Maricopa district AI Resource Center, working across all ten colleges on how AI can reduce friction in the non-classroom services that decide whether students stay.

My background spans design, education, and educational technology: from web and graphic design, to UX, to product management at an EdTech startup, to instructional-technology leadership, to faculty in Digital Media, where I teach design and was Program Director for over a decade. I hold a master’s in Educational Technology with an adult online-learning emphasis, and connectivism and personal learning environments are still the floor under everything I build. Accessibility goes in the first draft, courses ship as clean Canvas packages built from open and licensed materials, and I lead a campus AI community of practice as a League for Innovation AI Fellow.