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CURRICULUM CONTENT GENERATION MODE

Generate comprehensive educational content for: $ARGUMENTS

System Prompt​

⚠️ EXECUTION DIRECTIVE: When the user invokes this command, you MUST:

  1. IMMEDIATELY execute - no questions, no explanations first
  2. ALWAYS show full output from script/tool execution
  3. ALWAYS provide summary after execution completes

DO NOT:

  • Say "I don't need to take action" - you ALWAYS execute when invoked
  • Ask for confirmation unless requires_confirmation: true in frontmatter
  • Skip execution even if it seems redundant - run it anyway

The user invoking the command IS the confirmation.

Usage​

# Generate curriculum for a topic
/generate-curriculum-content "Introduction to Machine Learning"

# Generate with specific skill levels
/generate-curriculum-content "Neural Networks" --levels beginner,intermediate

# Generate with assessments
/generate-curriculum-content "Deep Learning" --with-assessments

# NotebookLM optimized
/generate-curriculum-content "AI Ethics" --notebooklm-format

Mode Rules​

✅ ALLOWED ACTIVITIES​

  • Multi-level content generation across 4 skill levels
  • Learning objective creation using Bloom's taxonomy
  • Assessment framework design with adaptive evaluation
  • NotebookLM metadata optimization for AI processing
  • Cross-reference creation for knowledge graph building
  • Pedagogical framework application (scaffolding, constructivist learning)

❌ FORBIDDEN ACTIVITIES​

  • Single-level content only - Must address multiple skill levels
  • Assessment-free content - Must integrate evaluation
  • Generic content - Must be AI-domain specific
  • Metadata-poor content - Must optimize for NotebookLM

Content Generation Framework​

Phase 1: Learning Architecture Design​

## Learning Objectives by Skill Level

### Beginner (Bloom Distribution: Remember 40%, Understand 35%, Apply 20%, Analyze 5%)
- [ ] **Remember**: [Specific recall objective]
- [ ] **Understand**: [Conceptual understanding objective]  
- [ ] **Apply**: [Basic application objective]

### Intermediate (Bloom Distribution: Understand 25%, Apply 35%, Analyze 20%, Evaluate 5%)
- [ ] **Understand**: [Detailed comprehension objective]
- [ ] **Apply**: [Implementation objective]
- [ ] **Analyze**: [Comparison/analysis objective]

### Advanced (Bloom Distribution: Apply 25%, Analyze 30%, Evaluate 20%, Create 5%)
- [ ] **Apply**: [Complex implementation objective]
- [ ] **Analyze**: [Research analysis objective]
- [ ] **Evaluate**: [Critical evaluation objective]

### Expert (Bloom Distribution: Apply 25%, Analyze 25%, Evaluate 20%, Create 15%)
- [ ] **Analyze**: [Theoretical analysis objective]
- [ ] **Evaluate**: [Peer review/critique objective]
- [ ] **Create**: [Innovation/research objective]

## Prerequisites Assessment
- **Beginner**: [List foundational requirements]
- **Intermediate**: [List technical prerequisites] 
- **Advanced**: [List specialized knowledge needed]
- **Expert**: [List research-level requirements]

Phase 2: Multi-Level Content Creation​

Beginner Content Pattern:

# [Topic] for Absolute Beginners

## Learning Story: [Character]'s Journey
[Narrative approach with relatable character learning the concept]

### What is [Topic]? (Simple Definition)
[Everyday language explanation with analogies]

### Visual Analogy: [Real-World Comparison]
[Concrete example from daily life that mirrors the AI concept]

### Simple Example: [Guided Walkthrough]
[Step-by-step example with explanations]

### Key Takeaways
- [3-5 memorable bullet points]

### Check Your Understanding
- [Simple quiz questions focusing on recognition and basic comprehension]

### Next Steps
[Preview of intermediate level with encouragement]

Intermediate Content Pattern:

# [Topic] in Practice

## Project Overview
[Real-world application project]

### Learning Goals
- [Specific implementation objectives]

### Tools and Technologies
- [Required software, libraries, frameworks]

### Step-by-Step Implementation
#### Part 1: [Foundation Setup]
#### Part 2: [Core Implementation]  
#### Part 3: [Testing and Validation]
#### Part 4: [Optimization and Enhancement]

### Expected Outcomes
- [Measurable deliverables]
- [Portfolio additions]

### Troubleshooting Guide
[Common issues and solutions]

### Extension Challenges
[Additional features to implement]

Advanced Content Pattern:

# Advanced [Topic]: Research and Optimization

## Research Context
[Current state of the field and open problems]

### Paper Analysis
[2-3 recent papers with implementation insights]

### Optimization Challenges
[Performance, scalability, accuracy improvements]

### Implementation Deep-Dive
[Complex algorithm implementation with mathematical foundations]

### Evaluation Framework
[Rigorous testing and benchmarking methodologies]

### Case Studies
[Industry applications and real-world deployments]

### Research Opportunities
[Open problems and potential research directions]

Expert Content Pattern:

# [Topic] Research Frontiers

## Theoretical Foundations
[Mathematical frameworks and formal analysis]

### Current Research Landscape
[Survey of latest developments and breakthrough papers]

### Innovation Challenge
[Original research problem to solve]

### Methodology Requirements
[Research design, experimental protocols, evaluation criteria]

### Contribution Pathways
[Publication venues, collaboration opportunities, impact measurement]

### Theoretical Analysis
[Formal proofs, complexity analysis, theoretical guarantees]

### Future Directions
[Long-term research vision and societal impact]

Phase 3: Assessment Integration​

## Assessment Framework by Skill Level

### Formative Assessment (Embedded Throughout)
**Beginner**: 
- Concept check questions every 2 paragraphs
- Visual identification exercises
- Simple recall quizzes

**Intermediate**:
- Code checkpoint validations  
- Mini-project milestones
- Peer code review exercises

**Advanced**:
- Research paper analysis assignments
- Optimization challenge checkpoints
- Case study evaluations

**Expert**:
- Literature review submissions
- Original research proposal drafts
- Peer critique assignments

### Summative Assessment (End of Module)
**Beginner**:
- Multiple choice quiz (20 questions)
- Concept explanation project
- Learning reflection essay

**Intermediate**:
- Complete implementation project
- Technical presentation
- Portfolio documentation

**Advanced**:
- Research implementation project
- Performance optimization report
- System design document

**Expert**:
- Original research contribution
- Publication-quality paper
- Conference presentation

Phase 4: NotebookLM Optimization​

# Content Metadata Template
content_metadata:
  # Core Identifiers
  skill_level: [beginner|intermediate|advanced|expert]
  module: "module[X]_[topic]"
  week: [week_number]
  topic: "[specific_topic]"
  
  # Learning Structure  
  estimated_time_hours: [X]
  difficulty_score: [1-5]
  bloom_levels: [list of cognitive levels]
  learning_objectives: [specific objectives]
  prerequisites: [required knowledge]
  
  # Content Organization
  content_type: [concept|example|exercise|project|assessment]
  format: [markdown|jupyter|interactive|video|audio]
  language_complexity: [elementary|intermediate|advanced|graduate]
  
  # Cross-References (Knowledge Graph)
  related_concepts: [connected topics]
  prerequisite_topics: [foundational concepts]  
  follow_up_topics: [next learning steps]
  cross_module_connections: [links to other modules]
  
  # Assessment Integration
  formative_assessments: [embedded evaluations]
  summative_assessments: [module-level tests]
  project_connections: [related hands-on work]
  portfolio_items: [progressive skill documentation]
  
  # Accessibility and Personalization
  learning_styles: [visual|auditory|kinesthetic|reading]
  accommodation_features: [accessibility adaptations]
  personalization_markers: [adaptive content triggers]
  
  # NotebookLM Enhancement
  ai_generation_ready: true
  book_chapter_structure: [hierarchical organization]
  quiz_question_seeds: [assessment generation prompts]
  flashcard_concepts: [key terms and definitions]
  interactive_elements: [engagement opportunities]

Content Generation Strategies​

Strategy 1: Progressive Complexity Scaling​

1. Identify core concept at expert level
2. Abstract to essential elements
3. Scale down complexity for each level:
   - Expert: Full mathematical rigor + research context
   - Advanced: Algorithmic details + optimization focus  
   - Intermediate: Implementation focus + practical applications
   - Beginner: Conceptual understanding + visual analogies
4. Ensure concept integrity across all levels
5. Create progression bridges between adjacent levels

Strategy 2: Assessment-Driven Content Design​

1. Design assessments first for each skill level
2. Work backward to determine required content
3. Ensure alignment between content and evaluation
4. Integrate formative assessment throughout
5. Create clear performance criteria and rubrics

Strategy 3: Knowledge Graph Integration​

1. Map concept relationships and dependencies
2. Create explicit cross-references between topics
3. Design prerequisite validation checkpoints
4. Build knowledge progression pathways
5. Enable adaptive content navigation

Output Structure Template​

# Generated Content: [Topic] - [Skill Level] Level

## Content Metadata
[YAML metadata block]

## Learning Objectives
- [Bloom Level]: [Specific objective with assessment method]

## Prerequisites Verification
- [ ] [Prerequisite 1] (validation method)
- [ ] [Prerequisite 2] (validation method)

## Content Structure
### Core Concepts ([Appropriate Complexity Level])
[Level-appropriate content delivery]

### Hands-On Activities ([Skill-Appropriate Format])
[Practical exercises matching skill level]

### Assessment Integration ([Embedded Evaluation])
[Formative assessment throughout]

### Real-World Applications ([Level-Relevant Examples])
[Industry connections and use cases]

## Cross-References
- **Prerequisite Topics**: [foundational concepts with links]
- **Related Concepts**: [parallel topics with connections]
- **Next Learning Steps**: [progression pathway]

## Assessment Components
### Formative Assessment
[Embedded evaluation throughout content]

### Summative Assessment  
[End-of-section comprehensive evaluation]

### Portfolio Integration
[Progressive skill documentation opportunities]

## NotebookLM Optimization
- **Book Generation Ready**: [structured for chapter creation]
- **Quiz Seeds**: [assessment generation prompts]
- **Flashcard Concepts**: [key terms for memorization]
- **Interactive Elements**: [engagement opportunities]

## Quality Assurance Checklist
- [ ] **Learning Objective Alignment**: Content matches stated objectives
- [ ] **Skill Level Appropriateness**: Cognitive load matches target level
- [ ] **Assessment Integration**: Evaluation aligns with content and objectives
- [ ] **Cross-Reference Accuracy**: Links and dependencies verified
- [ ] **Technical Accuracy**: Code examples tested and validated
- [ ] **Accessibility Features**: Multiple learning styles accommodated
- [ ] **NotebookLM Optimization**: Metadata and structure optimized

Integration Requirements​

Skills Integration​

  • Auto-load: ai-curriculum-development skill for comprehensive framework
  • Coordinate: assessment-creation skill for evaluation design
  • Reference: notebooklm-optimization skill for content formatting

Agent Coordination​

  • Use: ai-curriculum-specialist for overall curriculum architecture
  • Delegate: Content generation to specialized educational agents
  • Validate: Quality and pedagogy with educational review agents

Command Workflows​

  • Precede: /research mode to validate educational approaches
  • Follow: /optimize-notebooklm for content enhancement
  • Integrate: /generate-assessment for evaluation creation

Best Practices​

✅ DO:

  • Start with clear learning objectives for each skill level
  • Apply Bloom's taxonomy distribution appropriate to skill level
  • Integrate assessment throughout the learning experience
  • Create explicit cross-references and knowledge connections
  • Test content with target skill level learners
  • Optimize metadata for NotebookLM processing
  • Ensure accessibility across diverse learning needs

❌ DON'T:

  • Create content for only one skill level
  • Skip learning objective definition
  • Ignore assessment integration
  • Create isolated content without connections
  • Use inappropriate cognitive complexity for skill level
  • Neglect NotebookLM optimization metadata
  • Forget accessibility and inclusion considerations

Quality Validation​

Content Quality Metrics​

  • Learning objective achievement: 90%+ of learners meet objectives
  • Skill progression rate: 80%+ advance to next level successfully
  • Engagement metrics: 85%+ completion rate across skill levels
  • Technical accuracy: 100% code functionality and concept validity

Assessment Quality Metrics​

  • Content-assessment alignment: Strong correlation between teaching and testing
  • Skill differentiation: Clear performance differences across skill levels
  • Bias detection: <5% performance gaps across demographic groups
  • Accessibility compliance: Full WCAG 2.1 AA standard adherence

Action Policy​

<default_behavior> This command analyzes and recommends without making changes. Provides:

  • Detailed analysis of current state
  • Specific recommendations with justification
  • Prioritized action items
  • Risk assessment

User decides which recommendations to implement. </default_behavior>

After analysis, provide: - Analysis completeness (all aspects covered) - Recommendation confidence levels - Specific examples from codebase - Clear next steps for user

Success Output​

When curriculum content generation completes:

✅ COMMAND COMPLETE: /generate-curriculum-content
Topic: <topic-name>
Levels: <beginner|intermediate|advanced|expert>
Sections: N created
Assessments: N integrated
NotebookLM: Optimized
Output: <file-path>

Completion Checklist​

Before marking complete:

  • Learning objectives defined (per level)
  • Content created (multi-level)
  • Assessments integrated
  • Cross-references added
  • NotebookLM metadata optimized

Failure Indicators​

This command has FAILED if:

  • ❌ No topic specified
  • ❌ Single-level content only
  • ❌ No assessments included
  • ❌ Missing metadata

When NOT to Use​

Do NOT use when:

  • Non-educational content
  • Simple documentation (use /document)
  • Single skill level only

Anti-Patterns (Avoid)​

Anti-PatternProblemSolution
Single levelLimited audienceCreate 4 skill levels
No assessmentCan't measure learningIntegrate formative/summative
Skip metadataPoor NotebookLM processingAdd full YAML metadata

Principles​

This command embodies:

  • #3 Complete Execution - Full curriculum workflow
  • #6 Clear, Understandable - Progressive complexity
  • #9 Based on Facts - Evidence-based pedagogy

Full Standard: CODITECT-STANDARD-AUTOMATION.md