Session 1: The Money Machine
A company is a machine: things go in, something comes out, money comes back. And an AI can sound completely certain and still be wrong.
App built that day: Money Machine
Interest, stocks and index funds — and every session, kids build a small money app with AI to prove they understood the idea.
Biweekly online sessions beginning Spring 2027, taught in English. Each 75-minute class starts with a 30-minute lecture, followed by 45 minutes of vibe coding. Pretend money only. No coding experience needed.
Kids work out a compound interest table with a pencil. Then they describe a calculator in plain English and the AI builds it. Then the app and the paper disagree — and hunting that bug is where the idea finally lands.
Every idea is worked out by hand first. The worksheet becomes the test the app has to pass, which is why kids can tell when the computer is wrong.
Fourteen small apps, each one built during a 45-minute vibe-coding lab. The later ones reuse the earlier ones — their code compounds too.
It gets facts wrong, it gets arithmetic wrong, and it writes buggy code. Catching those mistakes is celebrated and discussed together online.
Practice mini-apps vibe-coded with AI during the online class.
Three sessions of research on one real public company, with sources and dates the kid checked.
Pretend money split three ways, with a written reason defending every choice.
A two-minute pitch app, driven by the kid, presented at the family showcase.
Taught in English: the first 30 minutes are lecture, then the remaining 45 minutes are vibe coding.
A broken app still demos. The student shows it, says what it was meant to do and names the bug — and that counts fully.
Eight units, one money idea at a time, each one ending in something the kids got a computer to do.
A company is a machine: things go in, something comes out, money comes back. And an AI can sound completely certain and still be wrong.
App built that day: Money Machine
The difference between what something costs to make and what it sells for, what is left over, and what you gave up by choosing.
App built that day: Leftover Calculator
Interest is rent for money. Build the table by hand and see why every year adds the same amount.
App built that day: Interest Table Maker
Why year two grows more than year one, why waiting longer matters more than starting big, and a gentle look at prices rising over many years.
App built that day: Snowball Compounder
Two good reasons people borrow, why paying back is always bigger, what makes fees run away, and credit versus debit. Every story is about pretend characters and pretend shops.
App built that day: Runaway Fee Simulator
What a share is — and what it definitely is not. Nobody gets to walk into a store and take things.
App built that day: Pizza Slice Explorer
Small and promised next to bouncy and not promised, compared honestly in both directions. Neither one is "the good one."
App built that day: Two Envelopes
Pick one well-known public company and work out what it sells, who uses it, and who actually pays — which is often not the same person.
App built that day: Who Pays? Sorter
Explain your company in three sentences, then back two facts with a source and a date you found yourself.
App built that day: Engine Explainer
Compare your company with a rival fairly — including the row where your company loses — and say what owning one slice of it would actually mean.
App built that day: Head-to-Head Card
What people mean by a fund that follows a long list of big companies, in kid words, with no product or provider named.
App built that day: Basket Builder
The honest trade. A basket brings fewer surprises from any one company — and you give something up for that. Fewer surprises is not the same as safe.
App built that day: Stress Test Machine
Split a pretend $1,000 across savings, one company, and a basket — and write a real reason for every single choice. Nobody wins; there is no scoreboard.
App built that day: Three-Sleeve Board
Two minutes, no reading off a page, driving your own app — with your verdict clearly stamped as an opinion.
App built that day: My Money Plan
A 90-minute online evening: students share their apps, give their two-minute My Money Plan pitches, revisit fourteen sessions of AI mistakes they caught, and receive their badges. The not-investment-advice statement is read aloud at the start.
Students describe and vibe-code each practice mini-app with AI. They all carry the same banner on screen: pretend money — practice numbers only.
Press the button to sell one thing you make and watch "money in" climb.
Price, cost and how many sold go in; money in, money out and the leftover come out.
Prints a year-by-year table where the same amount of interest arrives every year.
Simple and compound side by side, so the gap opens up in front of you.
A pretend fee repeats every "two weeks" until the fees pass what was borrowed.
Slice the pizza, hold one slice, and see what that does and does not let you do.
A savings envelope and a share envelope, moved by pretend wiggles the kids typed in.
Tag who uses each product and who pays for it, and the app flags every mismatch.
A three-screen explainer with a Verified badge that only lights once sources are checked.
Five rows comparing your company to a rival, including the row where yours loses.
Add companies and a pretend amount splits equally into crumbs across all of them.
Draw a pretend event card and count how many of twelve companies it hits.
A pretend $1,000 across three sleeves; no sleeve locks without a typed reason.
A seven-screen pitch app that opens the earlier apps as live demos, ending on an OPINION stamp.
Kids will use Google AI Studio or similar tools; the final tools are still to be decided. Parents or adults supervise access on each kid's own laptop, with no child accounts.
This is a financial-literacy and AI-literacy class for children. It is education, not advice — and the guardrails are built into how the class runs, not just into what we say.
Money Smarts with AI is a financial-literacy and AI-literacy education program for children. It is not investment advice, and nothing in it is a recommendation to buy, sell, or hold any security, fund, or financial product. Students learn how interest, borrowing, stocks, and index funds work using pretend money and round, deliberately unrealistic practice numbers. We never name or recommend specific funds, banks, or lenders. Students research publicly available information about well-known public companies and build pretend paper portfolios. No real money, brokerage account, or trading platform is used at any point. We do not teach, model, or permit price prediction. Students also vibe-code small practice apps with AI on their own laptops under parent or adult supervision. The class may use Google AI Studio or similar tools; final tools are still to be decided. The apps use no live prices or market data, and students do not create child accounts or enter personal or financial information.
These are the rules students follow throughout every online session.
Nice to have first
AI Basics for Kids is the natural feeder, but the first session covers the essentials for anyone arriving fresh.
Money Smarts graduates arrive at Coding with AI having shipped fourteen small apps and debugged AI-written code. It is the gentlest on-ramp to building software we run, because nobody is asked to learn syntax first.
Tell us who is interested and we'll share enrollment details when they are ready. This is an interest list, not enrollment.