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Structure learning paths with pedagogical best practices including Bloom's Taxonomy, module arc patterns, prerequisite mapping, and duration estimation.
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SOC 職業分類に基づく
Ingest a user-provided document (PDF, Markdown, TXT, DOCX, EPUB, HTML) and turn it into a clean, chunked source corpus that grounds the syllabus and lessons. Use when the learner wants a course built FROM their own material (a white paper, book chapter, spec, internal doc) instead of, or in addition to, web research.
Generate audio narration for tutorial lessons using Edge TTS. Covers script conversion from lesson JSON, voice selection, audio generation, manifest creation, section timestamping, and Web Speech API fallback.
Deploy generated tutorials to free hosting services. Covers Vercel, Netlify, Surge, GitHub Pages, and Cloudflare Pages with auth flows, SPA routing, troubleshooting, and QR code generation.
Ensure WCAG 2.1 AA compliance for all generated tutorials. Covers perceivable, operable, understandable, and robust criteria plus reduced motion support.
Automated quality auditing for generated tutorials. Covers accessibility checks, performance analysis, content validation, responsive testing, and auto-fix strategies.
Visual design system and component library for Syllabus tutorials. Includes 4 theme presets with glassmorphism, gradient systems, micro-interactions, component specs, spacing scale, and icon conventions.
| name | syllabus-design |
| description | Structure learning paths with pedagogical best practices including Bloom's Taxonomy, module arc patterns, prerequisite mapping, and duration estimation. |
Patterns and frameworks for building effective learning sequences.
Map each lesson to a cognitive level:
| Level | Verbs | Example Objective |
|---|---|---|
| Remember | Define, list, recall | "List the 5 main SLM families" |
| Understand | Explain, compare, summarize | "Explain why LoRA reduces memory usage" |
| Apply | Implement, use, execute | "Fine-tune a model using the HuggingFace Trainer" |
| Analyze | Debug, differentiate, examine | "Diagnose why a fine-tuned model is overfitting" |
| Evaluate | Justify, critique, assess | "Evaluate which quantization method suits a given scenario" |
| Create | Design, build, produce | "Design a fine-tuning pipeline for a customer support bot" |
Each module should follow this arc:
1. HOOK — Why does this matter? (motivation)
2. FOUNDATIONS — Core concepts and vocabulary
3. DEEP DIVE — Detailed mechanics and theory
4. PRACTICE — Hands-on application
5. SYNTHESIS — Connect back to the bigger picture
6. CHECK — Quiz/exercise to verify understanding
Define prerequisites at three levels:
| Content Type | Duration per Unit |
|---|---|
| Concept explanation | 3-5 min per concept |
| Code walkthrough | 5-8 min per example |
| Hands-on exercise | 10-20 min per exercise |
| Quiz (MCQ) | 1-2 min per question |
| Coding challenge | 15-30 min each |
| Diagram analysis | 3-5 min per diagram |