AI Study Planner using Gemini 3

AI that turns notes into plans.

Comment

📘 AI Study Planner – Project Story

💡 Inspiration

As a student, I’ve often faced situations where exams or interviews were just days away, and I had large amounts of unstructured notes, PDFs, or syllabi. The hardest part wasn’t studying itself — it was figuring out what to study first, what matters most, and how to plan time effectively.

I wanted to build something that could instantly turn messy notes into clarity: a clean summary, actionable tasks, and a realistic study plan. With the release of Gemini 3, this hackathon felt like the perfect opportunity to solve a real student pain point using next-generation AI reasoning.

🚀 What the Project Does

AI Study Planner is an AI-powered workspace that helps students prepare smarter by:

  • Summarizing long notes or syllabus content
  • Extracting clear, actionable study tasks
  • Generating a structured, day-wise study plan
  • Allowing users to export the plan as a PDF for offline revision

The application is designed to be simple, fast, and distraction-free, so students can focus entirely on preparation rather than planning.

🛠️ How I Built It

Tech Stack

  • Next.js (App Router) – frontend & API routes
  • TypeScript – type safety and maintainability
  • Tailwind CSS – clean, modern UI
  • Gemini 2.5 Flash (Gemini 3 family) – AI reasoning and content generation

Gemini Integration

The core of the application uses the Gemini API to analyze user-provided notes. I carefully designed prompts to ensure Gemini returns output in a strict, structured format, which allows the frontend to reliably parse and display:

  • A concise summary
  • A list of tasks
  • A day-wise study plan

Gemini’s fast reasoning capabilities made it possible to generate meaningful, structured responses even for large inputs, while keeping latency low.

📚 What I Learned

  • How to design deterministic AI outputs instead of free-form text
  • Best practices for integrating LLMs into real products
  • Handling API limitations, rate limits, and model availability
  • Structuring a clean SaaS-style UI with a clear separation between landing page and workspace
  • Exporting AI-generated content into real-world formats like PDF

This project significantly improved my understanding of prompt engineering, AI reliability, and product-focused frontend design.

⚠️ Challenges I Faced

  • Model availability & quotas:
    Understanding which Gemini models were available, supported, and usable under free-tier limits required careful testing and iteration.

  • Output consistency:
    Early versions produced mixed or repeated content. I solved this by enforcing strict output markers and robust parsing logic.

  • UX balance:
    Making the interface feel professional without overwhelming users was challenging. I iterated multiple times to achieve a clean, focused workspace experience.

🌟 What’s Next

Future improvements could include:

  • User accounts and saved study plans
  • Progress tracking and reminders
  • PDF uploads with document parsing
  • Dark mode and accessibility enhancements

🎯 Final Thoughts

AI Study Planner is built to solve a real, everyday problem for students. By combining thoughtful UI design with Gemini’s powerful reasoning capabilities, the project demonstrates how AI can move beyond chat interfaces and become a practical productivity tool.

Thank you for checking it out!

Built With

Share this project:

Updates