Camryn Andrews

I work at the intersection of three fields that don't usually share a résumé: cognitive science, artificial intelligence, and classroom instruction. Each one has shaped how I think about the others.

My academic foundation is in cognitive science — currently completing a Master of Arts with Honours (2023–2026) — where my coursework has ranged from statistical modelling of psychological data in R, to building a text-to-speech system from scratch in Bash to study how predictable speech patterns are, to examining how infants encode number using EEG data. What ties that work together isn't the subject matter, it's the question underneath all of it: how does a mind — biological or artificial — build a usable model of the world from limited, noisy input?

That question is also what I work on directly as an AI Developer and Trainer at Waverider Technology, where I gather, develop, and train datasets that shape how a beta application's machine-learning-informed interface actually behaves — hands-on experience with the gap between what a model is trained to do and what a real user experiences.

And it's what I've spent the last several years teaching directly. As an instructor at Northwestern University's Center for Talent Development, I design and deliver curriculum in Psychology, Sociology, and Biology for a highly selective population — students admitted through a competitive gifted-applicant process — currently Abnormal Psychology, previously Criminology and AP-level Psychology. Teaching a population that selective has forced me to get precise about instructional design: how to sequence a difficult concept, when to change modality, how to build materials that adapt to a student who already suspects the easy explanation is incomplete.

That instructional instinct doesn't stay confined to a high school classroom. I've carried the same approach into teaching adults — both in independent freelance work and within Waverider — about cognitive science, and specifically about how AI models represent and process information. Translating a research-level concept into something a working adult can actually use is the same skill either way; only the audience changes.

I'm looking to bring that combination — a research-grounded understanding of how learning happens, direct experience building and training AI systems, and a practiced instructional-design skill set — to work that helps people build real fluency with AI, not just familiarity with it.