Economics & Business · Year 9

Active learning ideas

Automation and Artificial Intelligence

Active learning lets students engage directly with the real-world consequences of automation and AI, turning abstract concepts into tangible discussions and decisions. When they role-play job displacement or analyze biased algorithms, they confront their own assumptions and see how technology reshapes work in concrete ways.

• ACARA Content Descriptions
• AC9HE9K04

• 40–60 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Four Corners
• 50 min · Pairs

Debate Carousel: AI Ethics Dilemmas

Divide class into pairs to prepare arguments for and against AI in hiring or surveillance. Rotate pairs every 10 minutes to debate new opponents at four stations, each with a unique scenario. Conclude with whole-class vote and reflection on strongest points.

What skills will be most resilient to automation in the next decade?

Facilitation Tip

In the Debate Carousel, assign each group a unique AI ethics scenario to ensure diverse perspectives and prevent repetition across stations.

What to look for

Pose this question to small groups: 'Imagine you are a factory owner deciding whether to invest in new automation. What are the economic benefits and the potential social costs you must consider?' Have groups share their top two benefits and top two costs.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 02

• Four Corners
• 60 min · Small Groups

Industry Risk Mapping: Group Analysis

Assign small groups an industry like retail or healthcare. Groups research automation trends using provided articles, create risk matrices rating vulnerability on factors like routine tasks and tech adoption, then present findings. Peers score matrices for completeness.

Analyze the ethical considerations surrounding widespread AI adoption in the workplace.

Facilitation Tip

For Industry Risk Mapping, provide sector-specific data sets so groups analyze real labor trends rather than guessing risks.

What to look for

Provide students with a list of 10 job roles (e.g., data scientist, artist, assembly line worker, therapist, accountant). Ask them to categorize each role as 'High Risk', 'Medium Risk', or 'Low Risk' of automation in the next 15 years, providing one sentence justification for each choice.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 03

• Four Corners
• 45 min · Small Groups

Future Skills Simulation: Role-Play Scenarios

Students draw workplace roles affected by AI, such as driver or analyst. In small groups, they role-play pre- and post-automation shifts, brainstorming resilient skills needed. Groups share skits and compile a class skills wishlist.

Predict which industries are most vulnerable to job displacement due to automation.

Facilitation Tip

In the Future Skills Simulation, give each role clear constraints and incentives to push students to think critically about trade-offs in their decisions.

What to look for

On an index card, have students write down one skill they believe will be crucial for their future career in light of automation, and one concrete action they can take this week to develop that skill.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 04

• Four Corners
• 40 min · Pairs

Labor Market Prediction: Data Dash

Provide datasets on job growth projections. Individuals or pairs graph automation impacts, predict top vulnerable jobs, and justify with evidence. Discuss predictions as a class, updating graphs based on peer input.

What skills will be most resilient to automation in the next decade?

Facilitation Tip

During the Data Dash, model how to interpret job growth charts before students begin, so they focus on analysis rather than data extraction.

What to look for

Pose this question to small groups: 'Imagine you are a factory owner deciding whether to invest in new automation. What are the economic benefits and the potential social costs you must consider?' Have groups share their top two benefits and top two costs.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

A few notes on teaching this unit

Teachers should frame automation as a design challenge rather than a crisis, emphasizing how humans shape AI’s impact. Avoid framing AI as an unstoppable force; instead, highlight human agency in regulation, oversight, and skill adaptation. Research shows students grasp complex systems better when they see technologies as tools for problem-solving, not replacements for human judgment.

Successful learning looks like students moving from vague concerns about job loss to specific, evidence-based arguments about automation’s impacts. They should articulate risks, justify ethical positions, and connect skills to future careers with clarity and nuance.

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Watch Out for These Misconceptions

• During Debate Carousel: AI Ethics Dilemmas, watch for students assuming automation will eliminate entire professions without considering how new roles emerge.

  Use the debate prompts to guide students toward identifying complementary tasks where humans add value, such as overseeing AI outputs or handling exceptions, and have them map these roles in their discussions.

• During Industry Risk Mapping: Group Analysis, watch for students assuming only manual jobs are at risk.

  Direct groups to compare manufacturing data with service sector trends, and require them to present one counterexample where AI affects white-collar work, using their case studies as evidence.

• During Future Skills Simulation: Role-Play Scenarios, watch for students believing AI systems are inherently unbiased.

  During the role-play, provide each group with a mock dataset containing hidden biases and have them present how the algorithm perpetuates those biases, then brainstorm fixes together.

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Methods used in this brief

• Four Corners