Over the last several years, BirdBrain Technologies has created a growing collection of classroom-ready lessons that focus on practical and creative applications of Artificial Intelligence. Our focus with these lessons has been two-fold:

Help students understand how Machine Learning works, including data collection, the fragility of models when they encounter unfamiliar inputs, and the inherently statistical nature of their predictions.
Show students how to use AI models as intelligent sensors that can drive robot behavior—so robots can recognize and respond to patterns in images, sounds, and movement.

Our suite of lessons allows you to build and explore models that recognize objects, speech, or gesture, then use those models to control Snap! games or physical robots. These activities variously work in Snap! and MakeCode, with micro:bits, Finch robots, Hummingbird projects, and other micro:bit enabled robots.

In this two-hour workshop, we’ll give an overview of all eight lessons—what each one focuses on, the grade bands they best support, and what tools and materials you’ll need to run them in your classroom. Next, we’ll go hands-on with two model-building tools, Google Teachable Machine and Create AI, and show you how to connect the models you create to a micro:bit to control physical devices.

You’re welcome to treat this as a watch-and-learn session, or follow along hands-on. To participate interactively, you’ll need at least one micro:bit v2. A Finch or Hummingbird robot is helpful but not required. You can request a demo from BirdBrain Technologies of either Finch or Hummingbird, but note that demo requests typically take 4-6 weeks to process.

The Zoom session will be recorded. All registered participants will have access to the recording for post-workshop review. If you can’t attend the live session, you may register anyway to have access to the recording. Not the same as being there, but valuable nonetheless.

About the workshop leader:

Tom Lauwers founded BirdBrain Technologies in 2010 after receiving his doctorate in robotics from Carnegie Mellon University. His research was founded on engaging all students, regardless of background, in robotics and engineering. Tom seeks to design educational tools that catalyze positive making, coding, and engineering learning experiences in the classroom. Tom resides in Pittsburgh’s Squirrel Hill neighborhood with his wife, two kids, two birds, and a small army of robots. He would be an invaluable ally in the event of a robot uprising.

Contact the host of A Journey Through Eight Practical AI Lessons