How to learn Grades 6–8: Computer science

Wisconsin academic standards: Computer science, Grades 6–8

0 of 7 concepts ready to learn · Markdown, for your AI

1. Algorithms

1. Patterns and Classification: Humans are great at spotting patterns; computers can learn to spot patterns too, but they need lots of examples; sorting and classification activities as the basis of machine learning
2. AI and Fairness in Decisions: Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness
3. Designing Fair AI Rules: Design thinking applied to AI ethics: if you were designing an AI system, what rules would you give it? Who should it help? What should it not be allowed to do?

2. Impacts of Data Science

1. Bias in AI Systems: If training data is biased, AI will be biased; examples: facial recognition working better for some skin tones, translation assuming gender; where bias comes from and whether we can fix it

3. Impacts of Computing

1. AI and the Environment: AI needs huge amounts of energy and water to train; data centres and their environmental cost; but AI can also help — predicting weather, monitoring deforestation, optimising energy; trade-offs

4. Computing and Society

1. AI and the Future of Work: How AI is changing the world of work: some jobs disappear, new ones are created, many change; jobs AI can't do (yet); what skills matter in an AI world
2. Machine Learning Basics: How machine learning works at a conceptual level: show the computer many examples, it finds patterns, then it makes predictions about new things; hands-on experience with Teachable Machine or similar tool