Train Your First Vision AI in 10 Minutes: Google Teachable Machine for Teachers!

Train your first computer vision AI model in under 10 minutes right in the browser with zero coding! In this episode of AI Classroom Copilot, we show K-12 STEM and computer science educators how to use Google Teachable Machine (developed by Google PAIR) to teach students how machine learning actually works. We build a 3-class classroom waste sorting classifier, demonstrate why neural networks fall into shortcut learning and dataset bias, and show how 100% of video processing runs locally in browser WebGL memory with zero student data uploaded to the cloud.

FREE DOWNLOADABLE CLASSROOM RESOURCE PACK:
- Teacher Implementation Guide (2-Page Master SOP): https://drive.google.com/file/d/1t8pCKkOo8CEZn3zHeTkkAwJSx5ij8sHl/view?usp=sharing
- Student Vision Lab Blueprint (2-Page Sheet): https://drive.google.com/file/d/1XNUWr4RZH0rwecxvQzUjDNSwSOmI8WQP/view?usp=sharing

ACCREDITED SOURCES & CITATIONS:
- Google PAIR Teachable Machine Architecture: https://teachablemachine.withgoogle.com/
- Geirhos et al. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence.
- Computer Science Teachers Association (CSTA) Standards 1B-AP-10 & 2-AP-12: https://csteachers.org/k12standards/
- AI4K12 National Guidelines (Five Big Ideas in AI): https://ai4k12.org/
- Pew Research Center (2023) Teens, Technology & AI Exposure Report