📅 Lesson Timeline (90-120 minutes)
🎯 Platform Quick Comparison
🎓 Teachable Machine
Best for: First-time AI creators
URL: teachablemachine.withgoogle.com
Setup: No account needed
Time: Fastest (30-45 min project)
📱 MIT App Inventor
Best for: Mobile app developers
URL: appinventor.mit.edu
Setup: Google account required
Time: Moderate (60-75 min project)
🐱 Scratch with ML
Best for: Interactive games/stories | URL: scratch.mit.edu + machinelearningforkids.co.uk | Setup: Scratch account + ML for Kids account | Time: Longest (75-90 min project)
✅ Pre-Class Checklist
- Test all platforms beforehand
- Check camera/mic permissions
- Ensure stable internet connection
- Print planning worksheets
- Set up student accounts (if needed)
- Prepare backup project ideas
- Gather physical objects for training
- Test projection/display system
- Have troubleshooting guide ready
- Prepare assessment rubrics
💡 Key Teaching Points
Training Data Quality
- 50+ samples per category minimum
- Diversity is more important than quantity
- Vary angles, lighting, backgrounds
- Balance samples across categories
Iteration is Normal
- First attempts rarely perfect
- 70%+ accuracy is good for students
- Testing reveals improvement areas
- Professional AI also iterates
🚨 Common Problems & Quick Fixes
| Problem |
Quick Fix |
| Camera/Mic not working |
Check browser permissions in address bar, try different browser (Chrome best) |
| Low model accuracy (<50%) |
Add more diverse samples, simplify categories, check for background interference |
| Student can't think of project |
Offer menu of pre-approved ideas, show more examples, suggest simple versions |
| Platform running slow |
Close other tabs, clear cache, reduce image quality, try different device |
| Project too ambitious |
Help identify MVP (Minimum Viable Product), reduce from 5 to 3 categories |
| Students finish at different rates |
Have extension challenges ready: improve accuracy, add features, help peers |
🎤 Discussion Questions During Class
During Data Collection:
- "Why do we need so many samples?"
- "How is your data diverse?"
- "What makes categories distinguishable?"
During Testing:
- "What's your accuracy rate?"
- "Which categories confuse most?"
- "How will you improve your model?"
⏰ Time Management Tip: Set visible timers for each phase. Build in 5-min buffer between phases for transitions. If running short on time, prioritize: Planning → Data Collection → Training → Testing. Refinement and showcase can be continued next class if needed.
🎓 Teachable Machine - 5-Step Process
Step 1: Choose Project Type
teachablemachine.withgoogle.com → Get Started → Choose Image/Audio/Pose
Step 2: Add Classes
Rename "Class 1", "Class 2" to meaningful names → Add more classes (3-5 total)
Step 3: Record Samples
Click Webcam/Microphone → Hold to Record → Capture 50+ samples per class
Step 4: Train Model
Click "Train Model" → Wait 1-3 minutes → Training complete!
Step 5: Test & Export
Use Preview panel to test → Export Model to save or share
Pro Tip: Have students test with a partner before considering their model complete. Fresh eyes catch issues!
📱 MIT App Inventor - Key Steps
Setup (5 min)
appinventor.mit.edu → Sign in with Google → Create New Project
Designer View (10 min)
Add components: Button, Camera, Image, Label → Add AI Extension (Personal Image Classifier)
Blocks View (10 min)
When Button.Click → Camera.TakePicture → Classify → Display Result
Testing (ongoing)
Install MIT AI2 Companion app → Connect → AI Companion → Test on phone
Common Issue: Blocks won't snap together? Check that shapes/colors match. Red triangles indicate errors.
🐱 Scratch + ML for Kids - Two Methods
Method 1: Video Sensing (Simpler)
- Add "Video Sensing" extension
- Turn video on
- Use "video motion" blocks
- Good for motion-based games
Method 2: ML for Kids (Advanced)
- Go to machinelearningforkids.co.uk
- Train custom model
- Get special Scratch link
- Import model into Scratch
Time Saver: If time is limited, stick with Video Sensing for first projects. ML for Kids requires two accounts and more setup time.
📊 Assessment Quick Rubric
| Criteria |
Excellent (A) |
Good (B) |
Needs Work (C) |
| Functionality |
85%+ accuracy, all features work |
70-84% accuracy, minor issues |
<70% accuracy or major issues |
| Training Data |
75+ diverse samples/class |
50-74 samples, some variety |
<50 samples or not diverse |
| Iteration |
2+ improvement cycles, documented |
1 improvement cycle shown |
No iteration or documentation |
| Presentation |
Clear demo + explanation + reflection |
Demo + basic explanation |
Demo only, limited explanation |
🎯 Differentiation Quick Tips
For Struggling Students
- Suggest Teachable Machine (easiest)
- Start with 2-3 categories only
- Provide step-by-step checklist
- Pair with peer buddy
- Offer pre-selected project ideas
For Advanced Students
- Challenge with 5+ categories
- Encourage model export/integration
- Suggest combining AI types
- Ask to help troubleshoot peers
- Explore transfer learning concepts
🎉 Celebration Idea: Create a digital gallery of all student projects. Take photos/screenshots during presentations and share with parents/administration to showcase student AI creators!
Need More Help?
Full materials at: /edai/lesson-repository/lesson-12/downloads/
Evolve AI Institute | info@evolveaiinstitute.com