Lesson Overview
In this lesson students put their project on a micro:bit to test it themselves and allow other students to test it. Through live testing with different people, students begin to discover how variation in the people using a model can reveal its limitations — and what it means for an AI system to be robust.
Subjects & Topics
- AI literacy: Human role in AI design, Testing ML models, Impact of AI
- Data literacy: Data bias
Key Learning
By the end of this lesson, students will be able to:
- Evaluate their ML (machine learning) model and code running on a micro:bit using live data from different people.
- Discuss how to make their ML model more robust by adding more data from different people.
- Understand that their project is an AI system that includes software, data, and hardware.