Technologies · Year 6

Active learning ideas

Fairness in AI Decisions

Active learning helps students grasp fairness in AI by turning abstract concepts into concrete experiences. When students role-play, analyze datasets, and debate scenarios, they directly see how biases emerge in technology that often appears neutral.

• ACARA Content Descriptions
• AC9TDI6K04
• AC9TDI6P07

• 30–45 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Case Study Analysis
• 30 min · Pairs

Role-Play: Human vs AI Judge

Divide class into pairs: one acts as a human judge, the other as an AI using predefined rules on cards. Present scenarios like hiring or game penalties; switch roles and discuss differences. Groups report one key insight on fairness.

Explain why an AI might sometimes make a decision that seems unfair.

Facilitation Tip

For the Role-Play activity, assign clear roles and provide scripted scenarios so students focus on comparing human empathy with AI pattern-matching rather than improvising.

What to look for

Present students with a scenario: 'An AI is used to decide which students get extra help in a reading program. The AI was trained on data from last year, where more boys received help than girls.' Ask: 'Why might this AI decision seem unfair? How is this different from a teacher deciding?'

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

Activity 02

• Case Study Analysis
• 45 min · Small Groups

Bias Hunt: Dataset Analysis

Provide printed datasets on faces or names with imbalances. In small groups, students tally representations, predict AI outputs, then test with a simple sorting app. Discuss how to fix imbalances.

Compare how a human makes a decision versus how an AI might make one.

What to look for

Show students images of common AI-powered tools (e.g., a search engine results page, a video game opponent, a music streaming recommendation). Ask them to write down one way the AI in that tool might make a decision that could be unfair to someone, and one reason why.

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

Activity 03

• Case Study Analysis
• 40 min · Small Groups

Scenario Debate Carousel

Post 4-5 AI fairness scenarios around the room. Groups visit each for 5 minutes, note pros/cons of AI decisions, then rotate to build on prior notes. Whole class votes on fairest solutions.

Discuss a simple scenario where an AI's decision could affect people differently.

What to look for

On a slip of paper, ask students to define 'bias' in their own words and give one example of how it could affect an AI decision in a school setting, like choosing teams for a game.

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

Activity 04

• Case Study Analysis
• 35 min · Individual

Fair AI Design Challenge

Individuals sketch an AI tool for school use, list decision rules, and flag potential biases. Pairs review and refine, then share with class for feedback.

Explain why an AI might sometimes make a decision that seems unfair.

What to look for

Present students with a scenario: 'An AI is used to decide which students get extra help in a reading program. The AI was trained on data from last year, where more boys received help than girls.' Ask: 'Why might this AI decision seem unfair? How is this different from a teacher deciding?'

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

A few notes on teaching this unit

Teachers should emphasize comparison—between human and AI decision-making, and between biased and unbiased data. Avoid letting discussions stay theoretical. Use structured activities to anchor abstract ideas in student experiences, as research shows this builds deeper understanding of fairness concepts in young learners.

Successful learning looks like students identifying specific biases in datasets, explaining how AI decisions differ from human judgment in relatable terms, and designing fairer AI systems. Clear evidence of this includes reasoned arguments during debates and thoughtful design choices in their challenges.

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Watch Out for These Misconceptions

• During the Role-Play: Human vs AI Judge activity, watch for students who assume the AI judge will always be correct because it uses data.

  Use the role-play to highlight how AI relies on flawed data. After the activity, have students compare their AI judge's decision with the human judge's reasoning to show how context and empathy fill gaps that data alone cannot.

• During the Bias Hunt: Dataset Analysis activity, watch for students who think biases are intentional rather than accidental flaws in data.

  Guide students to focus on patterns in the data that disadvantage groups without anyone meaning to create those patterns. Ask them to identify which groups are affected and why the data might have missed them.

• During the Fair AI Design Challenge activity, watch for students who believe fairness means treating everyone exactly the same way.

  Use the design challenge to push students toward equitable solutions. Have them explain why identical treatment might not work for different groups, and ask them to redesign their AI to account for varying needs.

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Methods used in this brief

• Case Study Analysis