EduMap

Inspiration

Many students experience uncertainty when deciding their academic and career paths. With countless courses, certifications, and career options available, it is often difficult to determine the right learning sequence to achieve their goals. We wanted to build a platform that acts as a personalized mentor, helping students make informed decisions rather than relying on scattered online resources. This inspired us to create EDUMap, an AI-powered educational roadmap builder that generates customized learning paths based on a user's interests, skills, educational background, and career aspirations.

What it does

EDUMap is an AI-powered platform that helps students plan their educational journey through personalized learning roadmaps. The platform allows users to: Create a secure account and log in using JWT authentication. Build and manage their learner profile. Generate personalized educational roadmaps using AI. Interact with an AI chatbot to clarify doubts and receive career guidance. Upload relevant files and documents for future AI-assisted analysis. Store user profiles, chat history, and roadmaps securely using MongoDB Atlas. Our goal is to simplify educational planning and make career guidance more accessible through artificial intelligence.

How we built it

Our team of four collaborated to design and develop EDUMap using a modern full-stack architecture. Frontend: Streamlit, Python Backend: FastAPI, REST APIs, JWT Authentication, PyMongo, Database, MongoDB Atlas, Artificial Intelligence, Google Gemini API, LangChain The frontend communicates with the FastAPI backend through REST APIs. User authentication is handled using JWT tokens, while MongoDB Atlas stores user information, profiles, generated roadmaps, chat history, and uploaded file metadata. LangChain integrates the Google Gemini model to generate intelligent educational recommendations and provide conversational chatbot support.

Challenges we ran into

Building EDUMap involved several real-world engineering challenges. Encountered repeated 500 Internal Server Errors during user registration and login, requiring extensive backend debugging. Faced API communication issues between the Streamlit frontend and FastAPI backend due to incorrect endpoint configurations and request handling. Spent significant time resolving environment configuration problems caused by missing or incorrectly configured .env files. Experienced cloud database connection failures while integrating MongoDB Atlas. Learned that MongoDB database users must have Read and Write to Any Database privileges for authentication and CRUD operations to function correctly. Resolved issues related to incorrect MongoDB connection strings, authentication credentials, and network access settings. Debugged JWT authentication flow and secure token generation. Coordinated code integration across four team members while maintaining project consistency and resolving merge conflicts. Although these challenges slowed development initially, they significantly improved our understanding of full-stack application development and cloud deployment.

Accomplishments that we're proud of

Successfully developed a complete AI-powered educational guidance platform from scratch. Integrated FastAPI, Streamlit, MongoDB Atlas, LangChain, and Google Gemini into a single application. Implemented secure user authentication with JWT. Built an AI chatbot capable of assisting users with educational and career-related queries. Created personalized educational roadmaps tailored to individual learner profiles. Successfully connected a cloud database and managed user data securely. Worked effectively as a team of four, dividing responsibilities while integrating all components into one cohesive application.

What we learned

Throughout this project, we gained valuable experience in both technical and collaborative aspects of software development. We learned: Full-stack application architecture using FastAPI and Streamlit. Secure authentication using JWT. Cloud database integration with MongoDB Atlas. Environment variable management and secure configuration. AI integration using LangChain and Google Gemini. REST API development and frontend-backend communication. Debugging backend issues, authentication problems, and cloud connectivity errors. Version control, collaborative development, and effective teamwork using Git and GitHub.

Most importantly, we learned that solving real-world development challenges requires patience, systematic debugging, and strong team collaboration.

What's next for PROJECT

We plan to expand EDUMap into a comprehensive AI-powered learning companion by introducing: Progress tracking and roadmap completion analytics. Personalized course and certification recommendations. Resume analysis and AI-powered improvement suggestions. Internship and job opportunity recommendations. Calendar integration for study planning and reminders. Roadmap sharing and collaboration features. Multilingual support for wider accessibility. Advanced document analysis for uploaded resumes and academic transcripts. Deployment to the cloud for public access and scalability. Our long-term vision is to make EDUMap an intelligent educational companion that helps students confidently navigate every stage of their learning and career journey.