layer-definitions_skill

Overview

This skill provides clear L1–L5 pedagogical layer definitions tailored for the AI-Native Robotics Textbook. It helps instructors assign appropriate layers to lessons, validate progression, and ensure AI involvement matches learning goals. Use it to maintain consistency across curriculum content and to communicate expectations to students.

How this skill works

The skill inspects content for AI involvement, learning objectives, and required student actions, then maps the content to one of five layers: Manual (L1) through Full Autonomy (L5). It verifies that lower-layer prerequisites are present and that the progression from one layer to the next is logical and teachable. The output is a recommended layer with rationale and checklist items for prerequisite knowledge and tooling.

When to use it

• Assigning a pedagogical layer to a new lesson, module, or exercise
• Validating that course progression enforces prerequisite knowledge
• Designing assessments to match expected student roles and AI involvement
• Refining project briefs to specify AI autonomy and student responsibilities
• Reviewing curriculum for consistent AI-integration across topics

Best practices

• Start modules at the lowest layer needed to teach foundational concepts before adding AI tools
• Document prerequisites from lower layers explicitly when promoting content to a higher layer
• Use concrete rubrics that map student actions to layer expectations (e.g., manual tasks vs. orchestration)
• Prefer incremental progression: L1 → L2 → L3 → L4 → L5 rather than jumping layers
• Align assessments with the student role defined by the layer (hands-on, assisted, templated, guided, autonomous)

Example use cases

• Labeling a lecture on sensor fundamentals as L1 to ensure students build mental models without AI
• Classifying a lab where students use AI suggestions as L2 for collaborative exploration
• Designing a template-based coding exercise as L3 that teaches reusable AI-driven patterns
• Creating a system-integration assignment as L4 where students supply specs and AI generates components
• Defining a capstone where teams orchestrate multiple agents as L5 for end-to-end autonomy