Curricula ready to use
- Mathematics
- Physics
- Chemistry
- Biology
- Computer Science Fundamentals
- Kiswahili for English Speakers
- Modern Engineering — The Da Vinci Curriculum
- MBChB / USMLE Step 1
Can we take an 8-year-old to PhD-level research in six years — and give them their most creative decade back?
These are open questions. Nobody has answered them at scale. But we already know one thing that used to be a boundary: Benjamin Bloom showed in 1984 that mastery-based instruction lifts the average student one standard deviation — past the 84th percentile of a conventional classroom. Add a one-to-one tutor and the same student rises two standard deviations — into the top 2%. That has been the verified ceiling of human learning for forty years, and it has been unreachable at scale because a private tutor per child is what it takes.
Until now. The mastery-gating algorithm carries the first standard deviation. Carefully-implemented AI — Socratic dialogue, layered explanations, real-time scaffolding — adds another half. An expert human tutor delivers the last half. Three layers, one loop. Not one of the three was possible a decade ago; all three are now. Which means the questions above are, for the first time, open to serious experiment.
Come adventure with us. All content is free forever. You pay only when the AI runs. Bring your own effort; we bring the leading edge of learning theory and the tools to apply it.
Three open questions we are running an experiment to answer
Can genius be learned?
For decades genius has been treated as innate — a fixed trait of the few. But every capacity we've measured so far turns out to be trainable, given the right sequence and the right feedback. If a learner is never blocked, never made to repeat, and never left to guess what to work on next — how far does the capacity extend? We're going to find out.
Can we compress K→PhD to six years?
The nominal 20-year path is set by the pace of the median in a class of forty. Strip out the median-drag, gate advancement on evidence of mastery, unblock hard nodes with a human tutor, and the ladder should collapse dramatically. Nobody knows how far. Our working hypothesis is six years from age 8. We publish what actually happens.
Can we give them their creative decade back?
The Fields Medal goes to mathematicians under 40 because the most creative decade of a life is the one before it. If a learner reaches the frontier of their field at 14 instead of 24, they get ten more years of original work. That is the prize. Not a shorter education — a longer creative life.
You don't need to believe our answer. You need to be willing to run the experiment on your own learning, on your own child's learning, and see what shows up. The diagnostic is free. The content is free. The evidence — good and bad — will be published as it comes in. If it doesn't work for you, cancel; the mastery map you built is yours to keep. If it does, tell us what we should try next.
Why
Kenya's KCSE mean grade is D+. The teacher-to-learner ratio is 40:1. Content isn't the constraint — the internet already has more content than any child can read. What's missing is knowing which concept your specific child needs next.
Form 3 concepts your child moves past
A classroom of 40 covers the syllabus at the median. Nobody names the specific six your child never actually mastered. That's where the marks are lost.
what most apps tell you
Not which concepts. Not which prerequisites. Not what to do next. Content volume dressed up as progress. Bloom's ceiling isn't reached by more content — it's reached by knowing what to work on next.
concepts already wired into the graph
Every one is free to read once you sign in. What you pay for is the AI that routes your specific learner through the graph — never for the graph itself.
Method
The 2σ effect decomposes into three layers. Algorithm. AI. Expert human. Each carries a fraction of the lift.
In 1984 Benjamin Bloom found that one-on-one mastery tutoring lifts students two standard deviations above conventional classrooms. Forty years later this remains the verified ceiling of what learning can produce, and it has been the unreached target of every serious edtech investment. The reason nobody has closed it at scale is that the sigmas come from different mechanisms. ALE ships all three — because none of them alone reaches the ceiling, and every product that pretended one did has empirically fallen short.
Layer 1 · +1σ
→ 84th percentile
The mastery-gating algorithm
A curriculum knowledge graph. Diagnostic that names the specific gaps. Mastery gating that refuses to advance until evidence is in. FSRS spaced repetition. IRT question calibration. This is what a strong tutor does in their first twenty minutes, running continuously in software.
This lifts the average student to the 84th percentile of a conventional classroom. What ALE delivers today.
Layer 2 · +½σ
→ 93rd percentile
Carefully implemented AI
Layered explanations generated for the specific misconception you fell into. Socratic chat that walks you toward the answer instead of narrating it. Oral assessment. Cross-domain analogies. This is the piece frontier LLMs make possible for the first time — done carefully, with domain grounding and the graph in the loop, not a chatbot hallucinating vibes.
Adds another half standard deviation — into the top 7%. Now shipping across every subject.
Layer 3 · +½σ
→ top 2%
An expert human tutor
The last mile — presence, real-time reading of confusion, Vygotskian ZPD calibration, motivation on days learning is hard. Only dispatched when the loop identifies a genuine block the algorithm and the AI together could not clear. A fraction of tutor hours per learner, targeted at the exact node that needs a human.
Closes the final half sigma — into the top 2%. ALE Assist, rolling out via the Talente marketplace.
Everyone who has tried to close 2σ with one layer has failed. Cognitive Tutor and ALEKS delivered the first half of Layer 1 and got ~½σ. Byju's and uLesson dropped the knowledge graph and got less. Khan Academy is retrofitting Layer 2 onto a content-first product. The AI-only edtechs are betting that Layer 2 alone reaches the ceiling — the research does not support that. All three layers, in one loop, is what nobody has shipped before.
How it works
Diagnose. Map. Drill. Re-test. The loop runs per learner, not per syllabus.
Diagnose
A 45-minute subject-specific diagnostic identifies your current mastery on every concept in the graph. Adaptive branching — a correct answer at level N triggers a level N+1 follow-up; a wrong answer triggers a prerequisite probe.
Map
Your mastery map is generated. Every concept is scored — mastered, partial, gap — and root-cause gaps are traced backward through the DAG. The frontier — what you're ready to work on next — becomes your personalised sequence.
Drill
You work through the sequence. Problems scaffold from prerequisite to target, with hints declining as mastery evidence accumulates. FSRS spaced-repetition maintains what you've closed. The engine never asks you to review what you already know.
Re-test
Weekly the diagnostic runs on the worked nodes. The map updates. Movement is visible on the parent dashboard. If a node stays blocked after three re-tests, ALE Assist packages the stuck point for a human tutor.
A working hypothesis: age 8 to independent doctoral research in six years, when nothing in the loop is wasted.
The K-12 → PhD path is nominally 20 years because a classroom has to move at the median, repeats what most learners already know, and cannot name — let alone close — the specific gap a specific child is carrying. Strip out the median-drag and the repetition, gate advancement on mastery evidence, unblock the hard nodes with a human tutor, and the same ladder should collapse. How far? Nobody has run this experiment at scale before. The path below is the ALE architecture's outer promise — the ceiling we're actively working to reach, not a guarantee we've delivered. What a child actually achieves depends on effort, hours, and support, and we'll publish the evidence as it comes in.
Year 1
Age 8 → 9
CBC Class 3 → 6
Diagnostic locates the child's real position (often 1–2 grades below nominal). Adaptive engine closes the numeracy, literacy and Kiswahili L1 gaps most classrooms leave behind.
Year 2
Age 9 → 10
Junior Secondary Grade 7 → 9
Full CBC junior-secondary science, math, humanities. The graph is now dense enough that root-cause remediation is the norm, not the exception.
Year 3
Age 10 → 11
KCSE Form 1 → 2
Formal KCSE mathematics, physics, chemistry, biology begin. Kiswahili L2 pathway available for English-first learners. FSRS scheduling begins across the entire mastered graph.
Year 4
Age 11 → 12
KCSE Form 3 → 4
The full KCSE syllabus completes. Mock papers, exam-date-anchored study plan, viva-style oral assessments. Grade-A ceiling is available to any learner who has ridden the loop honestly.
Year 5
Age 12 → 13
Foundation + Advanced Undergrad
First-year foundation content bridges into the discipline of choice — Da Vinci Engineering, MBChB Years 1–2, CS, econometrics. Hybrid engine begins: DAG core + early Semantic Concept Graph.
Year 6
Age 13 → 14
Postgraduate research
ALE Advanced takes over: research-readiness diagnostics over literature coverage, methodological fluency, theoretical coherence, writing and viva readiness, on a citation graph built from Crossref, OpenAlex and Semantic Scholar. The learner is now doing original research, not consuming curriculum.
The four compressions that make six years possible
1 · No wasted time
FSRS never asks the learner to review what they already know. The classroom, by necessity, does.
2 · No hidden gaps
Root-cause tracing names the specific unclosed prerequisite behind every failure — and closes it before advancing.
3 · No blocked days
When the algorithm can't unstick a node in three sessions, ALE Assist puts a real human tutor on it within the hour.
4 · Right ontology per rung
DAG for convergent K-12, Semantic Concept Graph for rhizomatic postgrad. Neither gets forced to pretend it's the other.
Why this isn't rote learning
Adaptive is not the same as drill-and-kill. We're building for understanding — the kind that transfers.
The crammer apps optimise for one thing: getting the specific past-paper question right next time. That's memorisation with a leaderboard. Understanding is different — it transfers to a question the learner has never seen. Every mechanism in ALE is chosen to build the transferable version.
Understanding, not recall
Questions are IRT-calibrated to your current ability — always one step past what you've already got. If you can only answer questions you've memorised, the engine bumps difficulty until you have to actually reason. There is no leaderboard for grinding the same item.
Layered explanations, not answer keys
A wrong answer surfaces a Hook / Rule / Bridge / Trap explanation — the intuition, the underlying principle, the connection to what you already know, and the specific misconception you fell into. Not just "the answer is B."
Socratic dialogue, not lecture
The tutor chat asks questions back. If you're stuck, it walks you toward the answer by making you notice things — the way a strong human tutor would — not by narrating the solution while you nod.
Root-cause tracing, not surface drill
Fail a Form 3 target concept and ALE walks the graph backward to find the actual root gap — often two years upstream. Drilling harder on the target never fixes the upstream cause. The whole point of the DAG is to make that hidden dependency visible.
Synthesis for postgrad, not mastery
The Semantic Concept Graph deliberately does not gate. It illuminates the neighbourhood of methods, literature and cross-disciplinary connections the researcher needs to weave together. Doctoral work is not "master this list" — and we don't pretend otherwise.
Long-run retention, not short-run wins
FSRS spaced repetition schedules review just before you're about to forget — measured per concept per learner. If a concept doesn't stick past exam day, it wasn't understood. The whole graph is designed to compound over years, not decay after a term.
What's been tried before us
Adaptive learning has forty years of serious experiments. Most reached ~½σ. None have closed 2σ. We stand on that shoulder and try to go further.
We are not claiming to have invented adaptive learning. It is a mature field with a real track record — and a real ceiling that has held for decades. What is new is the combination the last few years have made possible: knowledge-graph density at the scale a full K→PhD curriculum needs, spaced-repetition schedulers that are genuinely per-learner (FSRS, shipped 2022), IRT that runs online, and frontier LLMs that can generate layered explanations and Socratic follow-ups on demand. Those pieces did not exist together before. That is what makes this a fresh experiment worth running.
| Programme | Era | What it tried | Effect / outcome |
|---|---|---|---|
| Bloom's original studies | 1984 | 1:1 human mastery tutoring vs classroom | The 2σ result — the ceiling everyone since has been chasing |
| Carnegie Learning · Cognitive Tutor | 1990s → today | Model-tracing intelligent tutor for algebra, Pittsburgh schools | ~0.3–0.4σ gains in RCTs; strongest peer-reviewed adaptive-learning evidence at scale |
| ALEKS | 1990s → today | Bayesian knowledge-state assessment, university math placement | Widely deployed in US higher-ed; gains modest but reproducible |
| DreamBox Math | 2006 → today | K-8 adaptive math with strategy diagnosis | Positive but small effects in independent studies; content-limited |
| Knewton | 2011 → 2019 (sold, wound down) | White-label adaptive engine for university textbooks | Overpromised on personalisation; commercial failure; instructive on what "AI-powered adaptive" cannot substitute for structure |
| IBM Watson Classroom | 2016 → 2019 (killed) | AI teacher-assistant for K-12 | Discontinued after failing to demonstrate outcomes |
| Byju's | 2015 → 2024 (collapse) | Video-first adaptive app, India-scale | Content-first architecture without a real mastery graph; unit economics + outcomes both failed |
| Squirrel AI (China) | 2014 → today | Fine-grained knowledge-point mastery, 1:1 chain schools | Vendor-reported gains; independent replication scarce |
| Khan Academy · Khanmigo | 2023 → today | Content library + GPT-4 Socratic tutor | Content-first, adding conversational LLM; not knowledge-graph-first |
| Duolingo | 2012 → today | Well-tuned SRS + gamification, single domain | Strong retention mechanics; deliberately narrow scope |
The pattern is honest: adaptive engines built without a full-ladder knowledge graph plateau around ½σ. Engines with a graph but without human unblock hit the same ceiling for a different reason. Nobody has combined all three — dense graph, calibrated adaptive loop, real-human unblock on the residual — at population scale, in a curriculum aligned to a specific country's exam boards, in a language other than English. That is what we are testing.
How we differ
Content-first, LMS, and crammer are the three shapes the market has settled into. None of them is what ALE is.
| Category | Example | What it optimises for | How ALE differs |
|---|---|---|---|
| Content library | Khan Academy · Coursera · edX | Availability of good videos | Graph-first not video-first; every concept is a node in a mastery-gated DAG aligned to CBC / KCSE / IGCSE; layered explanations generated per-learner, not the same video for everyone |
| Language app | Duolingo · Pimsleur · Fluently AI | Vocabulary retention (Duolingo) · oral fluency via graduated audio drill (Pimsleur) · adaptive AI conversation with pronunciation + grammar feedback (Fluently, English only) | ALE incorporates all three mechanics — SRS + streaks (Duolingo), graduated audio anticipation drill (Pimsleur), real-time AI conversation with sound-by-sound pronunciation and grammar correction (Fluently) — and adds what none of them ship: KCSE-competent Kiswahili as L1 AND as a zero-to-hero L2 pathway for English speakers, full grammar (ngeli, tense/aspect, composition), the Kamusi dictionary integrated as a one-tap popover, a scientific-Kiswahili corpus for STEM in Kiswahili, and cross-curricular integration so Kiswahili sits inside the same learner's mastery record as their other 10 subjects |
| Video-first adaptive | Byju's · uLesson | Consumption metrics dressed as personalisation | Real IRT + FSRS running per learner, per concept; wrong answers produce Socratic follow-ups not more videos; mastery gating not passive viewing |
| Exam crammer | Zeraki · Elimu · PastPapers.co.ke | Past-paper coverage | Root-cause tracing across the DAG — a Form 3 fail traces back to the specific Form 1 gap and fixes it first; understanding transfers, past-paper drilling doesn't |
| LMS | Google Classroom · Moodle · Kenya Ed Cloud | Delivering content the teacher assigned | Not an LMS at all — plugs into one when the school has one; the adaptive loop runs per learner regardless of what the class is doing |
| Private 1:1 tutor | The historical way to close 2σ | Presence, per-child diagnosis, real Socratic dialogue | Algorithm carries the mastery load across every subject at once; human tutor is dispatched only when the loop identifies a genuine block — a fraction of tutor-hours, available on demand |
| Adaptive engine, English-only | ALEKS · Cognitive Tutor · DreamBox | US / European curricula | African curriculum built-in (CBC, KCSE, IGCSE-Kenyan-context); Kiswahili STEM authored not translated; MBChB / USMLE Step 1 for East African medical schools |
The moat is not one piece. It is the combination: dense knowledge graph + IRT + FSRS + layered LLM explanations + Socratic tutor + human unblock + African-curriculum alignment + Kiswahili STEM. Every competitor has some of the parts. Nobody has all of them wired together per learner.
K → PhD, with the right ontology per rung
The most important architectural decision in ALE is epistemological, not technical.
Convergent knowledge — arithmetic → algebra → calculus — has genuine prerequisites, and a mastery-gated Directed Acyclic Graph is the correct instrument. Postgraduate research is rhizomatic, connected by relational edges (is-methodology-for, theoretically-contradicts, serves-as-literature-for). A DAG gets the first right and forces the second into a shape it doesn't have.
| Rung | Knowledge structure | Engine | Adaptive mechanism |
|---|---|---|---|
| Class 1–6 (CBC) | Convergent, cumulative | DAG mastery loops | Gate on mastery evidence |
| Grade 7–9 (JS) | Convergent + subject depth | DAG mastery loops | Per-subject mastery path |
| Form 1–4 (KCSE / IGCSE) | Convergent, exam-scoped | DAG mastery loops | Prerequisite cascade + FSRS |
| Foundation undergrad | Bridging + mirror of secondary | DAG mastery loops | Root-cause remediation |
| Advanced undergrad | Convergent core + lateral | Hybrid DAG + Semantic Graph | Core gates + lateral suggestions |
| Postgraduate / PhD | Rhizomatic, relational | Semantic Concept Graph | Heuristic mapping, no gating |
A postgraduate doesn't need to "master chapter 7 before chapter 8." They need to see how instrumental variables relate to their identification strategy, which literature their design extends, and where their methodology sits in the neighbourhood of adjacent work. The Semantic Concept Graph illuminates. It doesn't gate.
Curriculum
Aligned to what your exam board actually sets.
CBC and KCSE for Kenyan learners, IGCSE for the international/private-school cohort, foundation-year undergraduate content for Kenyan universities. Kiswahili is first-class — not a translation layer, an authored curriculum for L1 speakers and a zero-to-hero L2 pathway for English speakers.
Mathematics
Grade 1 → KCSE → undergraduate → postgraduate mathematics
Physics
CBC → KCSE → undergraduate → postgraduate physics
Chemistry
CBC → KCSE Papers 1–3 → MBChB biochemistry
Biology
CBC → KCSE → MBChB physiology & Step 1
Geography
Physical, human, and economic geography
History
African, Kenyan, and world history
Medicine
MBChB Years 1–3 + USMLE Step 1 clinical vignettes
Engineering
Foundation → Da Vinci Engineering undergrad
Computer Science
Algorithms, data structures, first-year CS
Kiswahili
L1 mother-tongue + L2 pathway for English speakers
English
CBC → KCSE literature & language
Who it's for
The parent, the student, the school, the postgraduate.
Families
An 8-year-old can pick a username and PIN and start. A grown-up gets an email — no account required for children too young to have one.
Kids sign up →KCSE / IGCSE candidates
Every KCSE subject with mocks, mastery tracking, and an FSRS scheduler that plans backwards from your exam date. IGCSE core and extended syllabi supported.
Sign in →MBChB & undergraduates
Foundation-year remediation, clinical vignettes, engineering, CS. Depth without noise. Layered explanations for every concept.
Get started →Postgraduates
ALE Advanced runs on a citation graph — literature, methodology, and cross-domain synthesis. Not a mastery ladder.
Preview →ALE Assist · rolling out
When the software can't solve it, a real tutor is on it within the hour.
Every adaptive app claims "AI-powered personalisation." Parents have been burned by hollow AI claims. ALE Assist is the anti-cargo-cult signal: when the adaptive loop detects a learner stuck on a specific concept — three spaced sessions with full hints, no advance — it packages the stuck point as a scoped 45-minute micro-task and dispatches it to a vetted human tutor via Talente. The session runs in an embedded whiteboard; on completion, ALE resumes the loop from the unblocked node.
A fraction of learner time. A fraction of tutor-hours. This is what makes population- scale approach to Bloom's second sigma economically possible.
Contract
- 1Learner stagnation detected on a specific concept node
- 2Micro-task scoped: subject, node, prerequisite chain, 45-min session, success criterion
- 3Talente matches a vetted domain-relevant tutor within 60 minutes
- 4Session runs in embedded whiteboard + video widget
- 5On completion, adaptive loop resumes from the unblocked node
How we charge · why content is free
Content is a one-time cost. AI is a per-invocation cost. We split the bill the same way.
Every lesson, worked example, past paper, and Kamusi entry is authored once and hosted at near-zero marginal cost. Every diagnostic run, every adaptive question the model selects, every layered explanation and every tutor-chat turn burns real inference time on real GPUs — a cost per invocation that we cannot fake away. So we don't. Content is free. AI is metered, priced at what it actually costs us plus a modest routing margin.
Free with a signed-in account
Content — read as much as you want
- Every concept lesson across all 11 subjects, Grade 1 → undergrad
- Worked examples, misconception probes, layered explanations
- KCSE / IGCSE past papers with worked solutions
- Kamusi (Kiswahili dictionary) and cross-subject glossary
- Your own mastery record and exportable notes, forever
Metered — pay per invocation
AI — you buy the compute you use
- Adaptive diagnostic — full mastery map (≈ 20 credits per subject)
- Adaptive session — IRT-selected questions with hints (1 credit per turn)
- Socratic tutor chat and oral assessment (1 credit per turn)
- Layered explanation generated on demand for a wrong answer (2 credits)
- ALE Assist — a real human tutor unblocks you (1 Assist credit ≈ 45 min)
Why we require a signed-in account for content
The graph, the lessons, and the worked solutions cost real effort to author. Free content behind a sign-in is not paywall friction — it's the only way to keep the whole library free for actual learners while stopping competitors and scraper farms from siphoning years of authoring work in a weekend. Every account is rate-limited per session, every request is bound to a learner-ID, and there is no bulk-export endpoint. Learners can export their own notes and mastery record any time; nobody gets to export ours.
Plans
Every plan includes unlimited content. Plans differ only in the AI credits you get.
Unused credits roll over. Bolt-on credit packs available any time. If your first paid month doesn't move your mastery map on the re-test, we refund the month.
Free
KES 0
signed-in account
Unlimited content. 30 AI credits/month — enough for a full diagnostic in one subject.
Create free accountLearner Lite
KES 800/mo
single subject focus
Unlimited content. 300 AI credits/month for one subject's adaptive drilling.
Choose LiteRecommended
Learner
KES 1,500/mo
all subjects
Unlimited content. 800 AI credits/month + 2 ALE Assist human-tutor credits.
Choose LearnerFamily
KES 3,500/mo
up to 4 learners
Unlimited content. 2,500 pooled AI credits + 8 ALE Assist credits.
Choose FamilyCredit top-ups: KES 200 for 100 credits; KES 400 for one ALE Assist human-tutor session (45 min). No auto-billing, no surprise charges — the app shows the credit cost of every action before it runs. Institutions: per-learner metered plans with pooled credits and admin dashboards. Talk to us.
Free diagnostic
Forty-five minutes. A readable mastery map: per-topic scored, per-prerequisite gap flagged, per-concept next step named. More useful than most schools' end-of-term reports. Free even if you never subscribe.
Refund if no movement
The diagnostic re-runs after your first paid month. If it doesn't show measurable movement on the mastery map, the month is refunded. The risk is on us, not on you.
Your data, your map
Every concept you master and every gap you close is yours. Full export at any time. GDPR-aligned. Kenya Data Protection Act compliant. Never sold, never used for cross-platform profiling.
Start where you actually are
The specific gaps costing your child a grade,
mapped in 45 minutes.
Free diagnostic. No credit card. No app to install. Cancel any time — the mastery map is yours to keep.