Intro to AI

Learn the core AI skills — and finish by building a small app of your own.

Track your progress. Sign in to save lesson completion and scores, submit your capstone, and earn a certificate. Teachers can create a class and watch student progress. Open the portal →

This track progresses from understanding what an AI assistant is to building and deploying a small application with one. The lessons teach skills that apply to any AI tool — Claude, ChatGPT, Gemini, and others. A hands-on capstone then applies them: you will build and deploy a small interactive application from start to finish.

Short, hands-on lessons (a few minutes of reading each) plus the capstone project. No coding background assumed.

Lessons

Lesson 1

What AI Assistants Actually Are

Hands-on: work through interactive activities to understand how large language models operate — what Claude, ChatGPT, Gemini, and Copilot have in common, and where their limitations lie.

Lesson 2

How AI Predicts the Next Word

Hands-on: the same blank in two different sentences, and why the most plausible word changes. An introduction to conditional probability — the probability of an outcome given known information — which underlies every response an AI produces.

Lesson 2b

How Machines Learn to Classify

Interactive: turn examples into dots on a grid, move the decision boundary, and classify new emails as spam or legitimate — step by step, with 5 hands-on activities.

Lesson 3

Working with an AI Assistant

Hands-on: how to direct an AI on a real project — giving context, scoping tasks, pausing before risky changes, and checking the result against what the AI claims.

Lesson 4

Prompting Well and Checking the Output

Hands-on: the two skills that matter most — writing a clear prompt, and never trusting an answer you have not verified.

Lesson 5

From Idea to a Working MVP

Hands-on: how a small application develops from an initial idea into a functioning product — defining the smallest useful version, planning the steps, building, testing, and iterating.

Capstone project

Build it

Build a Concept Manipulative

Apply the complete skill set: build a small single-page interactive application that explains one concept of your choice, taking it through the full lifecycle — idea, MVP, build, test, deploy.

Use AI as a collaborator, not an authoritative source. These tools draft quickly and explain clearly, but they also produce confident errors. Your role is to direct the work and verify it — and, by the capstone, to deploy a functioning application built with it.

Next course → Math Foundations for AI — eight interactive modules on the math behind machine learning: notation, probability and Bayes, vectors and matrices, gradient descent, and statistics, each tied to how real models work. No STEM degree required.

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