AI For Hong Kong High School

Teaching focus for each age group:

• Grade 7-8: Foundation Building

• Grade 9-10: Expanding Concept

• Grade 11-12: Advanced Knowledge

Detail curriculum for each grade:

Grade 7:

• What is AI

  • Smart assistant vs Computer program

  • Examples in daily lives

  • What AI can do today

• Basic algorithm and pattern

  • Basic if/then logic

  • Sorting and classification

  • Pattern recognition game

• Data and bias – simple explanation

  • What is “garbage in, garbage out”

  • What is AI hallucination

  • What is bad or unfair data

  • Why AI sometimes behave badly

• Basic AI ethics

  • Privacy

  • Data collection by AI

  • Creating or copying?

  • Honesty and fairness

Grade 8:

• How machine “learns”

  • Training vs rules

  • How computer learns words

• How computer handle image or sound

• Supervised learning ideas

  • Labeled examples (e.g., “apple/not apple”)

  • What “training data” means for accuracy

• Everyday AI systems

  • Recommendation engines

  • Spam filters

  • Translation apps

  • where they can fail

• Social impact of AI

  • Social media AI causes biases

  • AI hallucination causes misinformation

  • Importance of common sense and critical thinking

Grade 9:

• Core AI branches:

  • Classic machine learning

  • Computer vision

  • Natural Language Processing

  • Robotics

  • Generative AI (text and images)

• Hands-on intro tools

  • Use Teachable Machine (or similar) to build tiny classifiers

  • Experience hands-on AI

• Measurement concepts:

  • Accuracy

  • Training vs testing data

  • Overfitting – simple explanation

• Deeper AI ethics:

  • Fairness

  • Bias against groups

  • Accountability

Grade 10:

• Introduction to data science for AI

  • Datasets

  • Features

  • Labels

  • Cleaning data

  • Simple visualizations

• Machine learning methods at a high level

  • Classification vs regression

  • Clustering

  • Real-world examples

• Prompting and generative AI

  • How large language models (LLM) work conceptually

  • Writing eZective prompts

• AI in careers

  • How AI is changing work in medicine

  • Finance

  • Law

  • Creative industries

  • New skills needed

Grade 11:

• Deeper ML concepts

  • Training /validation / testing

  • Evaluation metrics (basics of precision, recall, confusion matrix)

• Neural networks idea

  • Basics of neurons, layers, training loops

  • Simple demos or visual tools

• Applied AI projects

  • Small projects (e.g. sentiment analysis, image recognition, chatbots)

• Policy and regulation

  • AI laws in diZerent countries

  • Data protection and copyrights

  • Deepfakes problems

  • Algorithmic transparency debates

Grade 12:

• End-to-end AI project

  • Define a problem

  • Gather/prepare data

  • Train a model

  • Evaluate

  • Present results

• Advanced topics overview

  • Reinforcement learning

  • Recommendation systems

  • Large language models

• Multi-modal AI

• Societal futures

  • AI and the future of work

  • Inequality

  • Global challenges (climate, health)

  • AI Safety

  • Human–AI collaboration

• Ethics & philosophy capstone

  • Autonomy

  • Responsibility

  • Human agency

  • Students’ own policy proposals