Home / Courses / Decoding AI: Machine Learning for Kids

Decoding AI: Machine Learning for Kids

A hands-on introduction to artificial intelligence and machine learning. Through interactive lessons and real-world problem solving, kids learn what AI is, how machine learning works, and how to use AI responsibly. Prompting is only a small part of it — understanding the logic behind AI and how to innovate with it matters far more for the future.

Enroll in This Course
Grades
4th–5th grade
Tuition
$50
per class
Billed per semester, by the number of classes it holds
Format
1.5 hours weekly
after school bell
Instructor
Computer Science & Data Science major
Students working together on an AI classroom activity Students building a hands-on AI logic project Students exploring machine learning concepts together

Who Is This For?

This course is designed for curious 4th–5th graders who are interested in learning more about technology and innovation. No coding experience or prior knowledge of AI is required.

Curious Minds

They aren't satisfied with "the computer just knows." This is the class where they finally get a real answer, and get to build the thing themselves.

Future-Ready Learners

They'll be a generation to learn and choose a career around AI. Understanding how the tools think is a different skill from using them.

Builders & Problem-Solvers

They like building things: games, stories, inventions. They'll finish the year having trained a working model and designed a game around its logic — learning how to break down ambiguous problems into logical steps.

What Will My Child Learn?

By the end of this course, students will be able to:

  • Explain what AI actually is and how it works.
  • Use AI responsibly as a tool.
  • Guide AI conversations and flag incorrect information, under supervision.
  • Understand how machines learn and process information through data and patterns.
  • Train a simple classifier and explain why it predicts what it predicts.
  • Build their own simple AI tools.
  • Apply their knowledge of machine learning in daily life to boost problem-solving and creativity.

How we use AI tools in class

What the students use directly are tools built for classrooms and appropriate for this age: browser-based training tools that run on the device, drawing and pattern games, and our own unplugged activities with cards, paper and physical models. Most of this course happens away from a screen.

When we look at a general-purpose AI tool, the instructor drives on one shared screen. Students decide what to ask, watch what comes back, and critique it together.

Nothing personal goes in. No student names, photos, schoolwork or personal details are entered into any AI tool. The data students collect and train on is classroom data — favorite snacks, shoe colors, that kind of thing.

You'll get the full list of tools we use before the class starts, and we'll ask students to bring their own device for some lessons.

Course Curriculum

Unit 1The Logic Behind AI
•What Is AI?
Following instructions vs. thinking for yourself — the logic behind "smart" machines
•AI All Around Us
Spotting AI in daily life and the patterns it's trained to notice
•What Is an Algorithm
Write and debug algorithms to solve problems and understand how computers follow rules
•Data & Pattern Recognition
Simple coded messages, then spotting patterns in real data
•Your First Dataset
The class collects data about itself — shoe size, snack preferences, walk-or-ride — and builds a clean table, then runs simple analysis in Google Sheets or CODAP
Unit 2Teaching AI to Classify
•Building a Decision Tree
A physical flowchart for everyday decisions
•Features and Labels
Breaking a thing into traits, the way a model needs labeled features
•Grouping by Similarity
Sorting classmates and objects by what they have in common
•Hand-Labeling the Data
Labeling examples by hand and watching how those human choices shape the output
•Unit Capstone: Classroom Recommender
Raw classroom data to a working recommendation tool
•Who's Missing From the Data?
Testing the recommender on classmates who weren't in the original survey, watching it miss, and working out why
Unit 3How Machines Learn
•How Humans vs. Machines Learn
Supervised learning — humans and machines both checking against a known answer
•Learning Without an Answer Key
Sorting with no instructions given: unsupervised learning
•AI Capabilities
Rewarding good guesses to reveal AI strengths and blind spots
•Training Your Own Model
Each student trains an image or gesture classifier on examples they create in the room
•Breaking Your Model On Purpose
Too few examples, all examples from one kid, two categories that look alike — learning what makes a model fail
Unit 4Working with AI
•Generative AI, Part 1 — Effective Prompting
How to communicate with AI effectively, on the instructor's screen
•Generative AI, Part 2 — Give AI a Job, Push Back, Validate
Role assignment, targeted refinement and validation, on the instructor's screen
•Project Studio
Student picks one project and applies prompting skills, on the instructor's screen
•Chapter Debrief
When to use AI, and how to take what it gives you
Unit 5Judgment & Ethics
•Garbage In, Garbage Out
What a model produces when the input is messy, biased or thin
•What AI Costs to Run
The electricity, water, hardware and infrastructure behind AI systems
•What AI Can't Do
What parts of planning AI genuinely can't do
•Safety AI Guide
Bias and fairness, in kid-friendly terms
•AI Validation — Spotting Fakes
Spotting AI-generated fakes: a media literacy checklist
Unit 6Capstone & Showcase
•AI Technologies & Inventions
Solving real-world challenges with AI, and how it's used across industries
•AI Future Jobs
Researching future AI-related jobs
•Capstone Build
Designing a game whose rules encode what they've learned, and retraining their model for demo day
•Showcase Night
Families come in. Each student demos their model, explains one thing it gets wrong and why, and plays their game

Ready to enroll?

Enroll in Decoding AI: Machine Learning for Kids