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🧠 PersonaLearn: The Adaptive AI Tutor

Built in just 3 hours. 🚀

A personalized learning platform that translates complex academic concepts into the language of your personal interests (Minecraft, Marvel, Cricket, and more).

Python Streamlit Gemini

💡 The Problem

Cognitive science shows that retention improves significantly when new information is anchored to familiar mental models. However, standard textbooks are "one size fits all."

🛠️ The Solution

PersonaLearn uses Google's Gemini Pro to dynamically reframe syllabi (UPSC, School, General) into analogies based on the user's hobbies.

  • Don't understand Trigonometry? Read an explanation based on Cricket.
  • Confused by Blockchain? Learn it through Minecraft block analogies.

✨ Key Features

  • Adaptive Syllabus: Pre-loaded tracks for School, SSC, UPSC, and General Knowledge.
  • Interest Mapping: Select your "lens" (e.g., K-Pop, Marvel, Cooking).
  • Dual Roles: * 🎓 Student Mode: Learn and track progress.
    • 👨‍🏫 Professor Mode: Edit and curate the syllabus.
  • AI Quiz Generator: Generates MCQs instantly based on the specific analogy used in the lesson.
  • Gamification: Live scoring and progress tracking.

🚀 Quick Start

Prerequisites

  • Python 3.9+
  • A Google Gemini API Key (Free tier available)

Installation

  1. Clone the repository

    git clone [https://github.com/yourusername/personalearn.git](https://github.com/yourusername/personalearn.git)
    cd personalearn
  2. Install dependencies

    pip install -r requirements.txt
  3. Set up credentials Create a .env file in the root directory:

    GOOGLE_API_KEY=your_api_key_here
  4. Run the App

    streamlit run main.py

📸 Screenshots

image alt image alt

🏗️ How it Works

  1. Input: User selects a topic (e.g., "Photosynthesis") and an Interest (e.g., "Marvel").
  2. Processing: The app constructs a prompt for gemini-pro to explain the concept using the selected interest domain.
  3. Output: A translated explanation is displayed.
  4. Assessment: The AI dynamically generates a quiz based only on the explanation provided. image alt

👥 Team Innovsphere

Built during a 3-hour rapid development sprint by:

  • [Chaitanya]
  • [Nishant]
  • [Lavisha]
  • [Ayush]
  • [Soham]

📄 License

MIT License

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