Unit 1: What Is AI?

Compare humans and machines. Spot AI in the world around us.

EQ: How are people and computers different when they make decisions?

Unit 2: Fair Helpers

Help and harm; sharing data carefully; kindness with tools.

EQ: When is a computer helper fair?

Unit 1: Models and Data

What a model is, how examples teach a computer, and why bad data misleads.

EQ: How do examples teach a computer to classify new things?

Unit 2: Sensors and Perception

How computers ‘see’ and ‘hear’ with sensors and patterns.

EQ: How does a computer turn light or sound into a useful decision?

Unit 3: Design for People

Ethical design loop and simple model cards for kid projects.

EQ: How do we design AI tools that consider everyone affected?

Unit 1: The Machine Learning Pipeline

From question to data to model to evaluation — and back again.

EQ: What steps turn a real-world question into a tested model?

Unit 2: Recommenders & Attention

How feeds rank content and what that means for agency.

EQ: Who shapes what you see next — and how do you stay in charge?

Unit 3: Build With Ethics

Create a small AI-assisted project with a model card and evaluation plan.

EQ: How do we ship something useful without shipping harm?

Unit 4: AI as a Work Tool

Use generative AI for learning and projects without outsourcing your brain — specs, verification, privacy, and agency.

EQ: How do I get real help from AI tools without becoming dependent, exposed, or wrong?

Unit 1: Intelligence, Representation & Reasoning

What we mean by AI systems; how representation shapes reasoning.

EQ: How do the representations we choose constrain what an AI system can do?

Unit 2: Generative AI Literacy

How genAI works at a systems level; verification, IP, integrity, and policy.

EQ: How should students and citizens work with generative systems responsibly?

Unit 3: Specialization Pathways

Depth tracks: neural nets, NLP intuition, vision ethics, public AI audit capstone.

EQ: Where do you want to go deeper?

Unit 4: Operator Skills

The high-school capstone skill stack: prompt systems, hallucination control, threshold decisions, and a personal AI playbook you can use at work and college.

EQ: What does it mean to operate AI systems competently — not just consume them?

Unit 1: Foundations & Evaluation

Formalize learning problem types, risk, validation, and reporting.

EQ: How do we evaluate AI systems rigorously enough to make institutional decisions?

Unit 2: Technical Track

For CS majors/minors and advanced dual-enroll: loss, nets, eval harnesses, capstone.

EQ: How do we go from literacy to building?

Unit 3: Professional AI Practice

Workplace-ready practice: evaluation harnesses, go/no-go memos, prompt systems at org scale, and refuse lines that survive contact with incentives.

EQ: How do professionals decide when AI systems are good enough — and when to refuse?

Unit 1: The FDE Mandate

What Forward Deployed Engineers actually do: embed with customers, convert ambiguity into working systems, and feed product.

EQ: How does an FDE create value when the customer cannot yet specify the product?

Unit 2: Claude Platform Fluency

Production patterns: prompts as systems, tools & structured outputs, cost/context, safety rails that survive incentives.

EQ: How do you design model-using systems that stay correct, cheap, and governable under load?

Unit 3: MCP & Integration Surfaces

Model Context Protocol as the field integration layer: resources, tools, transports, auth, multi-tenant judgment.

EQ: How do you expose just enough of a customer system to the model — safely, testably, and portably?

Unit 4: Agents & Orchestration

Pipelines over single calls: sub-agents, escalation gates, tool loops, and when not to agent-ify.

EQ: When should intelligence be a pipeline with gates rather than one chat completion?

Unit 5: Evals, Reliability & Ship

Golden sets, CI gates, observability, multi-tenant deploy judgment, and the FDE portfolio capstone.

EQ: What evidence justifies shipping — and what evidence demands a rollback?