AI Constitution

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Education Concept Tracker

A prerequisite knowledge graph of 1590 learning concepts across 8subjects. Track what you've mastered and see exactly what you're ready to learn next — the topics whose prerequisites you've already met. Built on the Marble Open Skill Taxonomy.

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Knowledge-graph map

Learning to Learn
Mastered Ready to learn LockedHover a node for its description · click to open
Asking for HelpAsking for Help — frontier Ask for help when you've had a go yourself and are still stuck — knowing when to ask is a skill in itselfChecking Your Own WorkChecking Your Own Work — frontier After finishing a task, look back at what you did and ask yourself: does this seem right?Persisting When It's Ha…Persisting When It's Hard — frontier Keep trying when something feels hard — making mistakes and trying again is how learning happensFeeling of not understa…Feeling of not understanding — locked Notice the feeling of not understanding — recognise when something is confusing rather than reading or listening past itPlanning a TaskPlanning a Task — locked Make a simple plan before starting a task: what do I need to do, and what should I do first?Thinking Before StartingThinking Before Starting — locked Before starting something new, stop and think: what do I already know about this topic?Spotting PatternsSpotting Patterns — frontier Spot patterns and recurring structures — in numbers, words, nature, sounds, or events — and use them to make sense of new informationDescribing Rules & Patt…Describing Rules & Patterns — locked When you notice a pattern repeating, describe it as a rule that works every time — then test whether the rule holds in new casesLearning from MistakesLearning from Mistakes — locked When you get something wrong, investigate why — what did you misunderstand or overlook? Analysing errors is one of the most powerful ways to learnTransferring SkillsTransferring Skills — locked Recognise when a skill or strategy learned in one subject or situation can be applied in a completely different one
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AI and Fairness in Decisions

Computing · Artificial Intelligence · conceptual

Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness

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What mastery looks like

  • Give an example of an important decision that AI might help make (hiring, medical diagnosis, loan approval)
  • Explain why letting AI make decisions about people can be risky if the system is biased
  • Suggest who should be responsible if an AI system makes an unfair decision

Check your understanding

If AI and Fairness in Decisions learned that a computer program was deciding who gets a job interview, could they explain why that might be unfair and who should be responsible?

Prerequisites

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Helpful

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