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
Available nowWhat is a smart machine? (notice & decide)
Licensing: Included in all licenses.
| 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.AP.1K.CSY.12.AP.12.IC.1 |
| U2 | Patterns Everywhere | Patterns let us predict what comes next — the everyday face of how machines guess. | 1.AP.12.AP.11.DA.22.DA.1 |
| U3 | Teach the Machine | Machines learn from the examples we show them; bad examples make wrong guesses. | K.AP.12.AP.1K.DA.22.DA.1 |
| U4 | Being Fair and Safe with Smart Machines | Fair examples, private information, and honesty — people decide what's right. | K.CYB.22.CYB.2K.CYB.12.CYB.1 |
3-5 How Computers Think
Available nowFrom switches to instructions (the machine underneath, gently)
Licensing: Included in School and Division licenses.
| 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. | 3.CSY.14.DA.15.CSY.1 |
| U2 | Algorithms: Recipes a Computer Follows | Step-by-step instructions, loops, and debugging. | 3.AP.13.AP.34.AP.25.AP.3 |
| U3 | Data, Sorting, and Smart Guesses | Computers find patterns in data to make predictions. | 3.DA.24.DA.24.CSY.35.DA.3 |
| U4 | Good Data, Fair Results | Where data comes from, why it can be biased, and responsible use. | 3.DA.14.DA.15.DA.33.IC.1 |
6-8 The Machine Underneath
Available nowMachine learning & neural networks (how it actually learns)
Licensing: Included in School and Division licenses.
| Unit | Title | Focus | Sample VA CS SOL |
|---|---|---|---|
| U1 | From Silicon to Chips | Transistors, logic gates, and how hardware computes. | 6.AP.26.CSY.28.CSY.1 |
| U2 | What Machine Learning Really Is | Training data, models, features, and generalization. | 6.CSY.36.DA.47.DA.28.DA.2 |
| U3 | Neural Networks by Hand | Neurons, weights, and layers — an unplugged-to-code build. | 7.DA.28.DA.18.AP.17.AP.2 |
| U4 | Bias, Data Quality, and Evaluation | Measuring whether a model is good — and fair. | 8.DA.26.CSY.36.DA.46.DA.1 |
| U5 | Security, Privacy, and Digital Citizenship | Protecting data and using systems responsibly. | 6.CYB.17.CYB.18.CYB.18.IC.1 |
| U6 | AI at Work | How AI changes industries and jobs — and the real routes into them. | 6.IC.67.IC.56.IC.37.IC.38.IC.3 |
9-12 Modern AI: How It Actually Works
Available nowModern LLMs, agents, ethics, IP & careers
Licensing: Included in current Division licenses at no extra cost; no re-procurement required.
Virginia does not define grade-level computer science standards for grades 9-12 — it defines three courses. Units below are mapped to the course they belong in. Individual standard codes are assigned at authoring, once the course standards are transcribed from the VDOE document; only codes verified against that document are shown.
- CSF Computer Science Foundations — Primary home for the 9-12 arc — CSF carries the explicit neural-network and machine-learning standards.
- CSP Computer Science Principles — Impacts, ethics, IP and careers — CSP has the heaviest Impacts of Computing strand.
- PRG Computer Science Programming — Data structures and implementation. Claimed only where genuinely taught.
| Unit | Title | Focus | Course | Verified VA CS SOL |
|---|---|---|---|---|
| U1 | Representation: Numbers, Vectors, Embeddings | How meaning becomes math. | CSF | CSF · codes pending VDOE transcription |
| U2 | Training Deep Networks | Gradient descent, loss, and overfitting. | CSF | CSF.AP.5CSF.CSY.4 |
| U3 | Transformers and Attention | The architecture behind modern models. | CSF | CSF.AP.5 |
| U4 | Large Language Models | Pretraining, tokens, and emergent behavior. | CSF | CSF.CSY.4 |
| U5 | Prompting and Context | Getting reliable results from a model. | CSF | CSF · codes pending VDOE transcription |
| U6 | Caching, Cost, and Latency | Engineering AI systems that scale. | PRG | PRG · codes pending VDOE transcription |
| U7 | Tools and Agents | Models that act: tool use, planning, and loops. | PRG | PRG · codes pending VDOE transcription |
| U8 | Retrieval and Grounding | Connecting models to real, current data. | PRG | PRG · codes pending VDOE transcription |
| U9 | Ethics and Bias at Scale | Harm, fairness, and accountability. | CSP | CSP · codes pending VDOE transcription |
| U10 | Intellectual Property and Authorship | Who owns AI-assisted work; attribution and integrity. | CSP | CSP · codes pending VDOE transcription |
| U11 | Safety, Society, and AI Careers | Alignment, policy, and the jobs this creates. | CSP | CSP · codes pending VDOE transcription |
Coverage map
Standards addressed by band
Distinct VA CS SOL codes each band’s units address. K-2, 3-5 and 6-8 are live and verifiable in the standards crosswalk. The 9-12 units are authored, but their per-code mapping is still draft: Virginia defines 9-12 by course, and only codes confirmed against the VDOE document are shown.
| Band | Units | Distinct SOL codes |
|---|---|---|
| K-2 Smart Machines Around Us | 4 | 12 |
| 3-5 How Computers Think | 4 | 13 |
| 6-8 The Machine Underneath | 6 | 20 |
| 9-12 Modern AI: How It Actually Works | 11 | 2 (draft) |
All four bands — K-2, 3-5, 6-8 and 9-12 — are authored and available now, and 9-12 is included in current Division licenses at no extra cost. The 9-12 standards crosswalk is still being transcribed from the VDOE course documents; the units ship regardless. The K-2 band is free to preview.