Scope & sequence · grades K-12
One idea, taught for thirteen years
From Sand to Agents — K-12 Artificial Intelligence follows a single spiraled throughline. Nothing taught early is ever undone — each band revisits the same core ideas with more depth and more rigor.
silicon → chips → machine learning → neural networks → modern LLMs → prompting, caching & agents → ethics, IP, safety & careers
K-2 Smart Machines Around Us
authoredWhat is a smart machine? (notice & decide)
| Unit | Title | Focus | Sample VA CS SOL |
|---|---|---|---|
| U1 | Smart Machines Around Us | Machines that 'notice and decide' vs. machines that just do their job. | K.4K.52.52.13 |
| U2 | Patterns Everywhere | Patterns let us predict what comes next — the everyday face of how machines guess. | 1.12.11.112.11 |
| U3 | Teach the Machine | Machines learn from the examples we show them; bad examples make wrong guesses. | K.42.5K.92.11 |
| U4 | Being Fair and Safe with Smart Machines | Fair examples, private information, and honesty — people decide what's right. | K.82.10K.102.142.6 |
3-5 How Computers Think
plannedFrom switches to instructions (the machine underneath, gently)
| Unit | Title | Focus | Sample VA CS SOL |
|---|---|---|---|
| U1 | Inside the Box: Switches and Signals | Everything a computer does is built from tiny on/off switches. | mapped at authoring |
| U2 | Algorithms: Recipes a Computer Follows | Step-by-step instructions, loops, and debugging. | mapped at authoring |
| U3 | Data, Sorting, and Smart Guesses | Computers find patterns in data to make predictions. | mapped at authoring |
| U4 | Good Data, Fair Results | Where data comes from, why it can be biased, and responsible use. | mapped at authoring |
6-8 The Machine Underneath
plannedMachine learning & neural networks (how it actually learns)
| Unit | Title | Focus | Sample VA CS SOL |
|---|---|---|---|
| U1 | From Silicon to Chips | Transistors, logic gates, and how hardware computes. | mapped at authoring |
| U2 | What Machine Learning Really Is | Training data, models, features, and generalization. | mapped at authoring |
| U3 | Neural Networks by Hand | Neurons, weights, and layers — an unplugged-to-code build. | mapped at authoring |
| U4 | Bias, Data Quality, and Evaluation | Measuring whether a model is good — and fair. | mapped at authoring |
| U5 | Security, Privacy, and Digital Citizenship | Protecting data and using systems responsibly. | mapped at authoring |
9-12 Modern AI: How It Actually Works
plannedModern LLMs, agents, ethics, IP & careers
| Unit | Title | Focus | Sample VA CS SOL |
|---|---|---|---|
| U1 | Representation: Numbers, Vectors, Embeddings | How meaning becomes math. | mapped at authoring |
| U2 | Training Deep Networks | Gradient descent, loss, and overfitting. | mapped at authoring |
| U3 | Transformers and Attention | The architecture behind modern models. | mapped at authoring |
| U4 | Large Language Models | Pretraining, tokens, and emergent behavior. | mapped at authoring |
| U5 | Prompting and Context | Getting reliable results from a model. | mapped at authoring |
| U6 | Caching, Cost, and Latency | Engineering AI systems that scale. | mapped at authoring |
| U7 | Tools and Agents | Models that act: tool use, planning, and loops. | mapped at authoring |
| U8 | Retrieval and Grounding | Connecting models to real, current data. | mapped at authoring |
| U9 | Ethics and Bias at Scale | Harm, fairness, and accountability. | mapped at authoring |
| U10 | Intellectual Property and Authorship | Who owns AI-assisted work; attribution and integrity. | mapped at authoring |
| U11 | Safety, Society, and AI Careers | Alignment, policy, and the jobs this creates. | mapped at authoring |
Planned bands (3-5, 6-8, 9-12) show the intended arc; unit titles are finalized when each band is authored. The K-2 band is complete and free to preview.