Course Content
Lesson 1: What is AI and Why Should I Care?
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: How Does AI Work?
Activity – Where Have I Seen AI Today?
Quiz
Reflection Questions
Extension Activity – “My AI Day”
Lesson 2: Can Machines Think Like Me?
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: How Do Humans and Machines Think Differently?
Activity – AI vs Human Decision Quiz
Conclusion
Quiz
Reflection Questions
Extension Project – “Human + AI Super Team”
Lesson 3: Train the Machine!
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: How AI Learns Like a Student
Activity – Teachable Machine Workshop
Reflection: What Did You Learn?
Conclusion
Quiz
Extension Project – “Smart Classifier Challenge”
Lesson 4: Bias in AI – Is That Fair?
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: How Bias Enters AI
Activity – Fair or Unfair?
Conclusion
Quiz
Extension Project – “Fair Tech Pledge”
Lesson 5: Talking to Machines – Natural Language Processing (NLP)
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: How Does NLP Work?
Real-Life Examples of NLP
Activity – Talking to Machines
Conclusion
Quiz
Extension Project – “My AI Voice Journal”
Lesson 6: My AI App Idea!
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Introduction
Learning Outcomes
Key Concepts and Definitions
Theory: Designing with Empathy
Activity – AI Assistant Design
Conclusion
Quiz
Extension Project – “AI for Good”
C7: Introduction to AI – Understanding How Machines Learn

By the end of this lesson, you will be able to:

  1. Explain what Machine Learning means.

  2. Describe how computers learn from examples and data.

  3. Understand the role of training data and patterns.

  4. Use Google’s Teachable Machine to train an AI model.

  5. Reflect on why the quality of data matters in AI learning.
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