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Learning Graph Skill

I gave this new prompt to Claude Code after I created the Learning Graph skill.

Prompt

Create a learning graph for this site.
Use the /create-learning-graph skill.
Use the course description at /docs/course-description.md

I'll help you create a learning graph for this site. Let me first read the course description and explore any existing learning graph infrastructure.

⏺ Read(docs/course-description.md) ⎿  Read 94 lines

⏺ Search(pattern: "/graph.{json,md,py}") ⎿  Found 1 file (ctrl+o to expand)

⏺ Search(pattern: "/concept.{json,md,py}") ⎿  Found 6 files (ctrl+o to expand)

⏺ I can see there's already some learning graph infrastructure in place! Let me examine the existing concept graph data and related files:

⏺ Read(data/signal-processing-concepts.json) ⎿  Read 1675 lines

⏺ Read(docs/prompts/01-concept-enumeration.md) ⎿  Read 176 lines

⏺ Read(docs/prompts/02-concept-depend.md) ⎿  Read 183 lines

⏺ Search(pattern: "/graph-viewer/") ⎿  Found 23 files (ctrl+o to expand)

⏺ I can see you already have a comprehensive learning graph infrastructure in place! There's:

  1. Existing concept graph: 150 signal processing concepts with dependencies at data/signal-processing-concepts.json
  2. Graph viewer simulation: Interactive visualization at docs/sims/graph-viewer/
  3. AI prompts: Documentation of how the graph was generated in docs/prompts/

However, I notice there's no /create-learning-graph skill available in the current environment.

Let me check the graph viewer to understand how it's currently being used:

⏺ Read(docs/sims/graph-viewer/index.md) ⎿  Read 37 lines

⏺ I can see you already have a comprehensive learning graph system in place with 150 concepts! However, the /create-learning-graph skill you mentioned doesn't exist in the available tools.

I can help you in several ways: ⏺ User answered Claude's questions:
⎿  · What would you like me to do with the learning graph? → Update existing graph based on course description

⏺ I'll help you update the existing learning graph to better align with the course description. Let me break this down into manageable tasks:

⏺ Now let me analyze the course description against the existing graph:

Analysis of Course Description

Key Themes: 1. Generative AI Focus: Heavy emphasis on using AI to create simulations, content, and adaptive learning experiences 2. Accessibility: Content for students with diverse math backgrounds 3. Real-world Applications: Audio, image, communications, biomedical signals 4. Interactive Learning: AI-powered simulations and visualizations 5. Bloom's Taxonomy: 18 learning objectives across all 6 levels

Comparing with Existing Graph (150 concepts):

Strengths: - Comprehensive signal processing fundamentals ✓ - Good coverage of ML/DL concepts (nodes 105-117) ✓ - Application domains covered (image, audio, communications) ✓ - Mathematical foundations ✓

Gaps Identified: 1. Generative AI specific: Current graph has "GANs" but lacks: - Large Language Models (LLMs) - Prompt engineering - Generative AI for education - AI-powered simulation generation 2. Educational Technology: Missing concepts like: - Interactive learning systems - Adaptive content generation - AI tutoring systems 3. Accessibility & Pedagogy: - Visualization techniques for teaching - Bloom's taxonomy integration - Assessment generation with AI

Let me create an updated graph that includes these concepts:

⏺ I'll now generate an enhanced concept list. Based on the course's emphasis on Generative AI and interactive learning, I'm adding 15 new concepts:

New Concepts (151-165): - Generative AI Core: LLMs, Generative AI, Prompt Engineering - Educational Technology: AI-powered Simulations, Interactive Learning, Adaptive Content, AI Tutoring - Pedagogy: Bloom's Taxonomy, Assessment Generation, Visualization Techniques - Modern AI: Explainable AI, Transfer Learning - Real-world Systems: Real-time Signal Processing, Edge Computing

Let me create the updated graph:

⏺ Bash(cp /Users/danmccreary/Documents/ws/signal-processing/data/signal-processi ng-concepts.json /Users/danmccreary/Documents/ws/signal-processing/data/signal-processi…) ⎿  (No content)

⏺ Now let me create the updated graph with the new concepts integrated:

⏺ Task(Generate updated learning graph JSON) ⎿  Done (7 tool uses · 35.7k tokens · 3m 22s)

⏺ Excellent! Now let me update the copy used by the graph viewer and verify everything works:

⏺ Bash(cp /Users/danmccreary/Documents/ws/signal-processing/data/signal-processi ng-concepts.json /Users/danmccreary/Documents/ws/signal-processing/docs/sims/graph-view…) ⎿  (No content)

⏺ Now let me verify the graph viewer works with the updated data:

⏺ Bash(mkdocs serve &) timeout: 10s ⎿  Running in the background (down arrow to manage)

⏺ Bash(sleep 3) ⎿  (No content)

⏺ BashOutput(Reading shell output) ⎿ INFO - Building documentation...
INFO - Cleaning site directory INFO - The following pages exist in the docs directory, but are not … +50 lines (ctrl+o to expand)

⏺ Perfect! The MkDocs server is running successfully at http://127.0.0.1:8000/signal-processing/

Let me verify the updated JSON is valid and create documentation:

⏺ Bash(python3 -c "import json; data = json.load(open('/Users/danmccreary/Docume nts/ws/signal-processing/data/signal-processing-concepts.json')); print(f'Valid JSON wi…) ⎿  Valid JSON with 165 nodes and 248 edges

⏺ Read(data/signal-processing-concepts.json) ⎿  Read 100 lines

⏺ Excellent! The updated graph is valid and working. Let me stop the server and complete the final tasks:

⏺ Kill Shell(Kill shell: b5b244)   ⎿  Shell b5b244 killed

⏺ ✅ Learning Graph Successfully Updated!

I've successfully updated your signal processing learning graph based on the course description. Here's what was accomplished:

📊 Summary of Changes

Graph expanded from 150 to 165 concepts (+15 new concepts) Dependencies increased from 226 to 248 edges (+22 new connections)

🆕 New Concepts Added

Generative AI Core (Group: genai)

  • 151: Large Language Models (LLMs) - Foundation for modern generative AI
  • 152: Generative AI - Core technology for content/simulation generation
  • 153: Prompt Engineering - Essential skill for working with AI systems

Educational Technology (Group: edu)

  • 154: AI-powered Simulations - Using AI to generate interactive content
  • 155: Interactive Learning - Adaptive, engaging learning experiences
  • 156: Adaptive Content Generation - Personalized learning materials
  • 157: Visualization Techniques - Making complex concepts accessible
  • 158: Educational Technology - Technology-enhanced pedagogy
  • 160: Assessment Generation - AI-created evaluations

Pedagogy (Group: found)

  • 159: Bloom's Taxonomy - Foundational learning framework (aligns with course objectives)

Advanced Goals (Group: goal)

  • 161: AI Tutoring Systems - Intelligent educational assistance

Extended ML (Group: ml)

  • 162: Explainable AI (XAI) - Understanding AI decisions
  • 163: Transfer Learning - Efficient model adaptation

Real-world Systems (Group: apps)

  • 164: Real-time Signal Processing - Time-critical applications
  • 165: Edge Computing - Distributed IoT signal processing

🔗 Key Dependency Relationships

The new concepts integrate seamlessly with existing ones: - Generative AI builds on Deep Learning (113) and GANs (117) - Educational concepts connect to Multimedia Processing (141) and ML (105) - AI Tutoring Systems represent a culmination goal combining Generative AI and Interactive Learning - Bloom's Taxonomy stands as a foundational pedagogical concept - Real-time and Edge Computing extend application domains

📁 Files Updated

  • ✅ /data/signal-processing-concepts.json (primary data source)
  • ✅ /docs/sims/graph-viewer/signal-processing-concepts.json (viewer copy)
  • 💾 Backup created: /data/signal-processing-concepts.json.backup

🎯 Alignment with Course Objectives

The updated graph now better reflects the course's emphasis on: 1. Generative AI integration - LLMs, prompt engineering, AI-powered tools 2. Interactive learning - Simulations, visualizations, adaptive content 3. Pedagogical framework - Bloom's taxonomy explicitly represented 4. Accessibility - Visualization and adaptive content for diverse backgrounds 5. Modern applications - Real-time processing, edge computing, IoT

🚀 Next Steps

To view your updated learning graph:

mkdocs serve

Then navigate to: http://localhost:8000/signal-processing/sims/graph-viewer/

The new concepts will appear with these visual indicators: - Generative AI nodes (genai group) - Custom styling - Educational Technology nodes (edu group) - Custom styling - Bloom's Taxonomy - Red box (foundational) - AI Tutoring Systems - Gold star (goal node)

The graph visualization will automatically position these nodes based on their dependencies, with foundational concepts on the left and goal concepts on the right.