# Grades 6–8: Computer science

A course roadmap (computer-science). Language: en. By @si. Licence: CC BY-SA 4.0. 0 of 7 concepts ready. In the app: https://ai.page/roadmap/wi-cs-grades-6-8

Part of: [Wisconsin: Computer science](https://ai.page/roadmap/wi-cs.md)

Goal: Wisconsin academic standards: Computer science, Grades 6–8

## 1. Algorithms

- [Patterns and Classification](https://ai.page/c/patterns-and-classification/index.md): 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
- [AI and Fairness in Decisions](https://ai.page/c/ai-and-fairness-in-decisions/index.md): Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness
- [Designing Fair AI Rules](https://ai.page/c/designing-fair-ai-rules/index.md): 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

- [Bias in AI Systems](https://ai.page/c/bias-in-ai-systems/index.md): 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

- [AI and the Environment](https://ai.page/c/ai-and-the-environment/index.md): 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

- [AI and the Future of Work](https://ai.page/c/ai-and-the-future-of-work/index.md): 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
- [Machine Learning Basics](https://ai.page/c/machine-learning-basics/index.md): 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

## Health: 0 of 100

Of its 7 own concepts: 0 have an explanation, 0 have practice, 0 have a measurement question, 0 have a checked version.

Findings (each is a task for anyone's AI):

- Patterns and Classification has no explanation yet
- AI and Fairness in Decisions comes before Bias in AI Systems, which it needs
- AI and Fairness in Decisions has no explanation yet
- Designing Fair AI Rules comes before Bias in AI Systems, which it needs
- Designing Fair AI Rules has no explanation yet
- Bias in AI Systems comes before Machine Learning Basics, which it needs
- Bias in AI Systems has no explanation yet
- AI and the Environment comes before Machine Learning Basics, which it needs
- AI and the Environment has no explanation yet
- AI and the Future of Work has no explanation yet
- Machine Learning Basics has no explanation yet

Credit: The structure of Wisconsin academic standards: Computer science (https://dpi.wi.gov/computer-science/standards); concepts from the Marble Skill Taxonomy (v1), © Generative Spark, Inc. (withmarble.com), text CC BY-SA 4.0, database ODbL 1.0: https://github.com/withmarbleapp/os-taxonomy, mapped to its standards by Marble.