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https://github.com/rolbester/adaptive-learning-platform

Full-Stack Web Application for an AI-Powered Educational Platform
https://github.com/rolbester/adaptive-learning-platform

ai chat education javascript llms pipeline python pythonflask quizzes react reactrouterdom tensorflow

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Full-Stack Web Application for an AI-Powered Educational Platform

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README 

          

AI-Powered Educational Platform where you can become a student of an AI operated school.

An adaptive learning system that personalizes educational content, quizzes, and study materials based on user performance. Integrates a Large Language Model (LLM) for question-answering and content recommendations, leveraging Meta’s Llama 2 (7B HF) as the core NLP engine.

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1. Table of Contents

2. Project Overview

3. Key Features

4. Tech Stack & Tools

5. Project Structure

6. Installation & Setup

7. Usage

8. Seeding the Database

9. Taking a Quiz

10. Fetching Recommendations

11. Asking Questions (LLM)

12. Environment Variables

13. Notes on Llama 2 Usage

14. License

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1. Project Overview

This platform provides an adaptive learning experience using AI-driven quizzes, personalized study resources, and a real-time dashboard. By analyzing user performance data, it recommends targeted materials to address individual learning gaps.

The system also features a chat-based question assistant powered by Llama 2, enabling students to ask topic-specific queries and receive detailed explanations.



2. Key Features

Adaptive Quizzes:

Users complete quizzes, and their performance is recorded to gauge strengths/weaknesses.



Personalized Recommendations:

An AI-based recommendation engine leverages stored user progress data to suggest relevant study materials.



LLM Question-Answering:

A question assistant (chat interface) integrates Llama 2 to handle user queries and provide explanations.



Real-Time Analytics:

Dashboards for users/instructors to track quiz performance, visualize correctness, and identify learning trends.



Modular Architecture:

Clean separation of backend (Python) and frontend (React) services, facilitating scalability and maintainability.

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3. Tech Stack & Tools

Languages & Frameworks



Backend: [Python], [FastAPI or Flask], [MongoDB] for data storage

Frontend: [React], [TypeScript] for a dynamic user interface

LLM: Meta Llama 2 (7B HF) for NLP/Q&A

Other Tools



Hugging Face for LLM Inference API

Chart.js or similar for analytics dashboards

TailwindCSS for responsive design (if applicable)

VS Code as the primary IDE (with debugging extensions)

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4. Project Structure

└── ...

backend/app/main.py – Entry point for the Python backend.

backend/app/routes/*.py – Contains API endpoints for quiz evaluation, recommendations, chat, etc.

backend/app/models/*.py – Core logic for AI-based recommendations, DB interactions, and QA pipeline.

frontend/src/* – React/TypeScript code for UI, including quiz forms, dashboards, and chat assistant.



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5. Installation & Setup

Clone the Repository

cd AI-Edu-Platform



Backend Setup

cd backend

python3 -m venv venv

source venv/bin/activate  # or venv\Scripts\activate on Windows

pip install -r requirements.txt

Create a .env file (see Environment Variables below).



Frontend Setup

cd ../frontend

npm install

Adjust .env or configuration if needed for the frontend (e.g., REACT_APP_API_URL).



Run the Backend

# from the backend/ directory

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Open a browser at http://127.0.0.1:8000/docs to see the interactive API docs (Swagger UI).



Run the Frontend

# from the frontend/ directory

npm start

By default, it starts at http://localhost:3000.

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6. Usage

6.1 Seeding the Database

(Optional step, if you want sample resources for recommendations.)



POST to http://127.0.0.1:8000/api/seed-resources/ using browser, Postman, or Swagger UI.

This inserts example resources (e.g. “Algebra Basics,” “Calculus 101”) into the Resources collection.



6.2 Taking a Quiz

Navigate to http://localhost:3000/quiz.

Enter a user ID (e.g., user123) and answer the quiz questions.

The system records your results and updates your progress in MongoDB.



6.3 Fetching Recommendations

Go to http://localhost:3000/learning-path.

Enter the same user ID used for the quiz.

Click Get Recommendations. If your score for a topic was below the threshold (e.g., <60), you’ll see relevant resources recommended for that topic.



6.4 Asking Questions (LLM)

Visit http://localhost:3000/chat.

Type in a question or statement. The AI chatbot, powered by Llama 2, will respond.

Each new user message re-sends the entire conversation to the backend endpoint (/api/chat/), which calls the Hugging Face Inference API for Llama 2.



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7. Environment Variables

In backend/.env, define:

MONGO_URI="mongodb+srv://:@.mongodb.net/AdaptiveLearning"

HUGGINGFACE_TOKEN="hf_XXXXXXXXXXXX"

MONGO_URI: Connection string for your MongoDB (Atlas or local).

HUGGINGFACE_TOKEN: Your personal access token from Hugging Face.

Required to call Llama 2 (and other HF models) via the Inference API.

(Optional) MODEL_ID="meta-llama/Llama-2-7b-hf" if your code references an environment variable for the model ID.

You may also have a frontend .env for environment-specific settings (like REACT_APP_API_URL).



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8. Notes on Llama 2 Usage

License Acceptance: Llama 2 typically requires accepting the license on Hugging Face.

API Access: The free Inference API might block large models (403 errors). If so, either:

Use a smaller variant, or

Sign up for a paid Inference Endpoint on Hugging Face, or

Host the model locally on GPU (requires setup).

Rate Limits & Performance: The free tier can be slow or limited. For production usage, consider a self-hosted or paid solution.



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10. License

MIT License - open-source license.



Disclaimer: This project is for educational demonstration. The Llama 2 model usage is subject to Meta’s license terms and may not be used for commercial purposes without prior permission.



Enjoy exploring the AI-Powered Educational Platform! Feel free to raise issues or contribute via pull requests. If you have any questions, contact: [RoLBester]



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