One powerful way is to connect AI concepts to real-world tools students already know—like fitness trackers. Many students use technology like fitness trackers every day, but they rarely question how these tools actually work or whether they work fairly for everyone.

How can teachers make learning about AI meaningful and relevant for students? View our new lesson to try for yourself with your students. View Lesson

Connecting Classroom Learning to Real-World Experience

This new Forward Education course uses the micro:bit V2, the CHARGE Battery Pack, and Create AI to help students explore how fitness trackers work and whether they are fair. Through hands-on learning, students build, test, and improve their own AI models while thinking critically about bias, data, and inclusive design.

Students collect movement data, train an AI model, and test how accurately it recognizes different activities. Students will see firsthand how AI makes decisions based on data and explore what happens when the data is limited or biased. By testing and improving their models, students experience the full process of iterative design in a real-world context.

Building AI Literacy Skills

Teaching AI through real-world tools helps students understand how machines learn and make decisions and at the same time students build critical thinking skills and question fairness, accuracy, and bias in technology and AI.

This kind of authentic, real-world learning helps students see themselves as creators in computer science, not just users of technology. This also reinforces the importance of keeping humans at the center when designing future technologies.

Why Use Fitness Trackers to Teach AI?

Fitness trackers are familiar to many students. They count steps, track movement, and measure activity but an important question that often goes unasked is whether this kind of technology is equitable and inclusive?

Real-world example:

• A fitness tracker might count steps differently for different body types or movement styles

• Activities like dancing, wheelchair movement, or slow walking may not be recognized accurately

• AI models can reflect bias based on the data they were trained on

This makes this 45 minute fitness tracker lesson the perfect entry point for discussing fairness in AI systems, and this lesson has minimal setup and beginner-friendly entry points.

Try out this activity in your classroom tomorrow! https://learn.forwardedu.com/course/fitness-tracker-fairness-pe-math-ai/