How AI Can Help You Build Better Study Routines

Harness the Power of AI to Fine-Tune Your Study Sessions and Achieve Maximum Productivity with Personalized Feedback

In my last post I introduced PomoCoach, a simple app with a mission to help people keep it together, maintain their focus, and avoid the burnout that inevitably follows a never ending grind. At its core, PomoCoach uses the Pomodoro technique to break up study sessions into manageable chunks. Pretty straightforward stuff. But life has a funny way of throwing curveballs.

Shortly after that post, the company I work for decided to let go of most of my team. With more time on my hands, I poured it into building something that didn’t just function, but solved a bigger problem: giving people the insight they need to make the most of their study time. Enter PomoCoach v2.

This version doesn’t just tick away in the background while you work. It learns from you. It takes in your study habits, your break times, and it gives you something in return: feedback. Real, actionable feedback that you can use to get better at managing your time.

The Mission

The mission has been the same since the start: build something that helps people study smarter, not harder. But I knew I wouldn’t be satisfied with just timing study sessions. I wanted to dig into the data, see the patterns, and give users insights into how they work. We live in a world where everything is data, so why not use it to actually make your life better?

PomoCoach v2 tries it’s best. Once a user creates a profile, the AI does the heavy lifting. It analyzes their study sessions and breaks in real time, then offers advice that’s designed to optimize their routine. Maybe they’re burning out too fast, maybe they need longer breaks. Either way, the AI steps in and says, “Here’s what you need to do better.”

The Technical Details

The frontend of PomoCoach is built with Next.js and styled with Tailwind CSS for a streamlined experience. I skipped the App Router and leaned into Next’s file based routing system. Here’s how it works: drop a file in the pages folder, and Next treats it as a route automatically. Pages map directly to URLs. Simple and efficient is all I know.

Here’s a quick look at the file structure:

pages
├── _app.js
├── _document.js
├── api
│ └── hello.js
├── dashboard.js
└── index.js
components
├── ai
│ └── AIAnalyzer.js
├── charts
│ ├── BarGraph.js
│ └── DonutChart.js
└── core
 ├── Container.js
 ├── Error.js
 ├── Layout.js
 ├── Modal.js
 ├── NavBar.js
 ├── Page.js
 ├── StudyStats.js
 └── Timer.js
context
└── AppContext.js

Rendering

The app mounts through the _app.js file, where I created a layout to keep the navbar persistent across all pages . This eliminates any useless re-renders. If you’re logged in, you get access to the analytics and AI features. If not, you get just the timer and a sign up prompt. Conditional rendering keeps it clean and functional. The UI flips from green (study mode) to blue (break mode), a simple visual cue that helps users stay on track.

State

State management is handled via the context provider. That means less prop drilling and more sanity. Everything related to the timer’s countdown function stays in Timer.js, while other states like user profiles, study/break modes, and session times are handled centrally. The idea here is to keep the app lightweight while still offering flexibility.

AI Integration: Making It Smarter

Now let’s talk about what makes PomoCoach v2 really shine: the integration of OpenAI’s GPT-3.5 turbo model. Once users start accumulating data through their study sessions, the AI gets to work. It analyzes everything from the study times, the break times, and everything in between and then spits out feedback designed to help users get the most out of their routine.

The kicker? Everything happens server-side. The frontend just sends a POST request with the relevant data, and the backend does the heavy lifting. That way, I’m in compliance with privacy laws, and the whole process is as seamless as possible. The AI results are parsed and displayed in the AIAnalyzer component; a straightforward feedback loop with real personalization.

Looking Ahead

Right now, the server runs off my personal OpenAI API key. It’s fine for now, with a $20 balance covering a few thousand requests, but that’s not sustainable if I want to scale this thing. Personally I think the next realistic move could be a premium model and charge for additional requests after a certain limit. That way, the burden of cost is shifted, and we can take on more users without breaking the bank.

Another idea for the future (and my resume) is testing. I’ve already started writing Jest tests to ensure the app’s core features like user authentication and timer functionality run flawlessly as we move forward.

The Bigger Picture

In a world full of distractions, people need something to help them focus, but they also need insights into how to improve. That’s what this app does. It gives you control over your time, but more importantly, it helps you refine how you use it. That’s the kind of thing that makes a difference.

Right now, I’m open to collaborators whether that be developers, designers, anyone with a mind for building something bigger than just another app. This project is far from finished, and I want to see it grow. If you’re interested, get in touch. Let’s make something worth talking about.