Learning Twin
The substrate. Per-topic mastery + Bloom's level + Lexile + accommodation context, updated after every submission. Nothing else in the AI surface runs without it.
Pillar · AI personalisation
Personalisation by Bloom’s level — not by vibes.
Most “personalised” ed-tech sorts students into bins and calls it a day. CurioPilot builds a per-subject mastery model — the Learning Twin — and calibrates every activity, hint, and recommendation against it.
GDPRCOPPAFERPATraceLayer audit log
app.curiopilot.com / maya / mathLive
Learning Twin · Math · Grade 4
Maya R. · updated 2m ago
Equivalent FractionsApplying88%
Long DivisionUnderstanding64%
Decimals → FractionsApplying72%
Mixed NumbersAnalysing81%
Word ProblemsUnderstanding58%
Place ValueEvaluating94%
Active misconceptions
Treats fraction bar as subtractionProcedural · 3×
Ignores remainder in long divisionConceptual · 1×
Recommended next 3
1. Word problems · ZPD-pinnedWhy?
2. Long division with remaindersWhy?
3. Fraction → decimal practiceWhy?
The thesis
The bin model is broken.
Most “adaptive” ed-tech sorts a child into one of three buckets — easy, medium, hard — based on a single quiz score. Then it serves the next activity from that bucket. That’s not personalisation; that’s a difficulty knob with a smile.
Real personalisation needs a per-topic, per-subject, per-Bloom’s-level mastery model that updates after every interaction. It needs to track what kind of mistakea student just made, not just whether they got it wrong. It needs to know that “ignored remainder in long division” is procedural, fixable in two minutes; “doesn’t grasp fractions as parts of a whole” is conceptual, weeks of work.
That’s the Learning Twin. It’s not a marketing layer; it’s the substrate the rest of the AI features run on.
The loop
How the AI runs.
Five nodes, run on every activity. Every iteration writes to TraceLayer.
- 01
Student takes activity
Submission + hint requests captured.
- 02
AI extracts misconceptions
Bloom's classifier + misconception detector run on the submission.
- 03
Subject profile updates
Per-topic mastery + Lexile + accommodation context refresh.
- 04
Recommendations recalculate
Next 3 topics surface with a 'why' tooltip.
- 05
Next activity calibrates
Difficulty, format, and reading level pinned to ZPD.
Closes the loop → back to step 01 with a calibrated next activity.
The catalog
Everything that runs on the Twin.
Each feature is consent-gated and audit-logged. The shield means it’s enforced in code by TraceLayer.
Audit-gated
AI Quiz Generator
Pulls the student's current ZPD, Lexile, and active misconceptions, then drafts a 5–10 item set calibrated to the next-best topic. Teacher edits + approves before a student sees it.
Spark — hint-giver
Hints, not answers. Subject-scoped, Bloom's-aware. Walks through reasoning step by step when a student is genuinely stuck — but won't write the essay or do the maths.
AI Writing Coach
Inline feedback on argument structure, evidence, and clarity. Never rewrites the student's voice. Surfaces specific reverse-outline suggestions a teacher can adopt with one click.
Personalised Learning Paths
30/60/90-day topic plans drafted from the Twin, with a 'why this order?' rationale per step. Re-plans automatically when mastery jumps or stalls.
Three copilots
Spark, the Parent Guide, and the Teacher Coach.
One architecture, three audiences. All three pass through the same consent gates. All three log to TraceLayer.
for students
Spark
Hint-giver, never answer-giver. Subject-scoped, Bloom's-aware. Will walk through reasoning step by step when a student is genuinely stuck — but won't write the essay or do the maths.
for parents
Parent Guide
Plain-English chat for parents. "What did Maya struggle with this week?" "Why is this activity recommended?" One-click actions: schedule a check-in, switch to a calmer topic, change the consent state.
for teachers
Teacher Coach
Friday digest with six action cards ranked by impact. Drafts reteach lessons, parent updates, conference briefs. Teacher edits + approves; nothing leaves the platform without a human signing off.
The honest list
What our AI doesn’t do.
Every “won’t” below is enforced in code, not just policy. Most of them refuse to compile if violated.
- Send student names, emails, or birthdays to AI providers — the redactor refuses to compile if you try.
- Train models on your students' data. (Enterprise tier contracts forbid this on the provider side too.)
- Replace teacher judgement on grading. Every AI score is overridable; the teacher's score wins.
- Personalise without consent. The DPA + COPPA gates run before any prompt is assembled.
- Make decisions about students that aren't reviewable. Every AI decision lives in TraceLayer, forever.
- Use third-party trackers in the AI surface — no behavioural ad pixels, no cross-site fingerprinting.
- Provide AI features without redaction. The PII boundary refuses to compile a route otherwise.
Each item above is enforced by TraceLayer + the build-time PII attestation. Read the full compliance posture at /compliance.
FAQ
Questions admins, parents, and DPOs ask.
After every submission. The Bloom's classifier and misconception detector run on the response, then per-topic mastery, Lexile, and accommodation context refresh in roughly 800ms. Every update writes a row to TraceLayer with the input hash, model version, and confidence score.
Demo
See the Learning Twin live.
30-minute walkthrough with you, your IT, and your DPO. We’ll build a Twin from a sample student dataset and show you how it updates in real time.