Unaided first, then with AI
Learners attempt the problem alone, then bring AI in. Assessment is always unaided transfer, because access alone does not produce learning.
PAWANAX ACADEMY · Founding cohort waitlist open
A university for theage after answers.
When answers are free, judgment is the curriculum. We teach people to build, test, ship and answer for intelligent systems, with AI faculty to teach and humans to sign off.
- 9Schools
- 46Lessons
- 5Ladder levels
- Alwaysunaided assessment
Thesis
What is a university when answers are free?
Content is free. Judgment is scarce.
When answers are free, judgment is the curriculum.
The lab is the campus.
Learners build, test and ship real systems. Faculty guide the work and humans sign it off.
Art and human connection are the differentiator.
Our betA bet, not a measured result. We label it as one.
No single best student. We keep a proof ledger.
7 measured outputs per learner. No public rank.
Then → Now
- ThenLectureNowLab
- ThenSeat timeNowProof
- ThenPublic rankNowHouse
- ThenExamNowOral review by voice
- ThenAnswer keyNowHint ladder
How we teach
A hint ladder, never the answer
Faculty agents escalate from a nudge to a worked example. On graded work they withhold final answers.
Agents teach, humans decide
Every grade, credential and mastery claim is signed off by a named human Dean.
Proof, not seat time
You advance by shipping artefacts a peer can reproduce, not by hours watched.
Real problems from real engagements
Field studios use problems from live consultancy work, with client data handled under agreed consent.
Together, not ranked
Teams called houses pool progress. You see personal bests, not a public league table.
The ladder
Five levels. Each one proven.
Completes
Proof
A documented ten-task AI-assisted workflow with an error log.
Completes
Proof
A tool-using assistant that passes a 20-case eval set at a threshold you set in advance.
Completes
Proof
An agent serving real users for 30 days, with traces and an incident record.
Completes
Proof
A gated multi-agent system that beats a baseline on a pre-registered eval, reproduced by a peer. You mentor one Builder.
Completes
Proof
A standard adopted by two outside teams, plus an audit of another team's system that finds and fixes real faults.
Evidence
What the research says — and what we built because of it
Some of these results are early or limited. We say so, and we design around them.
-17%
Students who practised with a plain AI tutor scored 17% lower on later unaided exams. A tutor built around hints removed the harm.
Bastani et al., PNAS 2025Shapes our design: Unaided first. Hint ladder.
#1
Workers rank quality control of AI output as the top skill, ahead of critical thinking.
Microsoft Work Trend Index 2026Shapes our design: School 4, Evals and Verification.
n=194
A purpose-built, scaffolded AI tutor beat an in-class active-learning lesson in a Harvard physics trial. One course, so we treat it as promising, not proven.
Kestin et al., Scientific Reports 2025Shapes our design: Design the tutor; the model alone is not the ingredient.
15%
In a 2026 study, an AI math tutor showed no gain over practice alone, and only 15% of students with access used it.
Hechinger Report, 2026Shapes our design: Tutors alone do not drive engagement. Cohorts and humans do.
28%
Job postings that ask for AI skills carry a 28% salary premium.
Lightcast, 2025Shapes our design: Why institutions pay for the Ladder.
33%
Only a third of workers received employer-provided AI training in the past six months.
The Conference Board, 2026Shapes our design: The gap institutions have today.
Faculty
Meet your AI faculty
Every message is labelled as AI. Named humans sign off on every grade.
Professor Ndovu
Scripted sample, not a live chat
- You
Why does my agent keep repeating the same tool call?
- Tool call: Retrieved lesson 3.1, section on loop termination
- AI · Dean-reviewed sample
Good question to ask. What does your loop check after each tool result, and what would tell it to stop?
Guides you with questions and a hint ladder, one per course.
ToolsCourse retrievalYour learner profile (read)Guardrail Never gives final answers on graded work.
Agents teach. Humans decide.
Dean
Owns the curriculum, handles grading appeals and signs off every grade and credential.
Steward
Handles safety escalations, child protection and data-subject requests.
Faculty lead
Owns the quality of one course and reviews a weekly sample of tutor transcripts.
Proof Ledger
No single best student. A proof ledger.
Nobody is ranked against a classmate. Each learner builds a ledger of measured outputs, and a profile across them shows where the work is strong and where it is not yet.
1Eval delta2Shipped impact3Catch rate4Calibration5Orchestration yield6Reproducibility7Judgment under ambiguity
Example profile — illustration
Tap a metric to see what it measures and to highlight its axis.
Our bet
Not everything is a benchmark. Some of it is a song.
AI makes the first draft cheap. Taste decides what is worth finishing. Schools 6 and 9 teach critique, narrative and ethics, and then put a camera, an instrument or a game engine in your hands, with AI as your crew and you as the director. We believe taste and human connection will matter more as machines get better at answers. That is a belief, not a measured result, and we will keep testing it.
Houses
Learn together, not ranked.
Illustration — not live data
Each dot stands for one learner. The ringed dot in each house marks a place you could be here. Tap a house to focus it.
Tap a house to see what it means.
Together, not ranked
Teams called houses pool progress. You see personal bests, not a public league table.
- Pooled progress toward a shared goal.
- Personal bests, never a public league table.
- One agent peer per study pod.
Open Lab
The lab is open.
Learners work on real artefacts, not exercises made up for a course. Here are three you can run today.
45
parameters
Scenario model
Five conditional scenarios across 45 named parameters. Run it yourself.
scenario_model.pyOpen Scenario model (opens in a new tab)5
scenarios
Model results
Every scenario and sensitivity as data, with the evidence boundary written into the file.
model-results.jsonOpen Model results (opens in a new tab)363
source URLs
Methodology
What the model can say, and what it cannot. The inventory holds 382 dated claim lines and 363 distinct URLs, and it is an inventory, not 382 established facts.
methodology.mdOpen Methodology (opens in a new tab)
python scenario_model.py“These are conditional calculations, with no scenario probabilities or fitted causal coefficients.”
The curriculum
Nine schools. Forty-six lessons.
Not a pile of videos. Each lesson ends in an artefact you can show a peer, and each school feeds the same five-level ladder.
S1Model Literacy5 lessons · 17 h
S2Tools and Context5 lessons · 23 h
S3Agent Orchestration5 lessons · 23 h
S4Evals and Verification5 lessons · 25 h
S5Domain Judgment5 lessons · 24 h
S6Taste and Expression5 lessons · 18 h
S7Science and Method5 lessons · 25 h
S8Governance and Civic5 lessons · 58 h
S9Creative Studio6 lessons · 49 h
The market
Why now
AI-powered corporate training
20267.49
203118.19
USD billions
Source: Mordor Intelligence
Market estimates vary by definition. These are two analyst series, shown as planning bands, not forecasts we endorse.
AI certification courses
20264.72
203112.36
USD billions
Source: Mordor Intelligence
Market estimates vary by definition. These are two analyst series, shown as planning bands, not forecasts we endorse.
The big labs now give AI literacy away free. What is scarce is verified judgment, supervised practice and sector-specific proof.
For institutions
We already deliver the engagements. Now we teach them.
We already deliver these engagements. Each one becomes a cohort package: same method, same artefacts, now taught.
Founding cohort waitlist open
Be in the founding cohort.
Tell us where you would like to start. We will write with dates and a seat. Joining is free.
Prefer to talk? Message us on WhatsApp.
Founding cohort
Join the waitlist.
Tell us where you would like to start. We will write when dates and seats are set.
Questions
Honest answers.
We are opening a founding cohort. Join the waitlist and we will write to you with dates and a seat. Nothing is charged to join.
AI faculty teach and coach, and every message says so. Named humans, our Deans, sign off on every grade and credential. A human mentor is one tap away.
That is the risk we designed against. You work unaided first, faculty give hints rather than answers on graded work, and assessment is always unaided.
No. The Literate level and School 6, Taste and Expression, need none. The Builder and Orchestrator levels do involve building, and we teach it.
Yes. School 9, Creative Studio, has six lessons: directing film, composing music, designing games, worldbuilding, rights and credit, and releasing a work. You direct and decide; AI is your crew. You finish with something real that an audience has seen or heard.
A team of learners with shared progress toward a goal. You see your own personal best, and no public league table of individuals.
Free courses teach tools well. We teach evals, domain judgment and experimental method, and we certify proof you can show a peer, not hours watched.
Through six cohort packages, scoped in writing. Start with a Discovery conversation and we will map a package to your people.
We collect only what a waitlist needs. Learner profiles are visible and correctable by you, and children's data requires guardian consent under Kenya's Data Protection Act.