Start with Experience, Not Lecture

Let students encounter ethical issues firsthand:

• Have them use AI tools and discover biases, errors, or concerning outputs themselves
• Ask them to generate images of “a doctor” or “a CEO” and notice demographic patterns
• Try to get AI to help with homework and see when it produces unreliable information

Core Topics to Cover

1. Bias & Fairness

• Where does AI training data come from?
• Whose perspectives are represented (and whose are missing)?
• Real examples: facial recognition failures, hiring algorithms, predictive policing

2. Transparency & Accountability

• How do we know why AI made a decision?
• Who’s responsible when AI makes mistakes?
• Case studies: autonomous vehicles, medical diagnosis tools

3. Privacy & Data

• What happens to the information we give AI?
• Who owns AI-generated content?
• Training data ethics (was consent given?)

4. Impact on Society

• Job displacement and economic effects
• Environmental costs of AI (energy consumption)
• Deepfakes and misinformation

Engaging Activities

• Debate scenarios: “Should AI be used to grade essays?” “Should companies use AI to screen job applications?”
• Design challenges: “Create guidelines for ethical AI use in our school”
• Red-teaming: Try to make AI behave badly, then discuss why it happened
• Case study analysis: Examine real AI failures and discuss what went wrong

Make It Personal

Connect to their lives: social media algorithms, recommendation systems, filters they use daily.