StudyMate

log in
profile setup
swip screen
messages
calendar
chatbot

Inspiration

Studying today often feels fragmented. When students get stuck, they jump between lecture slides, search engines, AI tools, forums, and notes, breaking focus every time.

We wanted to reduce that friction.

Our inspiration came from a simple idea: What if students could find their perfect study match, and have an AI chatbot built directly into their academic workflow, personalized to their needs and available instantly?

Instead of interrupting a study session to look for help, help should already be there.

StudyBuddy was created to turn confusion into clarity without disrupting momentum.

What it does

StudyBuddy is an Android application that helps students find others who share similar study goals and academic interests.

Users can connect with peers who are preparing for the same subjects or working toward similar objectives, making it easier to study together, stay accountable, and remain motivated.

Instead of studying alone, students can find their ideal study partner based on shared goals and collaborate in a focused environment.

In addition to peer matching, StudyBuddy includes a real time chatbot feature that helps students clear academic doubts instantly. Students can ask subject related questions and receive structured explanations directly inside the app.

Together, peer matching and instant doubt solving create both collaborative and individual academic support within one platform.

How we built it

We built StudyBuddy as a structured Android application using Kotlin in Android Studio. The system is designed around modular components that handle authentication, user management, matching, messaging, scheduling, and chatbot communication.

For the study matching feature, we created user profiles that store academic interests and study goals. The app compares user preferences and interactions to identify compatible study partners. A swiping and connection logic allows users to express interest and form matches when there is mutual alignment.

Once matched, users can communicate within the app through a structured messaging system that supports real time interaction.

We also implemented a calendar and task planning feature that allows students to organize study sessions, define time ranges, and track academic tasks. This helps matched users coordinate and stay accountable.

By separating user data, matching logic, messaging, scheduling, and chatbot functionality into distinct layers, we created an app that supports collaboration and instant doubt solving within a single platform.

Challenges we ran into

One of our biggest challenges was deciding which features to prioritize. We had many ideas for expanding the platform, but with limited time during the hackathon, we had to carefully choose which features were essential and achievable. Narrowing our focus while maintaining impact required thoughtful planning and compromise.

Although the Gemini API for the chatbot was functioning, integrating it smoothly into the overall system required debugging and refinement. We also faced performance issues with the Android emulator, which slowed down development and testing.

Working with Firebase was another challenge, as some team members were new to it. Setting up authentication, managing data, and ensuring proper communication between components required learning and experimentation under time pressure.

Finally, since different team members worked on matching, messaging, scheduling, and chatbot functionality separately, integrating all components into one cohesive application was challenging. Ensuring that everything worked together smoothly required coordination, testing, and multiple rounds of fixes.

Accomplishments that we're proud of

Despite working on different components separately, we were able to integrate all features into a single working Android app. Bringing together user authentication, study matching logic, peer communication, calendar planning, and real time doubt solving required coordination.

What we learned

We learned how important clear system architecture is when combining multiple features in one application. Matching logic, messaging, scheduling, and chatbot communication each require different design considerations. We also learned how user experience decisions impact technical structure. Even small interface changes can affect data flow and backend communication.

Beyond technical growth, we learned how to prioritize effectively under time pressure and focus on delivering a stable and functional product.