Fifteen years engineering education that demonstrably works — curriculum, assessment, and the unglamorous systems underneath. The gap between what learning science knows and what schools actually do is the problem I keep returning to. AI has made it urgent.
I treat AI as the next great research science, and applied neuroscience as the discipline that decides what it does to us. I build it, run it in real schools, and publish what actually happened — at The EdJournal. Currently finishing an MSc in Applied Neuroscience at King's College London.
A year-long body of work on AI in education: a framework, a position paper, a state-of-use review — culminating in a complete K–12 AI curriculum, designed end to end. Rebuilt each time the models moved.
Trained an AI grading system on real exam papers and anonymized student responses, then went looking for where it was wrong. Where it breaks is the finding.2
A chemistry assistant producing the full teaching content stack — lesson plans, differentiated tasks, guidance through complex practical work. Built it, put it in front of teachers, rebuilt it around what they actually needed.
Advisory, speaking, collaboration — or an argument about where AI and learning are actually going. I'd rather have the argument.
1.Which is still a decision. It just isn't one anybody wrote down.
2.The model was confidently wrong in a beautifully consistent pattern. That pattern is now a human-in-the-loop check.
3.A global business unit needed to be taught something. Nobody called it learning design. It was.