--- license: apache-2.0 tags: - reinforcement-learning - education - doubt-prediction - adaptive-learning - multi-agent-systems - gesture-recognition - computer-vision - q-learning - grpo - edtech - mediapipe - privacy datasets: - synthetic-learning-interactions --- # ContextFlow: Predictive Doubt Detection in Adaptive Learning Systems **A Research Implementation of RL-Powered Educational Technology** | Property | Value | |----------|-------| | **Algorithm** | GRPO + Q-Learning | | **State Dimension** | 64 features | | **Action Dimension** | 10 doubt predictions | | **Policy Version** | 50 | | **Training Samples** | 200 | | **Final Loss** | 0.2465 | | **Avg Reward** | 0.75 | ## Overview ContextFlow predicts student confusion **before** it occurs using reinforcement learning and behavioral signal analysis. When a learner's actions suggest they might be struggling (mouse hesitation, scroll reversals, help-seeking gestures), the system proactively offers assistance. ## Architecture ``` ┌─────────────────────────────────────────────────┐ │ 9 Specialized Agents │ ├─────────────────────────────────────────────────┤ │ • StudyOrchestrator • DoubtPredictorAgent │ │ • BehavioralAgent • HandGestureAgent │ │ • RecallAgent • KnowledgeGraphAgent │ │ • PeerLearningAgent • LLMOrchestrator │ │ • GestureActionMapper • PromptAgent │ └─────────────────────────────────────────────────┘ ``` ## Quick Start ```python # Load the model from huggingface_hub import hf_hub_download import pickle path = hf_hub_download( repo_id='namish10/contextflow-rl', filename='checkpoint.pkl' ) with open(path, 'rb') as f: checkpoint = pickle.load(f) print(f"Policy version: {checkpoint.policy_version}") print(f"Training samples: {checkpoint.training_stats['total_samples']}") ``` ## State Vector (64 dimensions) | Component | Dims | Description | |-----------|------|-------------| | Topic Embedding | 32 | TF-IDF of learning topic | | Progress | 1 | Session progress (0.0-1.0) | | Confusion Signals | 16 | Behavioral indicators | | Gesture Signals | 14 | Hand gesture frequencies | | Time Spent | 1 | Normalized session time | ## Actions (10 doubt predictions) 1. `what_is_backpropagation` 2. `why_gradient_descent` 3. `how_overfitting_works` 4. `explain_regularization` 5. `what_loss_function` 6. `how_optimization_works` 7. `explain_learning_rate` 8. `what_regularization` 9. `how_batch_norm_works` 10. `explain_softmax` ## Training Results | Epoch | Loss | Epsilon | Avg Reward | |-------|------|---------|------------| | 1 | 1.2456 | 1.000 | 0.20 | | 2 | 0.8923 | 0.995 | 0.35 | | 3 | 0.6541 | 0.990 | 0.48 | | 4 | 0.4127 | 0.985 | 0.62 | | 5 | 0.2465 | 0.980 | 0.75 | ## Key Features - **Predictive Detection**: RL-based confusion prediction before it happens - **Multi-Agent Orchestration**: 9 specialized agents working together - **Gesture Recognition**: Privacy-first hand gesture detection with MediaPipe - **Face Blurring**: Real-time face blur for classroom deployment - **Browser AI Launch**: Direct AI chat interface from predicted doubts - **Spaced Repetition**: SM-2 based review scheduling - **Knowledge Graphs**: Concept mapping and learning paths ## Files | File | Description | |------|-------------| | `checkpoint.pkl` | Trained Q-network weights | | `train_rl.py` | Training script with GRPO | | `feature_extractor.py` | 64-dim state extraction | | `inference_example.py` | Usage examples | | `demo.ipynb` | Interactive notebook | | `RESEARCH_PAPER.md` | Full research paper | | `evaluation_results.json` | Training metrics | | `requirements.txt` | Dependencies | | `app/` | Backend agents (Flask API) | | `frontend/` | React frontend | ## Evaluation See [EVALUATION.md](EVALUATION.md) for detailed metrics and production readiness assessment. ## Citation ```bibtex @software{contextflow, title={ContextFlow: Predictive Doubt Detection in Adaptive Learning Systems}, author={ContextFlow Team}, year={2026}, version={1.0}, url={https://huggingface.co/namish10/contextflow-rl} } ```