Visit the interactive guidebook at: https://rtanler.github.io/Dementia/
This is an AI-generated intelligent textbook on dementia and cognitive decline designed for patients, caregivers, families, and healthcare professionals without specialized dementia training. Built using MkDocs with the Material theme, it incorporates a comprehensive learning graph with 200 interconnected concepts, interactive visualizations, and evidence-based content generation.
The guidebook follows Bloom's Taxonomy (2001 revision) for learning outcomes and uses concept dependency graphs to ensure proper prerequisite sequencing. Written at a 9th-10th grade reading level, it makes complex medical concepts accessible through clear language, diagrams, and real-world examples. All content is generated and curated using Claude AI and Claude Skills, making it a modern, AI-assisted educational resource.
Whether you're a patient newly diagnosed with dementia, a family member becoming a caregiver, or a healthcare provider seeking structured educational materials, this guidebook provides comprehensive coverage from basic neuroscience to practical daily care strategies, legal planning, and emotional support resources.
We track the status of book development tasks on a GitHub Projects Kanban board:
https://github.com/users/dmccreary/projects/10/
The board uses the following columns to organize work:
| Column | Purpose |
|---|---|
| Backlog | Tasks that have been identified but not yet started. New ideas, planned chapters, MicroSims, or improvements get added here while they wait to be prioritized. |
| Ready | Tasks that have been refined, prioritized, and are ready to be picked up. The scope is clear enough that work can begin without further clarification. |
| In progress | Tasks that are actively being worked on. Content is being drafted, code is being written, or a MicroSim is being built. |
| In review | Tasks where the work is complete and is being reviewed for accuracy, readability, accessibility, or technical quality before being marked done. |
| Done | Tasks that have been completed, reviewed, and merged or published to the live site. |
In addition to the status columns, each task can be tagged with a Priority and a Size to help with planning and triage:
| Priority | Meaning |
|---|---|
| P0 | Critical — must be addressed immediately. Blocks the site, breaks the build, or corrects a serious content error. |
| P1 | High — important work that should be done in the current development cycle. |
| P2 | Normal — useful improvements and enhancements that can be scheduled when capacity allows. |
| Size | Meaning |
|---|---|
| XS | Trivial — a quick fix such as a typo, link correction, or small wording change. |
| S | Small — a focused change that touches one file or a single concept. |
| M | Medium — a moderate task such as drafting a short chapter section or building a simple MicroSim. |
| L | Large — a significant task such as writing a full chapter or building a complex MicroSim. |
| XL | Extra large — a multi-step initiative that may need to be broken into smaller tasks before work begins. |
| Metric | Count |
|---|---|
| Concepts in Learning Graph | 200 |
| Foundational Concepts | 4 |
| Taxonomy Categories | 12 |
| Average Dependencies per Concept | 1.25 |
| Maximum Learning Path Length | 8 |
| Chapters | 15 |
| Total Chapter Words | ~109,000 |
| Markdown Files | 69 |
| Interactive MicroSims | 2 |
| Glossary Terms | 200 |
| FAQ Questions | 80 |
| Quiz Questions | 150 (10 per chapter) |
| Annotated References | 150 (10 per chapter) |
| Mascot Images | 7 (Tokie) |
Completion Status: Learning graph, all 15 chapters, glossary, FAQ, chapter quizzes, annotated references, mascot, and Google Analytics complete. Content is now in review before publication. Next focus: integrating the mascot into chapter content, building additional MicroSims, and developing interactive infographic drawings of the brain.
Learning Graph Quality Score: The concept dependency graph has been validated as a proper Directed Acyclic Graph (DAG) with no circular dependencies, ensuring learners encounter concepts in the correct order.
- 200 interconnected concepts covering all aspects of dementia from neuroscience to daily care
- 12 taxonomy categories: Foundation, Neuroscience, Dementia Types, Symptoms & Stages, Diagnosis & Assessment, Treatment & Therapies, Risk Factors & Prevention, Daily Living & Caregiving, Communication, Safety & Home Modifications, Legal & Financial Planning, and Support & Resources
- Interactive graph viewer with search, filtering, and visual exploration using vis-network
- Dependency tracking ensures concepts are introduced with proper prerequisites
- Structured learning paths following concept dependencies
- Bloom's Taxonomy alignment from knowledge recall to evaluation and creation
- Interactive MicroSims for visualizing concepts and exploring relationships
- Concept-based glossary following ISO 11179 metadata standards
- Chapter quizzes (planned) aligned to specific learning objectives
- FAQ section (planned) addressing common questions and concerns
- 9th-10th grade reading level for maximum accessibility
- Medical terminology explained in plain language
- Visual diagrams and infographics to support understanding
- Multiple usage modes: sequential reading, reference guide, or structured course
- Responsive design works on desktop, tablet, and mobile devices
git clone https://github.com/rtanler/Dementia.git
cd DementiaThis project uses MkDocs with the Material theme. You can use either pip or conda:
Using pip:
pip install mkdocs
pip install mkdocs-material
pip install -e . # Install social_override pluginUsing conda (recommended):
conda create -n mkdocs python=3.11
conda activate mkdocs
conda install -c conda-forge mkdocs mkdocs-material
pip install -e . # Install social_override pluginBuild the site:
mkdocs buildServe locally for development (with live reload):
mkdocs serveOpen your browser to http://localhost:8000/Dementia/
mkdocs gh-deployThis will build the site and push it to the gh-pages branch.
Navigation:
- Use the left sidebar to browse sections
- Click the search icon (🔍) to search all content
- Access the interactive Learning Graph Viewer under MicroSims
Learning Graph Viewer:
- Visual exploration of all 200 concepts and their dependencies
- Search for specific concepts
- Filter by taxonomy category (Foundation, Neuroscience, etc.)
- Click nodes to see connections and prerequisites
- Zoom and pan to explore the knowledge structure
Customization:
- Edit markdown files in
docs/to modify content - Modify
mkdocs.ymlto change site structure and navigation - Customize theme colors in
docs/css/extra.css - Add custom MicroSims in
docs/sims/
Dementia/
├── docs/ # MkDocs documentation source
│ ├── index.md # Homepage
│ ├── course-description.md # Comprehensive course overview
│ ├── chapters/ # Chapter content (in development)
│ ├── sims/ # Interactive MicroSims
│ │ ├── index.md # MicroSims overview
│ │ └── graph-viewer/ # Learning graph visualization
│ │ ├── main.html # Standalone viewer application
│ │ ├── local.css # Viewer styles
│ │ ├── script.js # vis-network integration
│ │ └── index.md # Viewer documentation
│ ├── learning-graph/ # Learning graph data and analysis
│ │ ├── learning-graph.csv # Concept dependencies (source)
│ │ ├── learning-graph.json # vis-network format
│ │ ├── color-config.json # Taxonomy color scheme
│ │ ├── concept-list.md # All 200 concept labels
│ │ ├── concept-taxonomy.md # Taxonomy definitions
│ │ ├── quality-metrics.md # Graph quality analysis
│ │ ├── taxonomy-distribution.md # Category distribution report
│ │ ├── add-taxonomy.py # Add taxonomy IDs to CSV
│ │ ├── analyze-graph.py # Graph structure validation
│ │ ├── csv-to-json.py # Convert CSV to vis-network JSON
│ │ ├── taxonomy-distribution.py # Generate distribution report
│ │ └── validate-learning-graph.py # DAG validation
│ ├── glossary.md # Terminology definitions
│ ├── faq.md # Frequently asked questions
│ ├── references.md # Curated references
│ ├── license.md # License details
│ ├── css/ # Custom stylesheets
│ ├── img/ # Images and icons
│ └── javascripts/ # Custom JavaScript (MathJax)
├── plugins/ # MkDocs plugins
│ └── social_override/ # Custom social media cards
├── mkdocs.yml # MkDocs configuration
├── setup.py # Python package setup (plugins)
└── README.md # This file
If you modify the learning graph:
- Edit
docs/learning-graph/learning-graph.csvwith concept changes - Run validation:
python docs/learning-graph/validate-learning-graph.py - Add taxonomy:
python docs/learning-graph/add-taxonomy.py - Convert to JSON:
python docs/learning-graph/csv-to-json.py - Analyze quality:
python docs/learning-graph/analyze-graph.py - Generate distribution:
python docs/learning-graph/taxonomy-distribution.py
Or use the automated validation script:
cd docs/learning-graph
./validate-learning-graph.shThe project follows this development sequence:
- ✅ Course Description - Complete with learning objectives
- ✅ Learning Graph - 200 concepts with dependencies validated
- ✅ Taxonomy & Color Scheme - 12 categories defined
- ✅ Interactive Viewer - Graph visualization complete
- ✅ Chapter Structure - 15 chapters aligned to learning graph
- ✅ Chapter Content - All 15 chapters drafted (~109,000 words)
- ✅ Glossary - 200 ISO 11179-compliant definitions
- ✅ Quizzes - 150 questions (10 per chapter) with Bloom's distribution
- ✅ FAQs - 80 questions across 10 categories
- ✅ Annotated References - 150 curated references (10 per chapter)
- ✅ Mascot (Tokie) - 7 mascot images with custom admonition styles
- ✅ Google Analytics - GA4 measurement integrated
- 🔄 Content Review - In review by Rick Tanler before publication
- ⏳ Mascot Integration - Adding Tokie admonitions throughout chapters
- ⏳ Additional MicroSims - Especially interactive brain infographics
- ⏳ Story Mini Graphic Novels - In planning
Found a bug, typo, or have a suggestion for improvement? Please report it:
When reporting issues, please include:
- Description of the problem or suggestion
- Steps to reproduce (for bugs)
- Expected vs actual behavior
- Screenshots (if applicable)
- Browser/environment details (for interactive MicroSims)
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — Give appropriate credit with a link to the original
- NonCommercial — Not for commercial use without permission
- ShareAlike — Distribute contributions under the same license
See LICENSE for full details.
This project is built on the shoulders of giants in the open source community:
- MkDocs - Static site generator optimized for project documentation
- Material for MkDocs - Beautiful, feature-rich responsive theme
- vis-network - Network visualization library for interactive learning graphs
- Python - Data processing, analysis, and automation tools
- Claude AI by Anthropic - AI-assisted content generation and curation
- Claude Skills - Reusable AI skill library for educational content
- GitHub Pages - Free hosting for open source educational projects
Special thanks to the educators, developers, healthcare professionals, and caregivers who contribute to making dementia education accessible, accurate, and compassionate.
Project Maintainer: rtanler
- GitHub: @rtanler
- Repository: github.com/rtanler/Dementia
Questions, suggestions, or collaboration opportunities? Feel free to open an issue on GitHub or reach out directly.
Built with ❤️ using Claude Code and Claude Skills