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

Discuss or write answers:

  • Did your AI make any mistakes? Why?

  • What happens if your data is too small or unclear?

  • How can you make your model “smarter”?

Key Takeaway:

AI learns from examples — the better and more diverse your data, the more accurate your AI becomes.

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