Focus Mate

A smart study planner that uses AI-assisted difficulty estimation and rule-based logic to generate personalized daily study schedules before exams.

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Inspiration

Students often struggle with deciding what to study first and how much time to allocate before exams, especially when multiple subjects have different exam dates and difficulty levels. Most existing planners are static and do not adapt to a student’s confidence or performance.
This inspired us to build Focus Mate, a tool that intelligently assists students in planning their daily study time in a simple and explainable way.

What it does

Focus Mate is an AI-assisted study planner that helps students allocate their daily study hours based on:

  • Individual subject exam dates
  • User confidence levels
  • Past performance (optional)

The application predicts subject difficulty using AI and combines it with exam urgency to generate a personalized daily study plan, clearly showing how much time to spend on each subject and how many days are left before the exam.

How we built it

The frontend is built using React with Tailwind CSS for a clean, responsive, and modular UI.
The application communicates with the backend using REST APIs.

For AI, we developed a lightweight machine learning backend using Python and Flask.
We used a Decision Tree model from scikit-learn to predict subject difficulty based on user confidence and past scores.

Instead of overcomplicating the system, we used a hybrid approach:

  • AI handles uncertainty (difficulty prediction)
  • Rule-based logic handles deterministic factors (exam dates and urgency)

This keeps the system fast, explainable, and reliable.

Challenges we ran into

Using AI in a way that adds real value without becoming a black box

  • Handling per-subject exam dates and urgency correctly
  • Ensuring smooth frontend–backend communication
  • Deploying a Flask ML backend reliably within hackathon time limits

Each challenge helped us improve both the technical design and the user experience.

Accomplishments that we're proud of

Built a complete full-stack AI application within a hackathon timeframe

  • Designed a hybrid AI system combining machine learning with rule-based logic
  • Successfully deployed an ML backend on Render and connected it to the frontend
  • Created an intuitive UI with dynamic emoji-based confidence input
  • Implemented per-subject exam urgency for realistic study planning

What we learned

  • How to design explainable AI systems instead of black-box solutions
  • Deploying and managing Flask ML services in production environments
  • Structuring a React application for clean, modular scalability
  • Managing environment variables securely across frontend and backend
  • Collaborating efficiently using GitHub during a hackathon

What's next for Focus Mate

Weekly and multi-day study plan generation

  • User accounts with progress tracking
  • Personalization based on historical study behavior
  • Enhanced ML models with more learning signals
  • Cloud deployment and scalability improvements

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