Work
I work with institutions, learning companies, and research-minded teams who are navigating how learning changes in an AI-rich world.
My role is often to help make the invisible structure visible—to surface the assumptions, systems, and design choices underneath curriculum, assessment, and learning technology, and to find places where small shifts can unlock meaningful change.
Most engagements blend strategic thinking with hands-on making. I'm less interested in producing reports than in building shared understanding, working prototypes, and internal capability that lasts.
Much of this work happens upstream—before solutions are obvious.
Areas of Exploration
Semantic Architecture for Learning Systems
I design the conceptual and semantic layers that allow learning systems to reason about relationships—between concepts, skills, evidence, and progression.
This includes ontologies, competency models, and other connective structures that sit beneath interfaces and content, shaping what systems can 'understand' and adapt to.
AI-Readable Curriculum & Learning Structures
I help re-architect curriculum, syllabi, and learning frameworks so they can be meaningfully interpreted by both humans and AI systems—without flattening pedagogy or intent.
This work often involves surfacing hidden assumptions, making learning logic explicit, and translating tacit educational design into structured, machine-legible form.
AI-Era Assessment & Evaluation
I work on assessment models that remain credible when AI is present—favoring diagnostic insight, sense-making, and transfer over recall or surveillance.
This includes both learner-facing assessment and system-level evaluation: how we know whether AI-mediated learning experiences are actually working, and for whom.
Pedagogical Agents & Learning Evals
I design how AI tutors, coaches, and learning agents should behave—what they attend to, how they intervene, and how their performance is evaluated over time.
This work brings learning science, interaction design, and evaluation frameworks together to ensure agents support thinking rather than replace it.
Prototyping & Exploratory Build
I use prototyping as a way of thinking—building small, concrete artifacts to explore complex questions about learning, AI, and systems design.
These prototypes are not demos for show; they're working objects that help teams see trade-offs, test assumptions, and learn faster than discussion alone.
Personalization & Adaptive Learning
I work on approaches to personalization that go beyond content branching—focusing on learner context, goals, constraints, and developmental readiness.
The aim is not maximal customization, but meaningful adaptation: learning experiences that respond to people without becoming brittle, opaque, or extractive.
How I Work
I usually begin with a period of discovery—listening closely to how things work today, where the constraints are, and what success would actually look like in practice.
From there, the work might include facilitated conversations, system mapping, rapid prototypes, or short periods of embedded collaboration. I work best with teams who are genuinely curious, comfortable sitting with uncertainty, and interested in learning their way forward.
The problems I'm most drawn to live at the intersection of pedagogy, technology, and organizational change—where clean answers are rare, but good design can still create clarity.
Let's talk
If you're working on something in this space and want a thoughtful partner to explore it with, I'd love to hear about it.
Get in touch