China 九年级（初三） Information Technology
Chapters: 2
1. Module: AI and smart society
AI application scenarios · Machine computation vs human computation · Ethics and safety challenges of AI
- Artificial Intelligence: How Machines Learn to Think – Artificial intelligence (AI) is the skill of a computer system to do tasks that normally need human thinking: seeing, understanding speech, deciding and learning. An AI system is an agent that senses, thinks and acts. Old AI followed rules written by people. Modern AI mostly uses machine learning: it finds its own rule from many labelled examples (data). Neural networks are layers of simple units whose link strengths (weights) change during training. AI is used in maps, translation, health, farming and games. It can be wrong or unfair when its data is one-sided (bias), so people must check it, protect privacy and stay responsible.
- Machine Learning – Machine learning (ML) is a way for computers to learn a rule from examples instead of being told the rule. We give data made of features (inputs) and, in supervised learning, labels (answers). The machine fits a model: regression predicts a number, a decision tree asks yes/no questions to pick a class, and k-means clustering groups unlabelled data. We train on most of the data, test on data it never saw, and measure accuracy or error. A model that only memorises (overfits) fails on new data.
- AI Ethics: Using Artificial Intelligence Fairly and Safely – AI systems learn patterns from data and then make decisions. AI ethics asks whether those decisions are fair, safe and respectful. The main issues are bias (unfair data gives unfair results), privacy (personal data needs consent and protection), transparency (people should know why an AI decided something), accountability (a human stays responsible), safety and misuse (deepfakes, false information), and social impact (jobs, the digital divide, the environment). Responsible AI means checking all of these before and after an AI is used.
2. Cross-disciplinary theme
Imagining future smart scenes · AI-predicted travel
- Imagining and Designing a Smart Scene – A smart scene is a small place, like a room or a street, that senses what is happening and reacts by itself. To design one, name a problem, add sensors, send their data over a network to a hub, write simple if-then rules, and connect devices that act (lamp, fan, door). Then test it, check for mistakes, and protect people's privacy.
- AI-Predicted Travel: Using Data to Predict Traffic – To predict traffic, an AI first collects past data, such as cars per hour on many days. It looks for a pattern, like two rush-hour hills in the morning and evening. Then it predicts the next day by using that pattern, for example the average of the same hour on past days. People use the prediction to choose a better time or route. A prediction is a good guess, not a promise.