Computing · Secondary 3

Active learning ideas

Introduction to Artificial Intelligence

Active learning works well for AI and ethics because students often see the topic as abstract or theoretical until they interact with real-world examples. When students debate, investigate data, and explore local applications, they connect abstract concepts to concrete dilemmas and decisions.

• MOE Syllabus Outcomes
• MOE: Ethics and Social Issues - S3

• 35–45 min
• Pairs → Whole Class
• 3 activities

Activity 01

• Formal Debate
• 45 min · Whole Class

Formal Debate: The Trolley Problem for Self-Driving Cars

Students are presented with a scenario where an AI car must choose between two harmful outcomes. They must debate which 'logic' the car should follow and who is responsible for the final decision: the programmer, the owner, or the car itself.

Explain the basic concepts of Artificial Intelligence and Machine Learning.

Facilitation Tip

During the Trolley Problem debate, assign roles clearly so students must defend perspectives they may personally oppose.

What to look for

Pose the following question to the class: 'Imagine an AI system is used to screen job applications. What are two potential benefits and two potential ethical concerns related to using AI for this purpose? Be specific about the types of bias that could arise.'

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

Activity 02

• Inquiry Circle
• 40 min · Small Groups

Inquiry Circle: Bias in the Data

Groups are given a 'dataset' (e.g., photos for a facial recognition system) that is heavily skewed toward one demographic. They must identify how this bias would affect the AI's performance and suggest ways to make the dataset more inclusive.

Analyze how AI is currently impacting various industries and daily routines.

Facilitation Tip

For the Bias in Data activity, provide a small subset of structured data so students can see bias emerge in real time.

What to look for

Provide students with short scenarios describing AI applications (e.g., a chatbot for customer service, a recommendation engine for streaming services). Ask them to identify whether each scenario represents weak AI or strong AI and briefly justify their answer.

• Analyze
• Evaluate
• Create
• Self-Management
• Self-Awareness

Activity 03

• Gallery Walk
• 35 min · Small Groups

Gallery Walk: AI in Singapore

Students research different ways AI is used in Singapore (e.g., Changi Airport, Smart HDB towns). They create posters highlighting one benefit and one ethical concern for each application, then walk around to critique each other's findings.

Differentiate between strong AI and weak AI with relevant examples.

Facilitation Tip

During the Gallery Walk, ask guiding questions like ‘Who benefits from this AI system?’ and ‘What data might be missing?’ to focus observations.

What to look for

Ask students to write down one industry significantly impacted by AI and one specific way AI is used within that industry. Then, have them list one question they still have about AI or its societal implications.

• Understand
• Apply
• Analyze
• Create
• Relationship Skills
• Social Awareness

A few notes on teaching this unit

Start with concrete examples students recognize, like targeted ads or chatbots, before introducing technical terms. Avoid overwhelming students with coding or algorithms; focus on decision-making and data choices. Research shows students grasp bias better when they manipulate biased datasets themselves rather than just reading about it.

Successful learning looks like students questioning assumptions, identifying bias in datasets, and articulating ethical trade-offs in AI applications. Students should move from passive acceptance of AI’s ‘neutrality’ to critical analysis of its societal impact.

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Watch Out for These Misconceptions

• During the Bias in Data activity, watch for students who assume datasets are objective because they are presented in a spreadsheet.

  Use the activity to show how even neutral-looking data can encode historical biases, such as hiring data reflecting past discrimination. Ask students to identify which features might lead to biased outcomes.

• During the Future Careers brainstorming session, watch for students who assume AI will eliminate jobs entirely.

  Use the brainstorming session to categorize tasks as automatable or augmentable. Have students map how AI tools might create new roles requiring human oversight, like AI trainers or ethics auditors.

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Methods used in this brief

• Formal Debate
• Inquiry Circle
• Gallery Walk