Build a Teachable Machine Image Classifier With Your Kid

Help your child build, train, and test their first computer vision model in under twenty minutes.

Most children interact with artificial intelligence as passive consumers. They watch recommended videos, ask voice assistants to play music, or use filters on social media.

To prepare the next generation for an AI-driven world, we must shift them from consumers to creators. Demystifying how computers learn does not require complex coding or expensive software. Using Google's free Teachable Machine platform, children aged eight to sixteen can build a fully functional computer vision model. This hands-on project allows them to train a neural network to recognize physical objects, hand gestures, or facial expressions, providing an intuitive understanding of how modern AI systems perceive the physical world.

Step 1: Set Up Your AI Laboratory

To begin, you only need a computer with a working webcam and an internet connection. No account creation, software installation, or payment is required.

Open your web browser and navigate directly to https://teachablemachine.withgoogle.com/ . Click on the "Get Started" button, and select "Image Project" followed by "Standard Image Model".

Before you start clicking, decide together what your model will classify. A classic, highly engaging option is a "Healthy vs. Unhealthy Snack" classifier, or a "Rock, Paper, Scissors" game helper. For this guide, we will build a classifier that distinguishes between a coffee mug and a writing pen.

Step 2: Gather Your Training Data

An AI model is only as good as the data used to train it. On the screen, you will see two default classes: "Class 1" and "Class 2".

• Rename the Classes: Click the pencil icon next to "Class 1" and rename it "Mug". Rename "Class 2" to "Pen".
• Capture Mug Images: Click the "Webcam" button under the Mug class. Hold your coffee mug in front of the camera. Click and hold the "Hold to Record" button. Have your child slowly rotate the mug, move it closer to the camera, and then further away. Capture at least one hundred images. This teaches the AI what a mug looks like from different angles.