Digital Solutions · Year 12

Active learning ideas

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the digital landscape. In this topic, Year 12 students move beyond the hype to understand the underlying mechanics of neural networks and training datasets. They explore how ML models learn from patterns in data rather than following explicit, hard-coded instructions. This aligns with the ACARA focus on emerging technologies and their impact on society.

• ACARA Content Descriptions
• QCAA DS 2019: Unit 4.3.1
• ACARA: ACTDIP044

• 25–50 min
• Pairs → Whole Class
• 3 activities

Activity 01

• Simulation Game
• 40 min · Whole Class

Simulation Game: Training a Human Neural Network

Students act as 'neurons' in different layers. They pass 'signals' (numbers) to each other, applying simple weights (multiplication). They must collectively 'classify' an input image, adjusting their weights based on whether the final answer was correct.

What distinguishes machine learning from traditional programming?

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Activity 02

• Inquiry Circle
• 50 min · Small Groups

Inquiry Circle: The Bias Detective

Groups are given a dataset used to train a fictional AI (e.g., for job hiring). They must identify missing demographics or skewed data points and predict how this bias will manifest in the AI's decisions.

How are neural networks structured?

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

Activity 03

• Think-Pair-Share
• 25 min · Pairs

Think-Pair-Share: AI vs. Traditional Programming

Students are given three problems (e.g., calculating tax, recognising a cat, playing chess). They must decide if each is better solved with traditional 'if-then' logic or a machine learning model, justifying their choice to a partner.

What role does training data play in AI accuracy?

• Understand
• Apply
• Analyze
• Self-Awareness
• Relationship Skills

A few notes on teaching this unit

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Watch Out for These Misconceptions

• AI 'thinks' like a human brain.

  AI uses mathematical patterns and statistics, not consciousness. A 'Human Neural Network' simulation helps students see that it's just a series of calculations and weight adjustments, not 'thinking'.

• AI is always objective and neutral.

  AI reflects the biases of its training data. A hands-on activity where students train an image recogniser with limited data (e.g., only green apples) helps them see how the AI 'fails' when it encounters a red apple.

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Methods used in this brief

• Simulation Game
• Inquiry Circle
• Think-Pair-Share