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Artificial intelligence (AI)

Term Key terms bank Y12 Software Automation

Also listed as: AI

Definition

The broad field of making computers perform tasks that normally need human intelligence (reasoning, understanding language, recognising images). ML is one way of achieving AI.

Example: All ML is AI but not all AI is ML.

Found in: syllabus, 2025 HSC marking guidelines, sample exam and familiarisation guidelines, school notes, past SDD papers.

Short version to memorise (key terms bank)

Machines performing tasks that need human intelligence; ML is a subset.

Where it is used in the test

Syllabus area: Y12 Software Automation.

Question types it can appear in

Where it has appeared in the paper

Software Automation (Part A, C). Part letters follow each paper’s own layout.

Related past questions

These are the questions the flashcard set links to this term. Open each one and compare your answer with its marking criteria.

Other questions that mention it

Study links

How to use it in an answer

A model sentence

General advice, built from the definition and example above

“The broad field of making computers perform tasks that normally need human intelligence (reasoning, understanding language, recognising images).” Then apply it: All ML is AI but not all AI is ML.

In a short answer, give the definition in your own words first, then tie it to the situation in the question. Do not copy the example above unless it fits the scenario.

Command words used with it

From the linked past questions
  • Explain: Relate cause and effect; make the relationship between things evident; provide why and/or how. (2025 HSC Q25 (5 marks): Patterns in human behaviour; neural networks (virtual personal assistant))

Technique for the question types

What the marking guidelines and feedback say

2025 HSC Q25 (5 marks): Patterns in human behaviour; neural networks (virtual personal assistant)

Marker feedback: explain how a neural network interacts with the VPA; differentiate neural networks from AI; use course language (nodes, weightings); pick clear positive and negative impacts from the stimulus.

Familiarisation Q14 (8 marks): AI and ML in society; privacy

The MG 'sample answer' is NOT a written response: it lists the syllabus dot points the question draws on (ML training models; ML applications; impact of automation on individual, society and environment; patterns in human behaviour; human and data set source bias; security, privacy, regulatory compliance; privacy by design; social, ethical and legal issues; data mining; streaming service management). The slideshow is not available. Use these dot points as a checklist for a judgement answer: state your judgement, give advantages and disadvantages, and cite slideshow items.

Worked answers from the marking guidelines

2025 HSC Q25 (5 marks): Patterns in human behaviour; neural networks (virtual personal assistant) (5 marks): criteria and sample answer

Criteria

5: Explains how the neural network supports the VPA AND includes one positive AND one negative impact. 4: Explains how the NN supports the VPA OR outlines how it supports the VPA and includes one positive AND one negative impact. 3: Outlines features of a neural network AND/OR impacts. 2: Outlines a feature of a neural network OR an impact, OR identifies features/impacts. 1: Some relevant information.

Sample answer

Neural networks recognise natural language patterns to categorise common support queries, such as lost luggage, and use nodes to assist with processing. They assess language tone to gauge frustration or urgency, allowing the virtual personal assistant to recommend specific phrasing for customer service staff. For instance, calming language may be suggested for distressed customers. By tracking customer ratings and turnaround times, the neural network uses weightings to refine recommendations to prioritise high-frequency queries or improve suggestions for poorly rated interactions. While this system streamlines support processes and improves response accuracy, it may also impact the role of customer service staff. As the neural network learns from interactions, staff may rely more on its recommendations, potentially reducing their familiarity with emerging query trends or response strategies. Customers may come to expect faster, more precise service, raising the standard for human interactions.

Open the question page

Common mistakes

General advice
  • Use the term exactly as the syllabus words it, then link it to the scenario in the question; the 2025 feedback repeatedly rewards using the stimulus.
  • Answer the command word: outline gives main points only, explain gives a reason or cause, discuss gives both sides.

Same group in the syllabus: Machine learning (ML) · Distinguishing AI and ML · DevOps · Robotic process automation (RPA) · Business process automation (BPA)

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In Y12 Software AutomationBias (human and dataset) →