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How to learn Grades K–5: Computer science

Wisconsin academic standards: Computer science, Grades K–5

0 of 11 concepts ready to learn · Markdown, for your AI

1. Algorithms and Design

  1. Step-by-Step Instructions: Step-by-step instructions for everyday tasks (making a sandwich, brushing teeth); that if instructions are wrong or missing, things go wrong; computers follow instructions exactly
  2. Data and Information for Computers: What data is: information that computers use — numbers, words, pictures, sounds; everything a computer knows comes from data that people give it
  3. 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. Algorithms

  1. 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
  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. Impacts of Data Science

  1. AI Data Collection and Privacy: What data AI systems collect about you; who has it and why it matters; cookies, tracking, smart speakers always listening; your data is valuable
  2. 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. 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?

4. Computing Systems and Security

  1. AI in Daily Life: Spotting AI in daily life: face unlock on a phone, video recommendations, spelling auto-correct, automatic doors that detect people; technology that seems to 'know' things
  2. Computers in Everyday Life: Identifying computers in everyday life — not just laptops but phones, tablets, smart speakers, traffic lights, washing machines; what makes something a computer

5. 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