Adaptive Learning Skill

Create self-contained, browser-based adaptive learning apps from any course material.

Architecture

• FSRS (ts-fsrs): Per-card spaced repetition scheduling (Stability, Difficulty, Retrievability)
• BKT: Per-topic Bayesian Knowledge Tracing for mastery estimation
• Two modes: Breadth-first (cover all topics, weakest first) / Depth-first (drill one topic deep)
• Pure frontend: HTML + CSS + JS, works offline via file://, no server needed

Workflow

1. Gather Course Material

From a URL:

1. Fetch the course page, extract topic list and resource links
2. Download HW/Discussion/Lecture PDFs to ~/COURSE_NAME/

From a local folder:

1. List files, identify PDFs and documents
2. Read/parse to understand topics and content

2. Generate Question Bank

Create questions.json with this schema:

[{
  "id": "unique-id",
  "topic": "Topic Name",
  "topicIndex": 0,
  "difficulty": 1,
  "question": "Supports $LaTeX$ via KaTeX",
  "answer": "Supports $LaTeX$ and \\n for line breaks",
  "tags": ["tag1", "tag2"]
}]

Guidelines:

• 5-8 questions per topic minimum, across 3 difficulty levels
• Difficulty 1 (基础): Definitions, "what is", simple complexity questions
• Difficulty 2 (中等): Apply algorithms, analyze examples, describe procedures
• Difficulty 3 (高级): Proofs, novel problem design, optimization, "why" questions
• Use LaTeX ($...$ inline, $$...$$ block) for math
• Use \\n for line breaks in question/answer text
• topicIndex controls topic ordering (0-based)

3. Build the App

Run the bundler script:

bash SKILL_DIR/scripts/generate-course.sh <course-id> <questions.json> <output-dir>

Then register the course in engine.js COURSE_REGISTRY:

{ id: 'course-id', name: 'Course Name', desc: 'Description', school: 'School', term: 'Term' }

4. Verify

Open /index.html in a browser. Verify:

• Course appears in selector
• Cards render with KaTeX math
• Flip/rating/FSRS scheduling works
• Mode toggle (breadth/depth) works

Framework Files (assets/framework/)

Key Features

• FSRS scheduling: Cards show Stability/Difficulty values; review intervals adapt to performance
• BKT mastery: Per-topic mastery percentage in progress drawer
• Configurable: Target retention (70-97%), daily new card limit
• localStorage: All progress persists across sessions
• Keyboard shortcuts: Space=flip, 1=Good, 2=Hard, 3=Again, f=follow-up, n=next-topic
• KaTeX: Full LaTeX math rendering
• Drag & drop: Import any questions.json directly in the UI
• Multi-course: One framework, multiple course data packs

Adding to Existing Installation

To add a new course to an existing adaptive-learning setup at ~/adaptive-learning/:

1. Save questions.json to ~/adaptive-learning/courses//
2. Generate preload: bash scripts/generate-course.sh questions.json ~/adaptive-learning/framework/
3. Add to COURSE_REGISTRY in engine.js