An AI-driven platform that analyzes student engagement through real-time emotion detection and focus tracking.
Using computer vision and deep learning, the system measures attention levels, visualizes emotion trends, and generates insightful reports to improve learning outcomes.
🔗 Dashboard:
https://smart-learning-ai-dashboard.streamlit.app/
📂 GitHub Repository:
https://github.com/anuskagupta123/smart-learning-engagement-system
- 🎥 Real-time engagement analysis using webcam
- 😊 Emotion detection using DeepFace
- 👀 Focus tracking with MediaPipe
- 📊 Interactive engagement analytics dashboard
- 🧾 Session logging and report generation
- 🤖 AI-based engagement feedback and recommendations
- Streamlit – Dashboard & UI
- TensorFlow – Deep learning framework
- DeepFace – Emotion detection
- MediaPipe – Face and focus tracking
- OpenCV – Computer vision processing
- Pandas – Data analysis
- Plotly – Data visualization
smart-learning-engagement-system │ ├── app.py ├── engagement_analyzer.py ├── dashboard.py ├── requirements.txt ├── README.md ├── session_logs/ └── train/
git clone https://github.com/anuskagupta123/smart-learning-engagement-system.git
cd smart-learning-engagement-system
pip install -r requirements.txt
streamlit run app.py
Webcam Input ↓ Face Detection (MediaPipe) ↓ Emotion Detection (DeepFace) ↓ Engagement Score Calculation ↓ Session Logs (.csv) ↓ Dashboard Visualization
The Streamlit dashboard provides:
- Engagement score trends
- Focus vs emotion analytics
- Emotion distribution charts
- AI-generated feedback
- Downloadable engagement reports
Try the live dashboard here:
👉 https://smart-learning-ai-dashboard.streamlit.app/
Anuska Gupta
AI / ML Developer
GitHub:
https://github.com/anuskagupta123