The personalized teaching layer for AI
It doesn't just answer.
It learns how you learn — and how to teach you next.
General models solved knowing the answer. Aptiz AI builds the personalized teaching layer on top — it works out who the student is, where they're stuck, and what to teach next.
| 01 | Knowledge Knowing the answer | Solved by LLMs |
| 02 | Teaching adaptation How to teach this student | Our layer |
| 03 | Learning state What to teach right now | Our layer |
01 · The problem
General AI waits for a good question — then stops at one answer.
Not the bottleneck
Generating knowledge
The real bottleneck
Deciding how to teach it
02 · What makes us different
| Ordinary AI tutor | Aptiz AI |
|---|---|
| Waits for the student to ask precisely | Infers the next step from work, errors & behavior |
| Generates one explanation | Generates several teaching moves and ranks them |
| Personalization stops at tone & examples | Personalizes the move, order, difficulty & hints |
| Chat ends = done | Must be verified by a new, delayed task |
| Logs the conversation | Maintains a correctable student state |
| Optimizes satisfaction / time-on-app | Optimizes mastery, transfer, memory, confidence |
03 · Live demo — try it
IllustrativeSame student, same error. Pick a teaching move — or let the system choose — then see why, verify it, and watch the model update. Switch students to see the same error get a different move.
Three candidate teaching moves
Why this method?
Verify → update
Watching an explanation isn't learning. Confirm mastery on a brand-new task.
New task
✓
Model update
04 · Why we can optimize teaching
Recording every learning trace is where AI beats any human — so we spend all our effort on teaching well.
A pile of chat logs isn't an advantage. Structured cause-and-effect — state → move → result → mastery — is. It compounds across students, subjects and regions: the more it's used, the better it gets.
05 · What counts as learning here
Step 1
Initial error
Step 2
Intervention
Step 3
Mastery on a new task
We accept one definition of success: unprompted transfer, still remembered later.
Not evidence of learning:
06 · Vision
For the first time, scale and personalization can happen together.
Scaling education used to mean standardizing it. AI breaks that trade-off — which reorganizes not a software category, but how every student understands, practices, gets feedback, and plans what's next.
i · Who decides where learning goes
Bus
Traditional school
Fixed route, time and group pace. Fall off the group's speed and you're left behind.
Carpool
Human tutor
More flexible, but bound by price, time and teacher supply — never everyone's infrastructure.
Private car
Personal AI tutor
The learner sets the destination and leaves anytime; the system adapts the route to them.
ii · How much the system drives — our roadmap
Phase 1 · Manual
Today's general AI
Powerful — but the student must judge what they don't know and what to do next.
Phase 2 · Automatic
Decides within a frame
The system chooses inside a human-designed teaching framework.
Phase 3 · Self-driving
Finds new methods
The system discovers teaching combinations no human wrote down.
iii · Equity = the market-expansion flywheel
Not a slogan bolted to the end — it comes straight from the cost structure. Lower marginal cost makes people who could never afford a tutor into users. Equity means the whole market gets bigger.
Cost falls
More students reachable
More learning evidence
Better adaptation
iv · The market, bottom-up
Entry
Paid personalized help
High-intent, well-defined tutoring & exam prep.
Expansion
A subscription product
High-frequency practice, explanation & planning.
Endgame
Every student's learning layer
The core interaction layer of next-gen education.
Mission
The help a child gets shouldn't depend on income, postcode, or luck with a teacher.
We don't promise to erase every gap. We aim to lower how much high-quality, personalized teaching depends on family income, region and teacher supply.
Early customers
Work with us
A limited Founding Pilot — a personalized AI learning agent with human quality calibration.
Explore the pilot →07 · Investors
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