Computing · Secondary 3

Active learning ideas

Ethical Considerations in AI Use

Active learning works for this topic because ethical dilemmas in AI require students to apply abstract concepts to real-world situations. When students debate fairness, analyze privacy risks, and role-play accountability, they move from passive observers to ethical decision makers.

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

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

Activity 01

• Inside-Outside Circle
• 40 min · Pairs

Debate Pairs: AI Fairness in Hiring

Pair students and assign pro/con positions on using AI for job screening. Provide case studies of biased algorithms. Students prepare 2-minute arguments, debate, then switch sides and reflect on counterpoints in writing.

Identify ethical questions that arise from the use of AI in daily life.

Facilitation Tip

In Debate Pairs: AI Fairness in Hiring, circulate with a timer and stopwatch to ensure both sides get equal speaking time, modeling respectful discourse.

What to look for

Present students with a scenario: 'An AI system is used to approve or deny loan applications. What are three potential ethical issues that could arise from its use?' Facilitate a class discussion, prompting students to consider fairness, bias in data, and the need for human oversight.

• Remember
• Understand
• Apply
• Relationship Skills
• Self-Management

Activity 02

• Inside-Outside Circle
• 50 min · Small Groups

Group Case Study: Privacy in Smart Devices

Divide class into small groups, each assigned a device like voice assistants. Groups review real privacy breaches, list risks, and suggest mitigations. Present findings to class for Q&A.

Discuss the importance of transparency and accountability when AI makes decisions.

Facilitation Tip

For Group Case Study: Privacy in Smart Devices, provide a sample smart speaker policy and a highlighter set so students can mark specific data collection clauses before discussing.

What to look for

Provide students with a short case study of an AI application (e.g., a facial recognition system). Ask them to write down one specific way the AI's decision-making process might lack transparency and one suggestion for how to improve it.

• Remember
• Understand
• Apply
• Relationship Skills
• Self-Management

Activity 03

• Inside-Outside Circle
• 30 min · Whole Class

Whole Class Role-Play: Accountability Scenarios

Pose scenarios like self-driving car dilemmas. Students volunteer roles (AI developer, user, regulator) and improvise responses. Debrief as a class on accountability measures.

Propose solutions to mitigate ethical concerns in simple AI applications.

Facilitation Tip

During Whole Class Role-Play: Accountability Scenarios, assign a student to document key arguments and outcomes on the board to anchor the debrief.

What to look for

On an index card, ask students to define 'algorithmic bias' in their own words and provide one real-world example where it has had a negative impact.

• Remember
• Understand
• Apply
• Relationship Skills
• Self-Management

Activity 04

• Inside-Outside Circle
• 35 min · Individual

Individual Brainstorm: Ethical AI Solutions

Students list 3 AI uses in Singapore (e.g., TraceTogether), note ethical risks, and propose fixes. Share top ideas in a class gallery walk for voting.

Identify ethical questions that arise from the use of AI in daily life.

Facilitation Tip

For Individual Brainstorm: Ethical AI Solutions, give students a two-column template to separate problems from proposed fixes, preventing vague responses.

What to look for

Present students with a scenario: 'An AI system is used to approve or deny loan applications. What are three potential ethical issues that could arise from its use?' Facilitate a class discussion, prompting students to consider fairness, bias in data, and the need for human oversight.

• Remember
• Understand
• Apply
• Relationship Skills
• Self-Management

A few notes on teaching this unit

Experienced teachers approach this topic by starting with students’ lived experiences of biased recommendations or data requests before introducing technical terms like 'algorithmic bias.' We avoid overwhelming students with jargon by focusing on concrete examples they can relate to. Research shows that when students confront their own assumptions through structured debate and case analysis, they build more nuanced ethical reasoning than with lecture alone.

Successful learning looks like students articulating specific ethical concerns with evidence from case studies, proposing solutions that balance innovation and responsibility, and recognizing their own agency in shaping responsible AI use. They should connect classroom discussions to their daily interactions with technology.

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Watch Out for These Misconceptions

• During Debate Pairs: AI Fairness in Hiring, watch for students claiming AI systems are neutral.

  After the debate, introduce the COMPAS recidivism tool case and ask pairs to audit sample data snippets for underrepresented groups, forcing them to identify how training data embeds human biases.

• During Group Case Study: Privacy in Smart Devices, watch for students dismissing privacy risks as minor inconveniences.

  After the case study, have groups simulate a data breach by redacting their own device data in a mock privacy policy, then present how this exposure could affect a real person’s life.

• During Whole Class Role-Play: Accountability Scenarios, watch for students absolving developers of responsibility.

  During the role-play debrief, assign each group to propose one regulation that could prevent their scenario, then have peers challenge the feasibility and fairness of each proposal.

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Methods used in this brief

• Inside-Outside Circle