Shilpa Kapur Singh — working notes Lexington, MA
Shilpa Kapur Singh
applied neuroscientist · education leader · founder

AI will either build human capability or quietly erode it. That isn't a prediction — it's a design decision, and almost nobody is making it on purpose.1

Fifteen years engineering education that demonstrably works — curriculum, assessment, and the unglamorous systems underneath. I treat AI as the next great research science, and applied neuroscience as the discipline that decides what it does to us.

I build the thing, then I write down what happened — at The EdJournal. Currently finishing an MSc in Applied Neuroscience at King's College London.

Shilpa Kapur Singh
that's me
the whole site is basically
this one sentence
§1 Selected work
Fig. 1 — Flagship · iterated across four model generations

AI by Design

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.

Fig. 2 — Research · adversarial, on purpose

Stress-testing AI on high-stakes assessment

Trained an AI marking system on real exam papers and anonymised student responses, then went looking for its biases. Finding out where it breaks is the finding.2

Fig. 3 — Shipped · in classrooms, not slide decks

AI teaching assistants

A chemistry assistant producing the full teaching content stack — lesson plans, differentiated tasks, guidance through complex practical work. Built, used, iterated.

the marking bias
turned out to be
the interesting part
§2 Observations
350+
practitioners trained across a multi-site trust
70%
of teacher planning time removed via AI workflows
4
continents reached by a venture built from nothing
80%
of a 250-student cohort met or beat targets, two years running
1
TEDx talk arguing grades don't matter. I stand by it.
every number here
has a story where
it nearly didn't work
§3 Where I've been
accidentally became
an instructional designer
at a paint company
§4 Grounding
§5 Elsewhere
§6 Get in touch

Advisory, speaking, collaboration — or an argument about where AI and learning are actually going. I'd rather have the argument. shilpakapur@outlook.com

Open to the interesting

1.Deliberately, anyway. Plenty of people are making it by accident, at scale, right now.

2.The model was confidently wrong in a beautifully consistent pattern. That pattern is now a human-in-the-loop check.

3.Powder coatings. Genuinely fascinating. Nobody believes me.