CCE · Primary 6

Active learning ideas

Artificial Intelligence and Ethical Considerations

Active learning works for this topic because ethical dilemmas in AI are complex and require collaborative analysis. Students need to see how abstract concepts like bias and accountability play out in real decisions, not just listen to explanations. Group activities turn passive listening into active grappling with consequences.

• MOE Syllabus Outcomes
• MOE: Cyber Wellness - P6
• MOE: Moral Reasoning - P6

• 30–50 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Case Study Analysis
• 45 min · Small Groups

Debate Circles: AI Bias Pros and Cons

Divide class into groups to research one pro and one con of AI bias examples, such as hiring algorithms. Groups present arguments in a circle debate, with peers asking clarifying questions. Conclude with a class vote on mitigation strategies.

Explain the ethical challenges associated with the development and deployment of artificial intelligence.

Facilitation Tip

For Debate Circles, assign roles like researcher, moderator, and skeptic to ensure all voices contribute and prevent dominant speakers from taking over.

What to look for

Present students with a scenario: 'An AI system is developed to help judges decide on bail. It is trained on past cases where certain communities were disproportionately denied bail. What are the ethical concerns here?' Facilitate a class discussion using these guiding questions: What is the potential bias? Who might be unfairly affected? How could this bias be addressed?

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Activity 02

• Case Study Analysis
• 30 min · Pairs

Role-Play: Job Displacement Scenarios

Pairs act out scenes where AI replaces a job, like a robot chef or self-driving taxi. One student plays the affected worker, the other the policymaker proposing solutions. Switch roles and discuss ethical responses as a class.

Analyze the potential for algorithmic bias in AI systems and its societal impact.

Facilitation Tip

During Role-Play, give students a character card with clear stakes so they embody perspectives authentically, not just recite arguments.

What to look for

Ask students to write down one example of AI they encounter or hear about. Then, have them identify one potential ethical challenge related to that specific AI example. For instance, 'AI in social media feeds' could have the challenge of 'manipulating user emotions'.

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Activity 03

• Case Study Analysis
• 50 min · Small Groups

Case Study Stations: Real AI Ethics

Set up stations with cases like biased loan approvals or autonomous car dilemmas. Small groups rotate, note ethical issues, and propose fixes on worksheets. Share findings in a whole-class gallery walk.

Predict the future implications of AI on employment and human decision-making.

Facilitation Tip

In Case Study Stations, post a timer for 8 minutes per station to keep discussions focused and prevent tangential conversations.

What to look for

Provide students with a statement: 'AI will create more jobs than it destroys.' Ask them to write 'Agree' or 'Disagree' and then provide one reason for their choice, referencing either job displacement or the creation of new roles related to AI.

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Activity 04

• Case Study Analysis
• 40 min · Small Groups

Future Prediction Think Tank

In small groups, students brainstorm AI's 10-year impact on jobs in Singapore, using prompt cards for categories like education and retail. Groups create posters with predictions and ethical safeguards, then pitch to the class.

Explain the ethical challenges associated with the development and deployment of artificial intelligence.

Facilitation Tip

For the Future Prediction Think Tank, provide sentence stems like 'If AI takes over X jobs, then people might...' to scaffold predictions.

What to look for

Present students with a scenario: 'An AI system is developed to help judges decide on bail. It is trained on past cases where certain communities were disproportionately denied bail. What are the ethical concerns here?' Facilitate a class discussion using these guiding questions: What is the potential bias? Who might be unfairly affected? How could this bias be addressed?

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

A few notes on teaching this unit

Teachers should avoid presenting ethics as a binary choice between 'good' and 'bad' AI. Instead, model nuanced thinking by highlighting how trade-offs are built into every system. Use real examples students can relate to, like school attendance algorithms or social media filters. Research shows that when students explain ethical dilemmas to peers, their understanding deepens more than with teacher-led lectures.

Successful learning looks like students confidently identifying bias in AI examples, explaining how skewed data affects outcomes, and debating trade-offs between efficiency and fairness. They should challenge assumptions, connect ethical principles to practical scenarios, and propose solutions grounded in evidence.

---

Watch Out for These Misconceptions

• During Debate Circles on AI Bias Pros and Cons, watch for students claiming AI is neutral because it is 'just code.' Correction: Have the group review a sample dataset used to train an AI, such as facial recognition images, and ask them to identify who might be underrepresented or overrepresented. Ask them to explain how these gaps could lead to errors in real-world use.

  During Debate Circles on AI Bias Pros and Cons, watch for students claiming AI is neutral because it is 'just code.' Correction: Have the group review a sample dataset used to train an AI, such as facial recognition images, and ask them to identify who might be underrepresented or overrepresented. Ask them to explain how these gaps could lead to errors in real-world use.

• During Role-Play of Job Displacement Scenarios, watch for students assuming all jobs will disappear. Correction: After the role-play, have each group list the skills they identified as still valuable in an AI-assisted workplace. Compare lists across groups to highlight how human strengths like creativity and empathy remain essential.

  During Role-Play of Job Displacement Scenarios, watch for students assuming all jobs will disappear. Correction: After the role-play, have each group list the skills they identified as still valuable in an AI-assisted workplace. Compare lists across groups to highlight how human strengths like creativity and empathy remain essential.

• During Case Study Stations for Real AI Ethics, watch for students saying 'machines can't be biased.' Correction: Provide a case study about an AI system that made biased loan approvals due to training data. Ask students to trace the bias from the data source to the AI's decision, then brainstorm how to redesign the data collection process.

  During Case Study Stations for Real AI Ethics, watch for students saying 'machines can't be biased.' Correction: Provide a case study about an AI system that made biased loan approvals due to training data. Ask students to trace the bias from the data source to the AI's decision, then brainstorm how to redesign the data collection process.

---

Methods used in this brief

• Case Study Analysis