Courseforge

Education Reimagined. Access for All.

AI-powered course generation and remediation system that creates and improves accessible, LMS-ready IMSCC packages.

Overview

Courseforge uses a multi-agent orchestration system to create high-quality online courses compatible with Brightspace/D2L and other learning management systems. It also provides comprehensive intake and remediation capabilities for existing courses.

Key Features

Course Creation

• Simplified Pipeline: Exam objectives + textbooks → IMSCC package
• DART Integration: Accessibility-first textbook processing
• Multi-Agent System: Specialized agents for each phase of development
• WCAG 2.2 AA: Built-in accessibility compliance
• OSCQR Standards: Educational quality validation
• Pattern Prevention: 22+ error patterns identified and prevented

Course Intake & Remediation (NEW)

• Universal IMSCC Import: Canvas, Blackboard, Moodle, Brightspace, Sakai
• Automated DART Conversion: PDFs and Office docs → accessible HTML
• AI-Powered Accessibility Fixes: Alt text, heading structure, contrast
• Intelligent Component Styling: AI-selected interactive elements
• 100% WCAG 2.2 AA Compliance: Guaranteed accessibility output

Example Package

See examples/intro_python.imscc for a complete 12-week Introduction to Python course demonstrating:

• Proper IMSCC 1.3 structure
• QTI 1.2 quizzes with Brightspace compatibility
• Native assignment integration (assignment_xmlv1p0)
• Discussion topics
• WCAG 2.2 AA compliant content

Quick Start

Mode 1: Course Creation

1. Place exam objectives in inputs/exam-objectives/
2. (Optional) Process textbooks through DART and place in inputs/textbooks/
3. Invoke: exam-research → course-outliner → content-generator → brightspace-packager
4. Output: IMSCC file in exports/YYYYMMDD_HHMMSS_coursename/

Mode 2: Course Remediation (NEW)

1. Place existing IMSCC in inputs/existing-packages/
2. Invoke: imscc-intake-parser → content-analyzer → remediation agents → brightspace-packager
3. Output: Improved IMSCC with 100% accessibility compliance

Project Structure

Courseforge/
├── CLAUDE.md # Orchestration instructions
├── README.md # This file
├── docs/ # Documentation
│ ├── troubleshooting.md
│ ├── workflow-reference.md
│ └── getting-started.md
├── agents/ # Agent specifications
├── inputs/ # Input files
│ ├── exam-objectives/
│ ├── textbooks/
│ └── existing-packages/ # IMSCC for intake (NEW)
├── scripts/ # Automation scripts (NEW)
│ ├── imscc-extractor/
│ ├── dart-batch-processor/
│ ├── component-applier/
│ └── remediation-validator/
├── templates/ # HTML templates
├── schemas/ # IMSCC schemas
├── imscc-standards/ # Technical specs
├── exports/ # Generated packages
└── runtime/ # Agent workspaces

Available Agents

Course Creation

Agent	Purpose
exam-research	Certification objective analysis
requirements-collector	Course specification gathering
course-outliner	Structure and learning objectives
content-generator	Educational content creation
educational-standards	Pedagogical compliance
quality-assurance	Pattern prevention
oscqr-course-evaluator	Quality assessment
brightspace-packager	IMSCC packaging

Intake & Remediation (NEW)

Agent	Purpose
imscc-intake-parser	Universal IMSCC import
content-analyzer	Accessibility/quality gap detection
dart-automation-coordinator	Automated document conversion
accessibility-remediation	WCAG 2.2 AA fixes
content-quality-remediation	Educational depth enhancement
intelligent-design-mapper	AI component selection

Workflow

USER REQUEST →
exam-research (analyze objectives) →
course-outliner (create structure) →
content-generator (create content, 10 agents/batch) →
quality-assurance + oscqr-course-evaluator (validate) →
brightspace-packager (package) →
IMSCC OUTPUT

Textbook Processing

Textbooks must be processed through DART before use:

# Set DART_PATH to your DART installation directorycd$DART_PATH
python convert.py textbook.pdf -o /path/to/courseforge/inputs/textbooks/

DART produces WCAG 2.2 AA accessible HTML with:

• Semantic structure
• Alt text for images
• MathML for equations
• Proper heading hierarchy

Quality Standards

OSCQR Thresholds

• Pre-development: 70%
• Pre-production: 90%
• Accessibility: 100%

Pattern Prevention

Critical patterns addressed:

• Schema/namespace consistency
• Assessment XML format (QTI 1.2)
• Content completeness
• Organization hierarchy

See docs/troubleshooting.md for complete pattern list.

Documentation

Document	Purpose
CLAUDE.md	Main orchestration instructions
docs/troubleshooting.md	Error patterns and solutions
docs/workflow-reference.md	Execution protocols
docs/getting-started.md	Quick start guide
agents/*.md	Individual agent specs

Technical Requirements

• Python 3.8+
• Claude Code access
• Brightspace/D2L for import testing

DART Setup (Optional)

DART (Digital Accessibility Remediation Tool) is a separate tool used for converting PDFs and Office documents to accessible HTML. It's optional but recommended for textbook processing.

Installation

DART is available as a separate project. Once installed, configure the environment variable:

# Add to your shell profile (.bashrc, .zshrc, etc.)export DART_PATH=/path/to/your/DART/installation

Usage with Courseforge

Once DART_PATH is set, Courseforge scripts will automatically detect and use DART for:

• PDF textbook conversion
• Office document (Word, PowerPoint) conversion
• Batch document processing via scripts/dart-batch-processor/

Without DART

Courseforge works without DART for:

• Course creation from exam objectives
• IMSCC package generation
• Course remediation (accessibility fixes to existing HTML)

Only textbook PDF/Office conversion requires DART.

Compatibility

• Brightspace/D2L
• Canvas
• Blackboard
• Moodle
• Any LMS supporting IMSCC 1.1+

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Version: 1.0.0 License: MIT Last Updated: December 2025

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