layer-definitions

zeeshan080's avatarfrom zeeshan080

Provide L1-L5 pedagogy layer reference for the AI-Native Robotics Textbook. Use when assigning layers to content, understanding layer requirements, or validating layer progression.

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[Lesson Planning]

When & Why to Use This Skill

This Claude skill provides a comprehensive L1-L5 pedagogical framework specifically designed for the AI-Native Robotics Textbook. It enables educators and content creators to systematically define, assign, and validate the level of AI involvement in educational content, ensuring a logical progression from manual foundational learning to full autonomous AI orchestration.

Use Cases

  • Curriculum Development: Assigning appropriate pedagogical layers (L1-L5) to robotics course modules to balance manual skill-building with AI-assisted learning.
  • Content Validation: Auditing existing textbook chapters to ensure that prerequisites from lower layers are met before advancing to more complex AI-driven tasks.
  • Instructional Design: Defining the specific roles of students and AI agents for robotics projects, ranging from assisted collaboration to spec-driven generation.
  • Pedagogical Alignment: Ensuring that mental models and foundational concepts are taught manually (L1) before introducing AI templates or autonomous workflows (L3-L5).
namelayer-definitions
descriptionProvide L1-L5 pedagogy layer reference for the AI-Native Robotics Textbook. Use when assigning layers to content, understanding layer requirements, or validating layer progression.
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Layer Definitions

Instructions

When determining pedagogical layers:

  1. Assess the content's AI involvement level
  2. Match to the appropriate layer (L1-L5)
  3. Ensure prerequisites from lower layers are met
  4. Validate layer progression is logical

Layer Overview

Layer Name AI Involvement Student Role
L1 Manual None Full manual work
L2 Collaboration Assisted AI helps after understanding
L3 Intelligence Templated Using AI templates/skills
L4 Spec-Driven Guided AI generates from specs
L5 Full Autonomy Autonomous AI-driven end-to-end

Layer Selection Guide

Choose L1 when:

  • Teaching foundational concepts
  • Student must understand without AI assistance
  • Building mental models

Choose L2 when:

  • Student understands the concept
  • AI can provide extensions or variations
  • Collaboration enhances learning

Choose L3 when:

  • Teaching reusable patterns
  • Introducing AI templates and skills
  • Building on L1-L2 understanding

Choose L4 when:

  • Working with specifications
  • AI generates implementation from design
  • Integration of multiple components

Choose L5 when:

  • End-to-end autonomous workflows
  • Student orchestrates AI agents
  • Capstone projects

Reference

See layers.md for detailed layer descriptions.