Learning Graph

View the Learning Graph

The learning graph is a directed acyclic graph (DAG) that maps the relationships between 200 concepts in the SEIS 666: Digital Transformation 2.0 with Generative AI course.

Graph Statistics

Metric	Value
Total Concepts	200
Foundational Concepts	4
Total Dependencies	294
Taxonomy Categories	12
Maximum Chain Length	14
Average Dependencies	1.50

Foundational Concepts

These concepts have no prerequisites and serve as entry points:

1. Digital Transformation - Core concept for the entire course
2. Artificial Intelligence - Foundation for all AI topics
3. Token - Fundamental unit for LLM operations
4. API Fundamentals - Basis for technical integration

Taxonomy Categories

Category	TaxonomyID	Color	Count
Foundation Concepts	FOUND	LightCoral	15
LLM Architecture	ARCH	PeachPuff	19
AI Platforms	PLAT	LightYellow	20
Prompt Engineering	PROMPT	Honeydew	18
Custom Solutions	CUSTOM	PaleGreen	17
API Integration	API	PaleTurquoise	17
Multimodal AI	MULTI	PowderBlue	17
Governance	GOV	Lavender	13
Ethics	ETHICS	LavenderBlush	23
Workforce	WORK	Thistle	13
Business Applications	BIZ	MistyRose	19
Advanced Topics	ADV	Plum	13

Interactive Viewer

Open Interactive Graph Viewer

The interactive viewer allows you to:

• Search and filter concepts by name
• Click nodes to see their dependencies
• Double-click to highlight connected concepts
• Switch between physics simulation and hierarchical layouts
• Filter by taxonomy category

Mermaid Diagrams

Visual flowcharts showing key learning paths are available in the Mermaid Diagrams page.

Related Documents

• Course Description Assessment - Quality analysis of the course description
• Concept List - Complete list of 200 concepts
• Quality Metrics - Graph structure validation
• Concept Taxonomy - Category definitions
• Taxonomy Distribution - Distribution analysis

Files in This Directory

File	Description
learning-graph.csv	Source data with dependencies and taxonomy
learning-graph.json	vis-network.js format for visualization
seis-666-learning-graph.json	Named copy for course-specific reference
metadata.json	Graph metadata (title, creator, etc.)
graph-viewer.html	Interactive vis-network.js graph viewer
learning-graph-mermaid.md	Mermaid diagram visualizations
analyze-graph.py	Quality validation script
csv-to-json.py	CSV to JSON conversion script
taxonomy-distribution.py	Distribution analysis script

Next Steps

1. Review the Interactive Graph Viewer to explore concept relationships
2. Generate chapter structure with book-chapter-generator skill
3. Create chapter content with chapter-content-generator skill