Project Name: Guru Cortex

Project Overview Project Cortex is a personalized, proactive AI study buddy designed to fundamentally change how students interact with educational AI. Unlike most AI tutors that simply wait to answer questions, Cortex utilizes a proactive approach—it actively pays attention to the student, notices when they are confused, and offers assistance before they even have to ask.

Core Problem & Solution Current educational AI relies on reactive prompts. Project Cortex solves this by acting as a highly observant tutor that provides affordable, accessible, and localized learning. It achieves this through a Privacy-First Hybrid Architecture (Edge + Cloud), ensuring that sensitive vision processing happens locally on the user's device while complex reasoning is handled securely.

Key Features

• Smart Vision System & Emotional Check-ins: Using a webcam, the system monitors for gaze, fatigue, and frustration. It detects when a student is confused or tired and proactively suggests new approaches or study breaks.

• Local Knowledge Base (DeepSearch): Instead of relying on generic internet data, Cortex indexes the student's personal PDFs and notes. This allows it to provide highly contextual answers based on the user's actual study materials.

• Active Learning Mode: Features a "Teach Mode" that prompts the student to explain concepts back to the AI to verify and enhance comprehension.

• Multilingual Support: Offers conversational AI capabilities in Hindi and other regional languages.

• Drawing & Diagrams: Automatically generates visual flowcharts to help simplify complex text.

• Focus Features: Detects distraction and actively alerts the student to stay focused on their screen.

Technology Stack & Architecture The system is built on a Hybrid Edge-Cloud model to balance performance with strict privacy:

• AI Models: Powered by Quantized Google Gemma (2B/9B) for language processing and Whisper Tiny for efficient voice input and output.

• Vision & Emotion Processing: Utilizes MediaPipe and OpenCV locally on the device, ensuring 100% video privacy without sending visual data to the cloud.

• Storage & Memory: Employs ChromaDB for a Local GraphRAG vector store (to search notes) and SQLite to track student progress and spaced repetition.

• Interface & Orchestration: Features a Next.js dynamic dashboard with Framer Motion UI, orchestrated by a Django/FastAPI server hosted on Google Cloud Run. Mermaid.js is used for generating diagrams.

Future Development Roadmap

• Multimodal Learning: Integrating Multimodal Gemma to analyze diagrams from textbook photos and digitize handwritten notes via OCR.

• Cross-Platform App: Transitioning to Flutter for seamless desktop-to-mobile syncing and running Gemma models locally on mobile devices.

• Privacy-Safe Study: Enabling secure collaborative learning between students using peer-to-peer (P2P) encryption and the ability to share Knowledge Graphs without sharing raw documents.