DigitalRoot turns the CAPS curriculum into a living model of what a learner knows, skill by skill, and teaches to the exact gap. Private tutoring, for everyone who can't afford it.
Everything in DigitalRoot orbits one thing: a durable, structured picture of what a learner knows. Below you can walk the curriculum into it, then watch the model react to real answers — live.
Click down through the curriculum. Each level narrows to a precise, measurable skill — that's the unit the model tracks.
CAPS maps cleanly onto five levels. The model anchors every lesson and question to the bottom one.
Mastery isn't one score. It's three: can they do it, do they understand it, can they apply it. Press the buttons and watch the model — and the tutor's next move — change.
A model this precise runs for one child or a million — the marginal cost of teaching the next learner is near zero. That's how you give South Africa private-quality tutoring for free.
Procedurally fluent but doesn't understand? It teaches the "why", not more drills.
Catches an actively-wrong rule and corrects it surgically, instead of grinding practice.
A Grade 5 gap blocking Grade 7 work? It steps back, fixes it, resumes.
Live view of progress, and a way to confirm work done at home.
The teaching algorithms evolve from real learner data over time.
The learner model is the system of record. Content flows in; a tutoring front end runs the conversation. Flip the toggle to compare the wiring as it stands today with the recommended shape.
Three systems sit around the learner model. Here's what they are and what they make possible.
Most tutors hand-tune their learning algorithms once and freeze them. ShinkaEvolve (from Sakana AI) lets those algorithms keep getting better against real data.
Pedagogy that compounds — the longer it runs, the better it models how learners actually learn. Needs a real event corpus first, so it's a later phase.
Parents should see learning happen in real time. SpacetimeDB is a database clients subscribe to — updates are pushed live — which is exactly what a real-time dashboard needs and what a normal database does awkwardly.
Parents watching mastery rise live as their child learns — real engagement and trust — without ever compromising the system of record or data residency.
South African learner outcomes are strongly driven by parental involvement. The parent UI makes a guardian an active, safely-scoped part of the learning loop.
Parents who can see progress in real time and add to it — turning the home into part of the tutoring loop, safely and privately.
Honest status. The design is ahead of the code — the right order. One component is genuinely built; the rest are prototypes or specs.
The parts were specified independently. Where they meet — who owns the curriculum, who owns the turn, what content is real — the joins need work. These are the red badges on the diagram.
The scraper feeds the tutor's own loader, not the learner model — two sources of truth, and the scraped one bypasses the system of record.
The generated items are placeholders, not teachable material. The real content is still locked inside the source PDFs, unparsed.
The tutor has its own agentic loop; the learner model wants to own sequencing and per-turn routing. Nobody decided which is subordinate.
The fork spec targets upstream modules that were deprecated and partly deleted. The "patch it in place" plan has no surface to attach to.
Keep every differentiated idea — vector mastery, misconceptions, reach-back, routing, event sourcing. Move the heavy infrastructure later, and fix the joins.
The learner model is the only source of truth. The tutor becomes a thin render + LLM client that asks "what's the next move?" each turn and reports events back. It reads curriculum from the learner model — never authored twice.
Resolves seams 1, 3 & 4 at onceA dumb content lake (scrapers) feeds an LLM-assisted, teacher-validated authoring pipeline that drafts real lessons and questions into the learner model. That pipeline — not the scraper — is the actual moat.
Resolves seam 2 · builds the defensible assetGet the core teaching loop running on plain Postgres — recursive queries and a polling dashboard. Defer SpacetimeDB, the graph database, and algorithm evolution until there's scale and data to justify them.
Dogfooding loop months soonerThe teaching loop first, scale second, self-improvement last — once there's data to learn from.
Resolve these six and the rebuild plan writes itself.