StudyPilot AI

StudyPilot AI is an AI-powered study planning assistant that generates personalized day-by-day exam preparation schedules using LLMs and stores plans permanently using MongoDB.

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Inspiration

Many students struggle to create effective study schedules before exams. Most students waste time deciding what to study instead of actually studying. I wanted to build an AI-powered assistant that can instantly generate personalized study plans and help students prepare smarter.

What it does

StudyPilot AI generates personalized day-by-day study schedules based on a student's exam timeline and goals. Users simply enter their exam details, and the AI creates a structured preparation plan.

The application also stores generated plans using MongoDB so users can access their study plans later.

How we built it

We built the project using:

  • Next.js for the frontend and backend API routes
  • React for UI and state management
  • Tailwind CSS for styling
  • OpenRouter API for AI-generated responses
  • MongoDB Atlas for database storage

The frontend collects user input, sends it to the backend API, which then communicates with the AI model. The generated study plan is saved into MongoDB and displayed back to the user.

Challenges we ran into

One of the biggest challenges was configuring AI APIs and handling quota limitations. We initially integrated Gemini API but faced quota and billing restrictions, so we switched to OpenRouter for a more accessible workflow.

Another challenge was connecting MongoDB Atlas and debugging authentication issues while configuring database access securely.

Accomplishments that we're proud of

  • Successfully built a complete fullstack AI application
  • Integrated AI-generated study planning
  • Connected MongoDB Atlas for persistent storage
  • Created a functional and responsive user interface
  • Built an end-to-end workflow from frontend to database

What we learned

During this project, we learned:

  • Fullstack application architecture
  • API integration and debugging
  • MongoDB Atlas configuration
  • Environment variable management
  • AI workflow integration using LLM APIs
  • Real-world debugging and deployment workflows

What's next for StudyPilot AI

Future improvements include:

  • Progress tracking and task completion
  • Authentication system
  • AI-powered schedule replanning
  • Calendar integration
  • PDF export for study plans
  • Dashboard for viewing previous plans

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