layer-definitions

# 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](layers.md) for detailed layer descriptions.