Lesson Plan

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Lesson Plan
Grade: 9 Date: 17/01/2026
Subject: Computer Science
Lesson Topic: Explain the basic operation and components of AI systems
Learning Objective/s:
  • Describe the core components of an AI system (data, algorithm, model, hardware, interface).
  • Explain the AI operation cycle from data collection to monitoring and updating.
  • Analyse how a trained model is deployed and used for inference.
  • Evaluate the impact of over‑fitting and hyper‑parameters on model performance.
Materials Needed:
  • Projector or interactive whiteboard
  • Slides with AI component diagrams
  • Sample dataset (e.g., handwritten digit images)
  • Laptop with Python/IDE and pre‑installed libraries (TensorFlow/Keras)
  • Worksheet with flowchart and key‑term matching
  • Access to an online AI demo or simulation
 Introduction:
Begin with a quick poll: “Which everyday technologies rely on AI?” Students share examples, linking to prior knowledge of pattern recognition. Explain that today they will unpack how such systems work internally and identify the success criteria: naming components, outlining the operation cycle, and predicting where over‑fitting can occur.
Lesson Structure:
  1. Do‑now (5’) – Students list AI examples on sticky notes; teacher clusters responses.
  2. Mini‑lecture (10’) – Present core components and operation cycle using slides and diagram.
  3. Guided activity (15’) – In pairs, students map a simple image‑classification example onto the cycle, filling a worksheet.
  4. Live demo (10’) – Show a pre‑trained model classifying a handwritten digit; discuss data, algorithm, hardware, interface.
  5. Check for understanding (5’) – Quick Kahoot quiz on component definitions and over‑fitting.
  6. Extension discussion (5’) – Brainstorm how hyper‑parameters affect training and how monitoring updates models.
Conclusion:
Recap the five components and the step‑by‑step AI cycle, emphasizing how each stage influences model quality. Students complete an exit ticket: write one way to improve a deployed AI system. For homework, they research a real‑world AI application and prepare a short summary of its components and operation.