How do you make Newton’s Third Law feel relevant to a generation raised on TikTok and Minecraft? You turn them into crash test engineers.
Our latest webinar, Crash Test Engineers, moves beyond the whiteboard to show educators how to integrate Artificial Intelligence (AI) into the science classroom. By combining the micro:bit V2, the CHARGE battery pack, and the Create AI platform, we’ve developed a high-impact workshop that makes abstract physics concepts tangible, visible, and fun.
Integrating AI Literacy in Physics with micro:bit
In most classrooms, AI is discussed as a theoretical concept or a writing tool. In this webinar, we reframe Machine Learning (ML) as a scientific instrument.
Using the micro:bit’s onboard accelerometer, students collect real-world data from physical impacts. They aren't just "using" an app; they are building a model that can distinguish between a hard crash (high peak acceleration) and a soft landing (dampened force).
Key Takeaways from the Workshop:
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Data Literacy: Students learn to identify "clean" data versus outliers in their accelerometer graphs.
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Newton’s Third Law in Action: Visualizing how different surfaces change the duration and intensity of impact forces.
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The Engineering Design Process: Using AI feedback to iteratively improve the design of "soft landing" zones.
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Addressing AI Bias: Discussing why a model trained on a 5-inch drop might fail during a 3-foot drop.
AI Literacy Tools: micro:bit V2 + CHARGE Battery Pack
A successful STEAM workshop requires hardware that can keep up with active students.
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micro:bit V2: The brain of the operation, providing the high-frequency accelerometer data needed for accurate ML training.
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CHARGE Battery Pack: A rugged, rechargeable power source that allows the micro:bit to be dropped, tossed, and moved without being tethered to a computer. Its mounting bricks make it easy to attach to "crash cars" or experimental builds.
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Create AI: A user-friendly browser interface that allows students to train and test models via Bluetooth without writing a single line of code (but ready for code when the students are)
From Data Collection to "Smart" Logic
The magic happens when students export their trained AI model into Microsoft MakeCode.
Once the model "knows" what a dangerous crash looks like, students can program the micro:bit to react. Imagine a classroom where every student’s device flashes a red "X" upon a hard impact but displays a "Checkmark" for a safe landing. This transition from Data → Model → Logic provides a complete picture of how modern technology works.
Pro-Tip for Educators: Use the "Confidence" threshold in Create AI to talk about error margins. If the AI is only 60% sure, should we trust the result?
Bringing the Webinar to Your School
This workshop is designed to meet NGSS and CSTA standards, making it a perfect fit for middle and high school science or computer science curriculums. By the end of the session, educators leave with a repeatable framework for teaching motion, forces, and the ethics of data.
Ready to level up your STEAM curriculum?
Whether you're a seasoned coding instructor or a science teacher new to AI, the Crash Test Engineers framework provides a low-barrier, high-engagement entry point into the future of education.
