# Grades K–5: Computer science

A course roadmap (computer-science). Language: en. By @si. Licence: CC BY-SA 4.0. 0 of 11 concepts ready. In the app: https://ai.page/roadmap/wi-cs-grades-k-5

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

Goal: Wisconsin academic standards: Computer science, Grades K–5

## 1. Algorithms and Design

- [Step-by-Step Instructions](https://ai.page/c/step-by-step-instructions/index.md): 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
- [Data and Information for Computers](https://ai.page/c/data-and-information-for-computers/index.md): What data is: information that computers use — numbers, words, pictures, sounds; everything a computer knows comes from data that people give it
- [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

## 2. Algorithms

- [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
- [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

## 3. Impacts of Data Science

- [AI Data Collection and Privacy](https://ai.page/c/ai-data-collection-and-privacy/index.md): What data AI systems collect about you; who has it and why it matters; cookies, tracking, smart speakers always listening; your data is valuable
- [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
- [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?

## 4. Computing Systems and Security

- [AI in Daily Life](https://ai.page/c/ai-in-daily-life/index.md): 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
- [Computers in Everyday Life](https://ai.page/c/computers-in-everyday-life/index.md): 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

- [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

## Health: 0 of 100

Of its 11 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):

- Step-by-Step Instructions comes before Computers in Everyday Life, which it needs
- Step-by-Step Instructions has no explanation yet
- Data and Information for Computers has no explanation yet
- Patterns and Classification has no explanation yet
- Machine Learning Basics 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
- AI Data Collection and Privacy has no explanation yet
- Bias in AI Systems has no explanation yet
- Designing Fair AI Rules has no explanation yet
- AI in Daily Life has no explanation yet
- Computers in Everyday Life has no explanation yet
- AI and the Future of Work 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.