Y10W09PA - Artificial Intelligence and Its Risks

This week you wrote a three-paragraph explanatory piece about artificial intelligence and its risks. Now you'll read another student's piece and judge how strong it is. Working through how assessors evaluate explanatory writing sharpens your ability to apply the same lens to your own work.

Part 1

The Assessor Scorecard for

Explanatory – Explanatory Piece

An effective explanatory piece selects accurate information, organises it clearly and expresses it with precision, so the reader gains genuine understanding. Assessors weigh how well content, structure and language serve the reader's need to know.

Ideas & Content

Accuracy and selection — the right information chosen and explained with enough depth. The reader understands not just what, but how and why. Explanations weaken when they are vague or incomplete. Key concepts named but not actually explained is a clear weakness.

  • Accurate selection: chooses the right information and explains how and why it matters.

Structure & Cohesion

Clear organisation signals what each section covers and how ideas connect. Writing weakens when topics bleed across paragraphs. No clear topic sentence leaves the reader guessing. The reader should never have to work out what a paragraph is about.

  • Clear organisation: signals each section’s purpose so the reader can follow without effort.

Audience & Purpose

Pitched at the right level — neither over-simplified nor assuming too much prior knowledge. Jargon used without explanation loses the reader. Tone that is too casual undermines the explanation. Tone that is too dense for the intended reader also weakens it.

  • Ask whether a: reader unfamiliar with the topic would understand each explanation without needing to look anything up.

Language Choices

Precise, subject-specific vocabulary builds credibility and clarity. Vague or informal language in place of accurate terms weakens the piece. Ordinary words should not substitute for technical ones.

  • Subject vocabulary: uses accurate terms that build clarity, precision and trust.

Conventions

Accurate spelling and punctuation, especially with technical or proper nouns. Errors in factual content or terminology undermine the reader's trust. Sentence variety supports clarity here too.

  • Technical accuracy: keeps terminology, spelling and sentence control reliable throughout.

Part 2

Today’s Marking Targets

Task in one sentence

Write a three-paragraph explanatory piece covering what AI is, how machine learning works and the main concerns about AI's growing role — selecting relevant information and writing in your own words.

Let’s Focus

Three strands matter most this week: Structure & Cohesion, Audience & Purpose and Language Choices. How the three paragraphs are organised decides whether the reader can follow the explanation. The calibration for an educated reader unfamiliar with AI decides whether the piece is accessible without being over-simplified. The precision of vocabulary decides how accurately technical concepts are communicated.

Structure & Cohesion

Strong writing this week shows Structure & Cohesion applied consistently — not just in isolated moments. Assessors look for organisation that serves this task: three paragraphs that move clearly through what AI is, how it learns and what it risks.

What markers scan for

  • Structure & Cohesion applied consistently throughout — not only in isolated moments.
  • The three-topic structure visibly shaping how each paragraph is organised.

Score Bands

  • Basic

    Structure & Cohesion is present but applied inconsistently or only at a surface level.

  • Strong

    Structure & Cohesion is applied consistently, with genuine understanding of what this task requires.

  • Excellent

    Structure & Cohesion is applied with sustained precision throughout, shaped by the specific demands of this task.

Audience & Purpose

Strong writing this week shows Audience & Purpose applied consistently — not just in isolated moments. Assessors look for calibration that serves this task: an explanation pitched for an educated reader new to AI, neither over-simplified nor assuming too much.

What markers scan for

  • Audience & Purpose applied consistently throughout — not only in isolated moments.
  • The specific reader and topic visibly shaping how the explanation is pitched.

Score Bands

  • Basic

    Audience & Purpose is present but applied inconsistently or only at a surface level.

  • Strong

    Audience & Purpose is applied consistently, with genuine understanding of what this task requires.

  • Excellent

    Audience & Purpose is applied with sustained precision throughout, shaped by the specific demands of this task.

Language Choices

Strong writing this week shows Language Choices applied consistently — not just in isolated moments. Assessors look for choices that serve this task: precise, subject-specific vocabulary that communicates technical concepts accurately rather than approximately.

What markers scan for

  • Language Choices applied consistently throughout — not only in isolated moments.
  • The technical demands of this topic visibly shaping vocabulary choices.

Score Bands

  • Basic

    Language Choices is present but applied inconsistently or only at a surface level.

  • Strong

    Language Choices is applied consistently, with genuine understanding of what this task requires.

  • Excellent

    Language Choices is applied with sustained precision throughout, shaped by the specific demands of this task.

Now read · Student sample

Artificial Intelligence and Its Risks

Year 10 sample · \~300 words

Student sample for assessment

Written by a Year 10 student in Wollongong, New South Wales, Australia.

Artificial intelligence, or AI, refers to computer systems that are designed to perform tasks that would typically require human intelligence. These tasks include things like recognising speech, identifying images, making decisions and generating text. The way most modern AI systems work is through a process called machine learning, where the system is trained on large amounts of data rather than being explicitly programmed with rules. When the AI processes this data, it identifies patterns and uses them to make predictions or generate outputs. Over time, and with more data, the system becomes more accurate. Machine learning works by feeding a system thousands or millions of examples of something and allowing it to learn what the correct answer looks like. For instance, a system trained to identify cats in photos is shown millions of images labelled as either containing a cat or not. It gradually adjusts its internal settings until it can reliably distinguish between the two. This process is called training, and the result is a model that can apply what it has learned to new images it has never seen before. The same basic approach is used for language models, recommendation systems, medical diagnosis tools and many other applications. The growing role of AI in society raises a number of concerns that are worth examining. One significant concern is bias: because AI systems learn from historical data, they can inherit and amplify existing biases present in that data. A hiring algorithm trained on historical decisions may learn to discriminate in ways that reflect past prejudice rather than genuine merit. A second concern is transparency. Many AI systems operate as what researchers call black boxes — they produce outputs without being able to explain how they reached them. This makes it difficult to identify errors or hold the system accountable. A third concern is automation and its effects on employment, as AI is increasingly capable of performing tasks previously done by humans across a wide range of industries.