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📚 Case Study: AI-Powered Multi-Agent Educational System

Overview

The AI-Powered Multi-Agent Educational System is an intelligent, interactive academic platform designed for students and teachers. Built using LangGraph and Flask, and powered by Gemini (Google) and DeepSeek, this system supports the automation and personalization of education through multi-agent collaboration.

This innovative solution streamlines teaching plan creation, automates assignment and quiz generation, and provides real-time feedback and guidance for students, making academic tasks more effective and engaging.


🎯 Objectives

  • To automate teaching tasks and reduce workload for educators.
  • To provide interactive, AI-driven learning tools for students.
  • To enhance educational quality using reasoning-based feedback and intelligent agents.

🛠️ Tech Stack

Technology Role
LangGraph Agentic framework for coordinating multi-agent workflows
Flask Backend server for APIs and routing
Gemini (Google) For AI-based quiz generation, reasoning, and feedback
DeepSeek For intelligent assignment generation
HTML/CSS Basic frontend for teacher/student interfaces

✨ Key Features

👩‍🏫 For Teachers:

  • Teaching Plan Generator: Input a subject and get a structured syllabus/lesson plan.
  • Assignment Creator: AI-generated assignments using Gemini + DeepSeek based on the uploaded plan.

🧑‍🎓 For Students:

  • Teaching Plan Viewer: Access structured academic plans uploaded by the teacher.
  • Quiz Generator: Subject-related quizzes generated on the fly by Gemini.
  • Interactive Feedback: Incorrect answers are explained through a reasoning agent, giving clear learning support.
  • Assignment Portal: View and download personalized assignments based on class material.

🔄 Agentic Workflow (Powered by LangGraph)

  1. Main Agent: Accepts user input (teacher or student) and routes requests.
  2. Plan Generator Agent: Builds teaching plans based on subjects.
  3. Assignment Agent: Uses Gemini + DeepSeek to create assignments.
  4. Quiz Agent: Uses Gemini to create quizzes.
  5. Feedback Agent: Evaluates student responses and gives intelligent explanations for mistakes.

💡 Use Cases

  • A teacher uploads a subject and receives a complete teaching plan and assignment.
  • A student selects a subject and takes a quiz; AI gives personalized feedback.
  • Students review teaching plans and complete auto-generated assignments.

🚀 Getting Started

git clone https://github.com/BlueOrbitAi/ai-academy-langgraph.git
cd ai-academy-langgraph

Set environment variables, run Flask, and access the platform.


🔮 Future Enhancements

  • Real-time collaborative classroom dashboard
  • Student progress tracking using analytics
  • Integration with LMS (Learning Management Systems) like Moodle or Google Classroom
  • Speech-to-text and voice quiz interaction

👨‍💻 Author

Built with ❤️ by Muhammad Abdullah at BlueOrbitAi 🔗 GitHub Repo: github.com/BlueOrbitAi/ai-academy-langgraph 🌐 Explore More Projects: blueorbitai.com


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