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Decoding the Logic of AI & Machine Learning

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

Enroll in This Course
Age Groups
1st–5th grade
Tuition
$180/month
Sep–May
Start Date
September 2026
Schedule
Right after the school bell (varies by school)
Instructor
AI & Data Science professional or related college study

We partner with your school to use a classroom right after the bell — your child walks straight from their school day into class. The exact schedule varies by school and will be shared through your school's flyer and in the registration form.

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 3rd–5th graders who are interested in learning more about technology and innovation. No coding experience or prior knowledge of AI is required.

Curious Minds

Are curious about how AI actually works and how to innovate with it.

Future-Ready Learners

Want practical skills that will support their future STEM education.

Builders & Problem-Solvers

Are interested in creativity, problem-solving, or coding.

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.
  • Understand how machines learn and process information through data and patterns.
  • Build their own simple AI tools.
  • Apply their knowledge of technology in daily life to boost problem-solving and creativity.

Course Curriculum

28 lessons across 5 units, one school year (September–May).

Unit 1Code Crackers: The Logic Behind AI
1.1What Is AI?
Following instructions vs. thinking for yourself — the logic behind "smart" machines
1.2AI All Around Us
Spotting AI in daily life and the patterns it's trained to notice
1.3The Recipe Robot, Part 1: Writing Bulletproof Instructions
Writing a literal, step-by-step algorithm; input vs. output
1.4The Recipe Robot, Part 2: Debugging With And/Or/Not
Testing and fixing a partner's algorithm using simple logic rules
1.5Data & Pattern Recognition
Simple coded messages, then spotting patterns in real data
Unit 2Sorting Smarties: Teaching AI to Classify
2.1Care for Your Pet, Part 1: Building a Decision Tree
A physical flowchart for pet-care decisions
2.2Care for Your Pet, Part 2: Refining Your Classifier
Breaking needs into traits, the way AI needs labeled features
2.3Recommendation AI, Part 1: Grouping by Similarity
Grouping items by how "close" they are in traits
2.4Recommendation AI, Part 2: Who Trains the Trainer?
Hand-labeling examples to see how human input shapes AI output
2.5Recommendation AI, Part 3: Build Your Own Sorter
Unit capstone — from raw classroom data to a working recommendation tool
Unit 3Training the Digital Puppy: How Machines Learn
3.1How Humans vs. Machines Learn
Checking answers against a known "answer key"
3.2Machine Learning
Sorting without instructions, and learning through trial and error
3.3AI Capabilities
Rewarding good guesses to reveal AI strengths and blind spots
3.4Plan a Vacation, Part 1: Training AI With Your Preferences
Feeding preferences into an AI travel planner
3.5Plan a Vacation, Part 2: Course-Correcting Your AI Travel Agent
Unit capstone — revising a plan based on AI suggestions
Unit 4Super Brains & Big Data: Deep Learning in Action
4.1AI Image Processing
How AI reads an image in stages — lines, curves, shapes
4.2AI and Music
How information passes through a neural network
4.3Visual Design With AI
Exploring an AI art studio
4.4AI Support With Writing, Part 1: Predictive Text
Finish-the-sentence games and how predictive text works
4.5AI Support With Writing, Part 2
How much text and data we each generate day to day
4.6AI Support With Writing, Part 3
What happens when AI is fed messy or low-quality input
4.7AI Help With Presentations
Unit capstone — combining image, writing, and design help into one project
Unit 5Tech, Truth & Teamwork: AI Ethics + Final Capstone
5.1Safety AI Guide
Bias and fairness, in kid-friendly terms
5.2Plan a Birthday Party, Part 1
What parts of planning AI genuinely can't do
5.3Plan a Birthday Party, Part 2
Spotting AI-generated fakes — a media literacy checklist
5.4Science Fair Project Ideas, Part 1
Researching a future AI-related job or invention
5.5Science Fair Project Ideas, Part 2
Finalizing a project through a fairness and ethics lens
5.6Planning Your Game — Final Capstone
Designing the logic rules for a video game, pulling together everything learned

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