Overview
MAGSEL is a metaverse learning platform designed for gender-sensitive social-emotional education. The system combines Unity 3D virtual environments with a fine-tuned DistilBERT emotion classifier to create adaptive learning experiences that respond to student emotional states in real time.
Research presented at the WILLS 2025 Conference, Kyoto University of Foreign Studies, Japan.
Key Features
- Fine-tuned DistilBERT emotion classifier achieving 92.45% accuracy and 0.9255 F1 score on sentiment analysis
- Custom ML pipeline built with Hugging Face Transformers and PyTorch for model training and inference
- Real-time emotion detection via Flask backend processing student interactions during learning sessions
- Procedurally generated environments that adapt virtual worlds to learning scenarios
- Encrypted data handling for secure storage of sensitive educational data
- Unity 3D environments delivering immersive metaverse experiences for social-emotional learning
Technical Achievements
The emotion classification model was fine-tuned from a pre-trained DistilBERT base, trained on labeled sentiment data specific to educational contexts. The model achieved strong performance across multiple metrics:
- 92.45% accuracy on the test set
- 0.9255 F1 score demonstrating balanced precision and recall
- Real-time inference through Flask API serving predictions to the Unity frontend
The ML pipeline handles data preprocessing, tokenization, model fine-tuning, and deployment as a REST API consumed by the Unity application.
Research Impact
Presented at Kyoto University of Foreign Studies, demonstrating the potential of metaverse platforms for sensitive educational topics. The research explores how gaming engagement combined with AI-driven emotional awareness can improve learning outcomes for social-emotional education.
What I Learned
Building MAGSEL taught me that fine-tuning pre-trained models for domain-specific tasks delivers significantly better results than using general-purpose classifiers. The gap between a general sentiment model and one tuned for educational contexts was substantial. Integrating ML inference into a real-time 3D application also pushed me to think carefully about latency, since students need responsive feedback for the experience to feel natural.
Tech Stack
Unity for the 3D metaverse environment, Flask for the backend API, PyTorch and Hugging Face Transformers for the ML pipeline, DistilBERT as the base model for emotion classification, C# for Unity scripting, and Python for model training and inference.