Insights on AI education
Published essays on pedagogy, safety, and the engineering behind Go2LearnAI
Latest
From consumer to creator: safe first projects for a child in their first month with AI
The first month shapes everything that follows. A week-by-week plan of small, safe, genuinely creative projects for a child meeting AI tools for the first time, with the ground rules that make the whole thing workable for parents.
Is the AI tutor teaching or just answering? Questions parents and teachers can ask
An answer machine and a tutor look identical in a screenshot. The difference shows up under pressure. A practical set of live tests parents and teachers can run on any AI tutor, and what good refusal behaviour looks like when you see it.
Earlier
Prompt literacy is the new grammar: a practical ladder for ages 8 to 16
Asking a machine a good question turns out to be the same skill as thinking clearly. A rung-by-rung ladder for building prompt literacy from age 8 to 16, for parents and teachers who would rather teach the skill than ban the tool.
Screen time or creation time: why what your child makes matters more than the minutes
Counting minutes tells you very little about what a screen is doing for a child. The better question is what the child has at the end of the session. A practical way for parents and teachers to judge screen use by what it produces, and why we refuse to build streaks.
Honest Health Endpoints: An Engineering Culture Note
A health endpoint that always says 'ok' is a lie with an HTTP status code. Ours report which backend is actually serving — in-memory or Postgres — because the fastest way to corrupt an engineering culture is to let the system flatter you.
Designing Guardrails for Young Learners, Not Around Them
Child safety in an AI product is usually implemented as a blocklist bolted to the output. We built it as a constitution with tiers, enforced on both the way in and the way out of every model call — and made the strictest rules impossible to configure away.
Inside the Originality Gate: Inspired By, Not Copied
Every piece of content that reaches a learner on this platform passes a publication gate that scores it against its own cited references. Here is how the gate works — shingles, similarity, verbatim runs — and why its verdicts are sealed into a hash-chained provenance trail.
Why Socratic Tutoring Beats Answer Engines
Most AI chat products optimise for the fastest correct answer. For learning, that is precisely the wrong objective. This piece explains why our tutor is built to ask before it tells, and what that changes about every layer of the product.
Every insight on this page carries a provenance record — which model drafted it and when — shown at the end of each article. Read one