NS-CausalKT is a state-of-the-art Neural-Symbolic framework designed to transform AI-driven education from simple "score prediction" to deep "causal diagnostics." By combining the predictive power of Transformers with the transparency of Causal Inference (Pearl's Do-Calculus) and Symbolic Logic, the system identifies the root causes of student misconceptions.
The model utilizes a Hybrid Transformer-Logic approach. While the Neural layer (Transformers) captures temporal patterns in learning, the Symbolic layer enforces mathematical constraints and logical prerequisites, ensuring the model's knowledge representation is educationally sound.
Unlike traditional Knowledge Tracing (DKT, AKT) which only finds correlations, NS-CausalKT builds a Structural Causal Model (DAG). It distinguishes between effort-based success (Time Spent) and knowledge-based success (Prior Mastery), allowing for "What-If" counterfactual simulations.
The Agentic System integrates GPT-4o Mini with the NS-CausalKT backend. It can:
- See: Analyze handwritten answer sheets via Multimodal Vision.
- Reason: Map visual errors (e.g., a sign error in Algebra) to specific nodes in the Concept Dependency Graph.
- Act: Generate strategic feedback tailored to the student's causal mastery profile.
| Model Architecture | AUC (BaseLine) | AUC (Causal) | RMSE Delta | Efficiency Gain |
|---|---|---|---|---|
| NS-CausalKT-v2 | 0.824 | 0.891 | -0.062 | +6.1% |
| AKT (Baseline) | 0.791 | 0.791 | +0.000 | Baseline |
| DKT (Baseline) | 0.805 | 0.812 | +0.002 | +1.2% |
- Core: Python 3.10, PyTorch, NumPy, Pandas.
- Causal Engine: Pearl's Do-Calculus, Structural Causal Models (SCM).
- Agentic Layer: OpenAI GPT-4o Mini (Vision API).
- Dashboard: Vanilla JS, CSS3 (Modern Blue/Cyan Glassmorphism UI), SVG-based Dynamic Graphing.
NS-CausalKT-Agentic-Grader/
├── agentic_ui/ # Full-stack Research Dashboard
│ ├── causal.html # Causal Analytics & Counterfactual Simulator
│ ├── inference.html # Agentic Grading UI (Full-width marksheet analysis)
│ ├── dashboard.html # Real-time Benchmark Monitoring
│ ├── js/app.js # SVG Arrow Mapping & Graph Rendering Logic
│ └── css/style.css # Professional Cyan-Slate Design System
├── models/ # Core Neural-Symbolic Causal Logic
├── checkpoints/ # Trained Model Weights & Benchmark Logs
├── data/ # Preprocessed Math Datasets
├── docs/ # Technical Documentation & Explanations
└── backend/ # Flask API for Model Serving
# Clone the repository
git clone https://github.com/Aditya-Ranjan1234/NS-CausalKT-Agentic-Grader.git
cd NS-CausalKT-Agentic-Grader
# Install local training/backend dependencies
pip install -r requirements-local.txt
# Run the backend
python agentic_ui/backend/app.pyThe project is configured for Vercel deployment out-of-the-box. Ensure your OPENAI_API_KEY is set in the environment variables for the Agentic Grading module.
- ACE (Average Causal Effect): Measures the intensity of the link between concepts.
- CDG (Concept Dependency Graph): A symbolic map of math topic prerequisites.
- SCM DAG: A directed acyclic graph representing the global causal factors of learning.
This project is licensed under the MIT License - see the LICENSE file for details.
“Moving beyond correlation to causal understanding in AI-driven education.”