Prompt2Learn

A web-based application designed to enhance the teaching and learning experience using AI-driven features such as transcription, summarization, automated quiz generation, performance analytics, and a context-aware RAG (Retrieval-Augmented Generation) chatbot.

Table of Contents

1. Overview
2. Features
3. Project Structure
4. Tech Stack
5. Getting Started
6. Usage

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Overview

Prompt2Learn aims to provide:

• Teachers an easy way to upload lecture materials (audio, PPTs), generate summaries, create automated quizzes, and review student performance analytics.
• Students quick access to lecture summaries, quizzes, personalized feedback, and a contextual chatbot to resolve doubts using past lecture content.

This repository contains code for both the backend (Spring Boot) and frontend (React), along with a Python-based Flask server dedicated to handling RAG chatbot interactions and content summarisation.

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Features

• Lecture Upload: Upload PPTs or audio files for AI-driven transcription and summarization.
• Automated Quiz Generation: Create quizzes based on uploaded materials for instant student assessments.
• Performance Analytics: Gain insights into student performance, identifying strengths and weaknesses.
• RAG Chatbot: A context-aware chatbot that references previously uploaded content to answer student queries accurately.
• Teacher Insights: Data-driven reports that highlight areas where students need additional support.
• Flask Server for AI Services: A dedicated Python-based Flask server running the RAG chatbot and summarisation services.

Project Structure

├── Portal
│   └── Portal # Spring Boot backend source code
├── portalfront
│   └── src # React frontend source code
├── flask_server # Flask server for RAG chatbot and summarisation
├── prompt2LearnIdea.pdf # Concept/idea document (not required for build)
└── README.md # Project README (this file)

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Tech Stack

• Frontend: React
• Backend: Spring Boot (Java)
• Database: MongoDB (or any other database you configure)
• AI/ML Components & Server:
  • Python-based Flask server that runs the RAG chatbot and content summarisation services.
  • AI/ML libraries and APIs for transcription, summarization, and quiz generation.

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Getting Started

Prerequisites

1. Java 20+ installed (for Spring Boot)
2. Node.js (v14+ recommended) and npm or yarn (for React)
3. Python 3.9+ (for the Flask server)
4. MongoDB or another database (if not using an in-memory DB)

Installation

1. Clone the repository

   git clone [https://github.com/jas2506/Prompt2Learn]
   cd Prompt2Learn

2. Backend Setup (Spring Boot)

   • Navigate to the backend folder (e.g., Portal/Portal).
   • Ensure you have the required dependencies (Maven or Gradle).
   • Configure your database settings in the application properties or YAML file.
   • Build the project:

   mvn clean install

   • Run the application:

   mvn spring-boot:run

   • The backend should be accessible on a configured port.

3. Frontend Setup (React)

   • Navigate to the portalfront folder.
   • Install dependencies:

   npm install

   • Start the development server:

   npm run dev

4. Flask Server Setup (Python)

   • Navigate to the project root where flask server is located.

   • Set up a virtual environment and install required packages:

   • Run the Flask server:

   python groq_test.py
   python rag_api.py

   • The Flask server should be running on its default port handling RAG chatbot and summarisation requests.

Usage

1. Upload Lecture Materials: Teachers log in to upload PPTs or audio files via the backend interface, triggering AI-based transcription and summarisation.
2. Generate Summaries & Quizzes: Once materials are processed, the system auto-generates summaries and quiz questions.
3. Student Dashboard: Students access summarized content, take quizzes, and monitor their performance through the React-based frontend.
4. RAG Chatbot & Summarisation: The Python-based Flask server handles requests for the RAG chatbot, providing context-aware answers and additional content summarisation.