Instructional designer with a background in enterprise information technology and in the classroom. I build courses that hold up to a quality rubric, to an accessibility audit, and to an actual learner, and I use innovative educational and AI technologies to deliver evidence-based results.
I design online learning with an engineer’s habits and a teacher’s instincts. I spent several years as an Information Systems Engineer at a large financial corporation working with UNIX systems, mid-tier infrastructure, enterprise databases, and Tier 2 support. Since then, I spent years teaching, creating learning and instructional designs, and advocating for students whose needs the standard course design had not anticipated. To me, Instructional Design is the field where systems thinking and teaching intersect.
What I care about is quality online education that is genuinely delivered rather than merely posted. In practice that means courses built to a standard that can be verified, materials that a learner with a disability can actually use, and design decisions that I can explain to the person paying for them.
I built two versions of the same asynchronous graduate course, identical except that one carried a short instructional video in each module, and I measured what happened. The video group reported higher course satisfaction but scored lower on course assessments.
A twenty-hour micro-credential for higher education faculty, designed against the Department of Justice web accessibility rule and the 2026 and 2027 compliance deadlines.
A complete online course built solo from a client’s observation through to a formative evaluation session with real high school learners in the room.
I design against WCAG 2.1 AA and Universal Design for Learning from the start rather than conducting an audit at the end. I do this because retrofitting accessibility costs more money, takes more time, and produces a worse result than designing for it from the start.
Where I can test a design decision rather than assume it, I do. Where I cannot, I explain my reasoning instead of presenting a preference as a finding. This applies to everything I design and build — educational courses, curriculum materials, and learning analytics models.
A design that assumes an educational expert in the room will fail without built-in educator support. I build for the person who has to deliver the learning — the adjunct professor with four sections, the camp counselor, the faculty member who received the course two days before the start of the term.
I use educational technologies and generative AI daily in my instructional design workflow to research design specifications, draft assessments or narration, or to comply with accessibility standards. My use of AI is always evidence-based, grounded in research, and always disclosed.
Before I design anything I find out whether instruction is even the right answer. For the investing course, I checked where a learning gap came from before writing a single screen. For the CryptoComics redesign my team mapped the existing program before proposing any changes. For the affect detection models, I inspected the data before building a single model. A course built to address the wrong problem is an expensive way to solve nothing.
I test my work with real learners before calling it finished. That means a formative evaluation session with actual learners, a sponsor review with the person who truly knows the material, and a controlled study when the question needs an answer. Design without evaluation is a hypothesis somebody else has to pay to test.
