A learning pathway across K–12

AI literacy.
Careful claims.
Responsible agents.

Start with models and examples, learn to evaluate outputs, then investigate AGI claims and systems that use tools. Grade bands are starting points; adjust support and prerequisites to the learner.

K–2Introduction to AI HelpersExamples, mistakes, evidence and asking an adult for help · B10→ 3–5AI Helpers & How They WorkTraining data, bias and checking outputs · E9→ 6–8AI Literacy and FluencyTraining, inference, AGI claims and checking evidence · MS5→ 9–12AI Engineering and AI SecurityAgentic AI topics: tools, permissions, workflows and safeguards · HS8→ 9–12AI Engineering for Software WorkPrompts, output evaluation, guardrails and an engineering challenge · CL7→

Where AGI and agentic AI fit

In MS5, learners separate training from inference and distinguish observed capabilities from debated AGI claims. In HS8, learners map AI systems, design bounded workflows, evaluate tool permissions, recognize prompt injection, and explain human review and security controls. These topics are integrated into the existing modules.

Start with the grade-appropriate AI foundations, then use HS8 for advanced systems and security. CL7 applies AI evaluation in the coding pathway. The supplied fictional cases support paper diagrams and private explanations; follow the lesson’s activity and assessment criteria. A workflow plan or simulated trace is evidence of reasoning, not proof of operating a live autonomous agent.

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