📘 CLASS AlignED

Bridging the gap between AI policy and classroom practice

CLASS AlignED is an AI-powered system that helps educators align course design with university AI policies by transforming syllabi and policy documents into actionable, policy-aware teaching recommendations.

🚨 Problem

As AI adoption grows in higher education, faculty face a major challenge:

• Universities provide AI policies, but
• Faculty lack practical guidance on how to apply them in real courses

This creates a policy–practice gap:

Policies exist, but instructors don’t know how to implement them in assignments, assessments, or lesson planning.

💡 Solution

CLASS AlignED solves this by:

• Ingesting course syllabi + university AI policies
• Structuring and analyzing them using AI
• Generating clear, policy-aligned recommendations for teaching

👉 Instead of interpreting policy manually, faculty receive ready-to-use guidance.

🧠 System Overview

The system works as a pipeline:

1. 📥 Input

• Upload:
  • Course syllabus (PDF/DOCX)
  • University AI policy

2. ⚙️ Processing

• Extract text from documents
• Chunk content into manageable pieces
• Structure data using AI

3. 🤖 AI Recommendation Engine

• Uses Gemini (LLM) to extract:
  • Learning outcomes
  • Assessments
  • Policies
• Uses GraphRAG to:
  • Connect syllabus + policy + contextual knowledge
  • Generate grounded recommendations

4. 📊 Output

Produces:

• Course summary
• Learning outcomes
• Assessment breakdown
• Policy interpretation
• AI-supported teaching recommendations

🏗️ Tech Stack

Core AI

• Gemini API → structured extraction + reasoning
• GraphRAG → knowledge graph + contextual querying

Backend

• Python
• JSON / JSONL pipelines
• Subprocess-based GraphRAG integration

Document Processing

• pypdf → PDF parsing
• python-docx → DOCX parsing

UI (Demo App)

• Streamlit → interactive frontend

📁 Projects

1️⃣ CLASS_AlignED_MVP

The original backend system that:

• Processes syllabus + policy documents
• Chunks and extracts structured data
• Runs GraphRAG indexing + querying
• Outputs:
  • JSON results
  • Graph-based insights
  • Faculty-facing reports

Key Features

• End-to-end pipeline (CLI-based)
• GraphRAG knowledge integration
• Policy-aware AI recommendations

2️⃣ CLASS_ALIGNED_MVP_UI_DEMO

A user-facing application built on top of the MVP.

What we added:

• 📤 File upload interface (Streamlit)
• 📂 Automatic file routing:
  • raw/syllabi
  • raw/policies
• ⚙️ Integrated pipeline execution
• 📊 Clean UI outputs:
  • Course summary
  • Learning outcomes
  • Assessments
  • Policies
  • AI recommendations
  • GraphRAG insights

Goal:

Make the system usable by non-technical faculty

🔄 How It Works (Step-by-Step)

1. User uploads syllabus + policy
2. Files are saved to structured directories
3. Text is extracted and chunked
4. Gemini extracts structured course data
5. GraphRAG builds relationships + context
6. System generates:
   • Recommendations
   • Reports
   • Insights

🎯 Key Innovation

CLASS AlignED doesn’t just analyze documents — it:

Transforms static policies into actionable teaching strategies

📈 Impact

• Reduces faculty uncertainty around AI usage
• Embeds policy into real classroom workflows
• Supports scalable adoption of AI in education
• Especially impactful for institutions with limited resources

🚀 Future Work

• Stronger policy–recommendation alignment
• Improved GraphRAG grounding with research datasets
• Better UI/UX for faculty workflows
• Integration with LMS platforms (e.g., Blackboard, Canvas)

🧑‍🏫 Example Use Case

An instructor uploads:

• A syllabus
• Their university’s AI policy

The system returns:

• AI-supported assignment ideas
• Policy-compliant usage guidelines
• Suggestions to improve engagement and learning outcomes

🧪 Running the Project

🔧 1. Set Up Environment

# Create virtual environment (if needed)
python -m venv graphrag-env

# Activate it
source graphrag-env/bin/activate  # Mac/Linux
# or
graphrag-env\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

🔑 2. Set API Key

Create a .env file in your project root:

GEMINI_API_KEY=your_api_key_here

🧠 3. Run CLASS_AlignED_MVP (Backend)

Navigate to your GraphRAG workspace:

cd processed/graphrag_workspace

Index documents

graphrag index --root .

Run a query

graphrag query --root . --method local "Identify AI-supported teaching strategies"

🖥️ 4. Run CLASS_ALIGNED_MVP_UI_DEMO (Frontend App)

From the UI demo project root:

cd ~/Desktop/CLASS_ALIGNED_MVP_UI_DEMO

Start the app:

streamlit run app/streamlit_app.py

📂 5. Using the UI

1. Upload:
   • Course syllabus (PDF or DOCX)
   • University AI policy
2. The system will automatically:
   • Save files to:
     • raw/syllabi
     • raw/policies
   • Extract and chunk text
   • Run Gemini extraction
   • Run GraphRAG query
3. View results in the UI:
   • Course summary
   • Learning outcomes
   • Assessments
   • Policies
   • AI recommendations
   • GraphRAG insights

⚠️ Notes

• Make sure your GEMINI_API_KEY is valid and loaded
• Ensure GraphRAG workspace is initialized before querying
• If GraphRAG errors occur, re-run:

graphrag index --root .

✅ Quick Start (TL;DR)

# Activate env
source graphrag-env/bin/activate

# Run UI
cd ~/Desktop/CLASS_ALIGNED_MVP_UI_DEMO
streamlit run app/streamlit_app.py