Concept Graph Prompt

Run The Concept Graph MicroSim Fixed Positions

A concept graph or learning graph is a directed graph that lists all of the concepts taught in a typical course or curriculum.

Sample Prompt

I am teaching a course to high-school teachers on how
to use generative AI to create small educational
simulations for a specific course or group of
students.  This course is called
 "Teaching with GenAI and MicroSims".

Please create a numbered list of the top 150 Concepts
we need to teach in this course.  Have one Concept
Label per line and use a Concept Label that is short
so it can be viewed in a graph network.  Feel free
to use abbreviations within the Concept Label.

This list should be sorted from Fundamental
Concepts first to the most advanced Concept
last.

Make sure not to duplicate Concepts.

The course learning objectives include:

1. How to create detailed high-quality generative AI prompts.
2. How to generate web-based simulations using JavaScript libraries such as ChatGPT or vis.js.
3. How to get ChatGPT to use templates for high-quality and consistent user interfaces.
4. How to save and test simulations using tools such as the p5.js editor
5. How to deploy these simulations using standards such as markdown, mkdocs and GitHub pages.
6. How to get feedback from students on how effective these
simulations are in the classroom.
7. How to use Google Analytics to see what MicroSims are being
used and how often.

ChatGPT o1-preview Response

GenAI Basics
MicroSim Concepts
MicroSim Benefits
AI Prompting Intro
Effective Prompts
Prompt Engineering
High-Quality Prompts
Avoid Ambiguous Prompts
AI Limitations
JS Fundamentals
JS Variables
JS Operators
JS Control Structures
JS Functions
JS Objects & Arrays
DOM Manipulation
Event Listeners
vis.js Intro
Networks with vis.js
vis.js Visuals
Animation in vis.js
p5.js Intro
Drawing in p5.js
p5.js Animation
Interactivity p5.js
p5.js Editor
Save p5.js Projects
Test Simulations
Debugging Techniques
ChatGPT for Code
ChatGPT Code Gen
Templates in ChatGPT
Apply UI Templates
Consistent UI
Save AI Code
Git Version Control
Using GitHub
GitHub Pages
Deploy Simulations
Markdown Intro
Writing Markdown
mkdocs Docs
Setup mkdocs Site
mkdocs on GitHub
Collect Feedback
Feedback Forms
Analyze Feedback
Improve Simulations
Google Analytics
Link Analytics
Interpret Usage Data
Engagement Metrics
Data-Driven Decisions
Privacy in Analytics
Ethical Student Data
Simulation Accessibility
Inclusive Design
Cross-Browser Tests
Mobile Responsive
Multi-Device Testing
Performance Optimize
Minimize Load Time
Image Optimize
Code Minify
Graceful Errors
Logging & Monitoring
Security Practices
Avoid Vulnerabilities
Open Source Licenses
Legal Library Use
Attribution & Licensing
Customize Open Source
Advanced Prompts
System Prompts
AI Output Variability
Rephrase Prompts
Chain-of-Thought
AI Debugging
Fine-Tune Sims
API Integration
Fetch API Data
JSON Handling
Real-Time Data
Advanced p5.js
p5.js 3D Graphics
Sound in p5.js
Advanced vis.js
vis.js Clustering
vis.js Hierarchy
D3.js Intro
vis.js vs D3.js
Library Selection
JS Performance
Memory Management
JS Event Loop
Async JS
Promises & Async/Await
Async Error Handling
Modular JS
ES6 Modules
Babel Transpile
Webpack Setup
Code Bundling
Source Maps
NPM Packages
Publish NPM Package
Reusable Components
Component Libraries
CSS Styling
CSS Preprocessor
Bootstrap CSS
Responsive Design
CSS Media Queries
Flexbox
CSS Grid
HTML Accessibility
ARIA Roles
Screen Reader Tests
Write Documentation
README Best Practices
Code Comments
Repo Maintenance
GitHub Collaboration
Pull Requests
Issue Tracking
Continuous Integration
GitHub Actions CI
Automated Testing
Jest Unit Tests
Integration Tests
Cypress E2E Tests
Deploy to Netlify
Deploy to Vercel
Env Variables
Secure API Keys
OAuth API Access
GDPR Compliance
Data Protection
AI Ethics Edu
Avoid AI Bias
Promote Fairness
Handle Sensitive Topics
AI Transparency
Teach AI to Students
Student Data Privacy
FERPA Compliance
Online Safety
Digital Footprint
Future GenAI Edu
Lifelong EdTech Learning

Concept Dependency Prompt

```linenums Our next step is to create a fully connected dependency graph. In this step, for each concept on this list, create a new list of other concepts on which it depends. If a concept depends on a specific concept that is not listed, then we will need to add it as a new concept. This will help us determine the teaching order of the concepts.

Return the new list as a CSV file using the following format. The first column is a numeric concept ID you assign, the second column is the concept name, and the third column is a pipe-delimited list of the concept IDs that the concept depends upon.

Check your results to make sure that all concepts are connected to at least one other concept.