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AI & IoT Training Camp — Day 6 Session 1

STEM Lesson Plan Template

Adapt hands-on AI & IoT activities into 45-minute classroom lessons aligned with national curriculum.

Lesson Overview
45 minutes (recommended)
STEM Integration Areas

Check which STEM areas your lesson addresses. A good lesson should touch at least 2 areas.

Science — Sensors, physical phenomena, hypothesis testing
Technology — PictoBlox, AI models, Arduino/ESP32
Engineering — Circuit design, prototyping, problem-solving
Mathematics — Analog values, mapping, accuracy, thresholds
Simplification Strategy
Key Principle: Training Camp activities use 90-minute sessions. For a 45-minute classroom lesson, select ONE core concept from any session and simplify.
e.g., Day 1 Session 2 — Image Classifier Training
e.g., "How does a computer learn to recognize objects?"

Select your simplification method:

Lesson Timeline (45 Minutes)
PhaseDurationActivity DescriptionTeacher Action
Hook / Engage 5 min Show a real-world example. Ask thought-provoking question.
e.g., "Can a computer tell if food is expired by looking at it?"
Explain / Model 8 min Briefly explain the concept. Show PictoBlox interface. Demonstrate the key step live.
Hands-On Practice 20 min Students work in groups. Follow step-by-step instructions. Build / train / test their own version.
Share / Present 7 min 2–3 groups share results. Compare outcomes. Discuss what worked and what didn't.
Reflect / Wrap Up 5 min Quick reflection questions. Connect to real-world careers. Preview next lesson.
Materials & Equipment
#ItemQuantityAlternativeNotes
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Safety Note: If using Arduino/ESP32, always supervise students during wiring. Never connect high-current devices directly to microcontroller pins.
Example Lesson Ideas (from Training Camp)

Example 1 — Science: "Can AI sort waste?" (Grade 6–8)

Source: Day 1, Session 2 (Image Classifier)

Simplification: 2 classes only — "Recyclable" vs "Non-Recyclable". Students collect 50 images each using webcam. Test with real objects.

STEM: Science (waste classification), Technology (AI model), Math (accuracy calculation)

Example 2 — Mathematics: "Mapping sensor data to degrees" (Grade 7–9)

Source: Day 2, Session 4 (Proportional Servo)

Simplification: Software only — use slider on Stage instead of body tracking. Map slider value (0–100) to servo angle (0–180).

STEM: Mathematics (linear mapping formula), Technology (PictoBlox), Engineering (servo mechanism)

Example 3 — Science: "Temperature & humidity monitoring" (Grade 5–7)

Source: Day 4, Session 1 (DHT11 Sensor)

Simplification: Read DHT11 data only. Display temperature on Stage sprite. No actuators required.

STEM: Science (weather, climate), Technology (IoT sensor), Math (reading data, averages)

Student Assessment

Formative Assessment (During Lesson)

  • Observe group collaboration
  • Check-in questions during practice
  • Walk-around for wiring / code accuracy
  • Exit ticket (1 question)

Summative Assessment (Post-Lesson)

  • Group presents working result
  • Written reflection or short quiz
  • Peer evaluation within groups
  • Portfolio entry (screenshot of project)
Differentiation Strategies
LevelAdaptation
AdvancedAdd a 3rd class, integrate hardware actuator, write custom If-Else logic
On-LevelFollow the lesson as designed with 2 classes and guided instructions
Support NeededUse pre-built model, pair with advanced student, provide visual step cards
Teacher Reflection (after teaching)

What worked well? What would you change? How did students respond?

Peer Review (Day 6 Session 1 — exchange with another teacher)