Start

Active Inference Curriculum Creation Pipeline

Overview

The START project provides a comprehensive, AI-powered pipeline for creating personalized Active Inference and Free Energy Principle curricula. The system uses real-time research via Perplexity API and advanced LLM-based content generation to produce professional-grade educational materials.

Pipeline Stages

Stages

Inputs: data/config/entities.yaml, data/config/domains.yaml, data/config/languages.yaml
Research → Curriculum Generation → Visualization → Translation → Outputs

Links

1. Research Phase 🔍

Domain Research: Analyze professional domains (biochemistry, AI, neuroscience, etc.)
Entity Research: Create personalized profiles for target learners
Configuration-Driven: Uses YAML configs for scalable, organized research

2. Curriculum Generation ✍️

Comprehensive Content: 40-60 hour professional development programs
Personalized: Tailored to specific domains and individual learning profiles
Structured Modules: Multi-section educational frameworks with assessments

3. Visualization Creation 📊

Data Visualizations: PNG charts showing curriculum metrics and analysis
Process Diagrams: Mermaid diagrams for curriculum structure and flow
Interactive Elements: Visual learning aids and conceptual frameworks

4. Multilingual Translation 🌍

Cultural Adaptation: Full localization beyond literal translation
Professional Quality: Native-speaker level fluency with technical accuracy
Multiple Languages: Support for 9+ target languages with script mapping

Script Entrypoints

Core Curriculum Creation Scripts

# Configuration-based research (NEW APPROACH)
learning/curriculum_creation/1_Research_Domain.py    # Domain analysis
learning/curriculum_creation/1_Research_Entity.py    # Audience profiling  
learning/curriculum_creation/2_Write_Introduction.py # Curriculum generation
learning/curriculum_creation/3_Introduction_Visualizations.py # Charts & diagrams
learning/curriculum_creation/4_Translate_Introductions.py     # Multilingual output

Supporting Infrastructure

src/perplexity/           # Perplexity API integration
src/common/               # Shared utilities (paths, config, prompts)
src/config/               # Configuration management
src/visualization/        # Visualization generation

Data Architecture

Input Configuration

data/config/
├── entities.yaml         # Target learner profiles (8 entities)
├── domains.yaml          # Professional domains (16 domains) 
└── languages.yaml        # Translation targets (9+ languages)

Research Outputs

data/
├── audience_research/     # Personalized learner analysis
├── domain_research/       # Professional domain analysis
├── written_curriculums/   # Generated curriculum content
├── translated_curriculums/ # Multilingual versions
└── visualizations/        # Charts and diagrams

Template System

data/prompts/
├── research_domain_analysis.md     # 6-section domain framework
├── research_domain_curriculum.md   # 9-section curriculum generation
├── research_entity.md              # 6-section personalization
├── curriculum_section.md           # Comprehensive module creation
└── translation.md                  # 7-section multilingual framework

Enhanced Features

Configuration-Driven Research

YAML-Based Control: All research targets defined in configuration files
Priority Filtering: Process high/medium/low priority items
Category Filtering: Focus on specific domain categories
Overwrite Control: Skip existing by default, force overwrite with flags

Advanced Command-Line Interface

# Filter by priority and category
python 1_Research_Domain.py --priority high --category life_sciences

# Process specific targets
python 1_Research_Entity.py --entity karl_friston --overwrite

# Multilingual output with specific languages
python 4_Translate_Introductions.py --languages Spanish French German

Comprehensive Content Generation

Domain Analysis: 3,000-5,000 word professional landscape analysis
Curriculum Content: 40-60 hour structured learning programs
Personalization: 5,000-8,000 word tailored learning strategies
Section Modules: 3-5 hour comprehensive learning units

API Integration

Perplexity API (Research)

Real-time Research: Current online information and analysis
Professional Insights: Industry trends, challenges, opportunities
Comprehensive Analysis: Multi-perspective domain understanding

OpenRouter API (Content Generation)

Advanced LLMs: High-quality curriculum and translation generation
Structured Output: Consistent, professional educational content
Multilingual Capability: Native-quality translations with cultural adaptation

Quality Assurance

Content Standards

Professional Grade: University course and corporate training quality
Evidence-Based: Real research and current industry insights
Comprehensive Coverage: Multi-modal learning approaches
Assessment Integrated: Built-in evaluation and progress tracking

Technical Standards

Modular Architecture: Clean, maintainable, testable code
Error Handling: Robust failure recovery and logging
Performance Optimized: Efficient API usage and data processing
Standards Compliant: Following Python best practices and project conventions

Cross-References

Environment setup and CI workflow: docs/environment.md
Testing policy and markers: docs/TESTING.md
Clone management for optional resources: docs/clones.md

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