🧠 Adaptive Personalized Learning System

An AI-powered, interactive learning platform that adapts to your learning style, background, and goals. Built with Streamlit, LangGraph, and OpenAI, this system simulates a personalized tutor that creates tailored lesson plans, assesses your understanding, and dynamically adjusts the curriculum.

✨ Key Features

• Personalized Curriculum: Generates lessons based on your Learning Style (Active, Reflective, etc.), Background Level, and Interests.
• Dynamic Adaptation: Uses LangGraph to create a stateful learning loop. It assesses your performance after every lesson and decides whether to:
  • Reinforce: Provide more examples.
  • Reteach: Explain concepts from first principles if you're stuck.
  • Advance: Move to the next topic with a "Graduation Report".
• Multi-Source Citations: Automatically aggregates and cites resources from:
  • Arxiv (Direct API integration for research papers)
  • Google Scholar (Academic Articles)
  • YouTube (Video Tutorials)
  • Web Search (Tavily)
• Interactive Exercises: Generates coding exercises (e.g., PyTorch/TensorFlow) relevant to the topic.
• Persistent Memory: Remembers your progress, scores, and knowledge gaps across the session.

🏗️ Architecture

The system is split into two main components:

1. Frontend (app.py): A Streamlit dashboard that:

   • Captures user profile data.
   • Renders generated lessons, code snippets, and specific citations.
   • Visualizes assessment scores (Confidence, Accuracy).
   • Manages session state and user interactions.

2. Backend Logic (workflow.py): A LangGraph workflow that manages the learning state.

   • Nodes: generate_lesson, assess_learning, evaluate_next_step.
   • Tools: Integrates TavilySearch, GoogleScholar, YouTubeSearch, and arxiv library.
   • LLM: Uses gpt-4o-mini for high-quality, pedagogical content generation.

🚀 Setup & Installation

Prerequisites

• Python 3.10+
• OpenAI API Key
• Tavily API Key (for web search)
• SerpAPI Key (for Google Scholar)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/topic-learning-agent.git
   cd topic-learning-agent

2. Install dependencies:

   pip install -r requirements.txt

3. Configure Environment Variables: Create a .env file or set up .streamlit/secrets.toml with the following keys:

   OPENAI_API_KEY="sk-..."
   TAVILY_API_KEY="tvly-..."
   SERP_API_KEY="..."

Running the App

Run the Streamlit application:

streamlit run app.py

The app will open in your browser at http://localhost:8501.

🛠️ Usage

1. Create Profile: On the sidebar, select your Learning Style, Experience Level, and Goal.
2. Choose Topic: Enter a topic you want to master (e.g., "Attention is all you need", "Quantum Computing").
3. Start Journey: Click "Generate Teaching Plan".
4. Learn & Interact: Read the lesson, review citations, and solve compliance exercises.
5. Assess: Click "Assess Understanding" to take a quiz. The AI will grade you and determine your next step.