Before you start
Think it through before you train
Most popular
Train-a-Classifier Show an AI labelled examples and watch it discover a rule — no coding. See the rule it learned and why it decides. 
Bias Detector Inspect a skewed dataset, name the bias, and fix it — where AI goes unfair, and how to catch it. 
Train / Test Split Test the AI on examples it has never seen — the honest way to know if it really learned. 
Adventure Map A path through all 16 kits — clear each stop, then move on. All activities
Build & inspect AI
Train-a-Classifier Teach the model to sort examples, then see the rule it learned. Bias Detector Inspect a skewed dataset, name the bias, and fix it. Feature Picker Pick the clue that sorts the examples most cleanly. Confidence Meter Decide: let the AI call it, or send it to a human? Train / Test Split Test the AI on examples it has never seen.
Learn & practice
Concept kits 16 kits from "what is AI" to responsible-AI synthesis — a hint whenever you get stuck. Mixed Practice A short round shuffling questions from the kits you have played — spaced out so it sticks. AIForge Sprint Race your OWN best on what you already know. Timer optional, no penalty for a miss. Trick Questions Brain-teasers with a twist — spot why the obvious answer is a trap. Teach the Cast Teach an AI idea to a cast member still learning it — teaching makes it stick. Progress Your levels, streak, and how each topic is coming along — saved on this device. Progress report For grown-ups — a private on-device snapshot mapped to the skill areas. Export a CSV or print.
Play together
Concept Kits — AI literacy, kit by kit
Kit 1: What Is Artificial Intelligence? What is AI · 25 questions Kit 2: How Machines Learn: Patterns, Data, and Models Machine learning · 25 questions Kit 3: AI Ethics & Bias: Fairness in Algorithms Ethics & bias · 25 questions Kit 4: Data Privacy & Digital Citizenship Privacy & digital citizenship · 25 questions Kit 5: Computer Vision & Image AI Computer vision · 25 questions Kit 6: Natural Language Processing & Language AI Language AI · 25 questions Kit 7: AI and the Future of Work & Society AI & society · 25 questions Kit 8: Building Responsible AI Systems Responsible AI · 25 questions Kit 9: Machine Learning Basics Machine learning · 25 questions Kit 10: AI Ethics & Bias Ethics & bias · 25 questions Kit 11: Natural Language Processing Language AI · 25 questions Kit 12: Computer Vision Fundamentals Computer vision · 25 questions Kit 13: Cross-Topic Connections Review & synthesis · 25 questions Kit 14: Real-World Applications Real-world · 25 questions Kit 15: Misconceptions & Reasoning Reasoning · 25 questions Kit 16: Advanced Synthesis Review & synthesis · 25 questions
More ways to play
Same idea, another world
😄 Brain break
Meet the cast — the characters who teach this
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Sort Classifier — the simplest ML; putting things in categories -
Feed Training data — the examples a model learns from; garbage-in-garbage-out -
Skew Bias — where AI systems go wrong when training examples lean -
Edge Model limitations — what a model can't do; modeling 'I don't know' as a good answer -
Stake Ethics — what's at stake in deploying AI; people choosing, not rules-from-the-sky -
Split Train/test split — keep some examples hidden to tell learning from memorizing -
Cue Features — a model decides from the clues you give it; choose good clues -
Sure Confidence — a model reports how sure it is; low confidence means check, not trust -
Mirage Hallucination — when a model confidently makes something up that sounds true but isn't; check, don't just trust -
Rote Overfitting — when a model memorizes the exact examples instead of learning the general idea
Ensemble stories — the cast together