Aim:

To explore how more data and variety improve an AI model’s accuracy.

Steps:

1. Revisit your Teachable Machine project.

2. Add more diverse data — different lighting, background, objects, or hand sizes.

3. Re-train and test again.

4. Record your results:

   • How many mistakes before vs. after adding data?

   • What changed in accuracy?

Outcome:

Students understand that AI learning improves with good, diverse data, promoting fairness and accuracy in real-world systems.