AlgoMentor – AI-Powered Educational Platform

🚀 Project Overview

AlgoMentor is an AI-powered educational platform designed to revolutionize learning through the Socratic method. The platform engages students with thought-provoking questions, promotes critical thinking, and provides a comprehensive learning environment using:

• Multimodal support (text, images, videos)
• Interactive tools for learning concepts dynamically
• Personalized paths for continuous learning and improvement

🎯 Vision

The goal is to create an intelligent Socratic learning assistant that can mimic a personalized tutor and guide students to understand concepts deeply. The platform enables:

✅ AI Chat Assistant for guided Socratic questioning
✅ Multimodal learning (Text, Image, Video-based queries)
✅ Integrated Code Editor for real-time coding & debugging
✅ Interactive Visualizations for Data Structures & Algorithms
✅ Self-Evaluation & Activity Tracking with performance insights
✅ Gamified Learning using badges & motivational AI assistant

Some future plans include collaborative learning, integration with external educational platforms, and live certified courses powered by cutting-edge AI models.

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🏗️ Architecture

🔑 Key Components:

1. AI Chat Assistant: Uses Socratic questioning to deepen understanding.
2. Multimodal Learning Support: Accepts text, image, and video-based queries.
3. Interactive Visualization Tools: Dynamic DSA visualizations.
4. Integrated Code Editor: Enables real-time coding and debugging.
5. Self-Evaluation Tools: Customizable quizzes for knowledge assessment.
6. Activity Tracking & Heatmaps: Monitors study patterns.
7. Gamification: Encourages engagement through achievement badges.
8. Motivational AI Assistant: Provides daily inspiration for learners.

🧠 Current LLM Model

• Framework: Groq
• Model: LLama 3 70B

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✨ Features

✅ In-Scope Features

• AI Chat Assistant with Socratic method-based learning.
• Multimodal Query Support: Accepts text, images, and video-based doubts.
• Interactive DSA Visualizations: Real-time demonstrations of algorithms.
• Resource Suggestion Engine: AI-driven learning material recommendations.
• Integrated Code Editor: Supports live coding & debugging.
• Custom Self-Evaluation Tests: Adaptive difficulty quizzes.
• Study Activity Heatmaps: Tracks user progress visually.
• Gamification & Achievement Badges: Rewards for learning milestones.
• Motivational AI Assistant: Daily quotes to inspire students.

❌ Out of Scope

• Guaranteed accuracy of AI-generated responses: Users should verify critical information independently.

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🔮 Future Opportunities

1. Collaborative Learning: Study groups, discussion forums, and peer learning.
2. Integration with External Learning Platforms: Access to high-quality courses.
3. Live Certified Courses: Expert-led sessions for advanced topics.
4. Gemini Model Integration: More personalized AI-driven responses.
5. Lang Graph Agent AI Assistant: Improved navigation & accessibility.
6. Optimized Inference Time: Enhanced system performance.

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🛠️ Challenges Faced During Development

🚧 Technical Challenges & Solutions

1. JSON Response Issues from LLM

   • Problem: Inconsistent JSON data parsing errors.
   • Solution: Added validation checks & logging for unexpected responses.

2. Gemini & Langchain Integration Challenges

   • Problem: Compatibility issues affecting smooth AI interaction.
   • Solution: Switched to alternative models, awaiting future Gemini updates.

3. CORS Policy Issues

   • Problem: Frontend API access restrictions.
   • Solution: Configured server headers to allow cross-origin requests.

4. Multimodal Chat Implementation

   • Problem: Handling diverse input types (text, images, etc.).
   • Solution: Researched & integrated a suitable multimodal framework.

5. Heatmap Generation for Study Streaks

   • Problem: Lack of clear documentation for heatmap visualization.
   • Solution: Experimented with various libraries to find an optimal solution.

6. LangGraph Agent Integration

   • Problem: Difficulty structuring all features as LangGraph nodes.
   • Solution: Exploring ways to resolve documentation gaps.

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🛠️ Installation & Setup Guide

🔹 Backend Setup (Python & FastAPI)

1. Clone the repository

   git clone https://github.com/Angad-2002/Socratic-Learning.git
   cd Socratic-Learning

2. Install dependencies

   pip install -r requirements.txt

3. Run the backend server

   cd backend-ml
   python main.py

   Backend Running at: http://0.0.0.0:8000

🔹 Frontend Setup (React & Vite)

1. Navigate to frontend directory

   cd frontend

2. Install dependencies

   npm install

3. Start the development server

   npm run dev

   Frontend Running at: http://localhost:3000

🔹 Backend API (Node.js & Express)

1. Navigate to backend directory

   cd backend

2. Run the server

   node server.js

   Backend Running at: http://localhost:5000

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💡 Contributing

Contributions are welcome! If you have ideas to enhance the platform, feel free to:

• Fork the repository
• Create a feature branch
• Submit a pull request

📜 License

This project is licensed under the MIT License.