CampusMate

Courses/Classes/Focussession/Plans Page
UserProfilePage
AIsuggestionPage
AIPromptingPage
PreferencePage
SchedulePage
TasksPage
Dashboard

Inspiration

Student life involves juggling lectures, assignments, deadlines, exams, and personal commitments. Keeping everything organized can become overwhelming, especially when students have multiple tasks due at the same time.

We built CampusMate AI to make student life more organized by bringing academic task management, study planning, and AI assistance together in one place.

What It Does

CampusMate AI is a student productivity platform designed to help students manage their academic workload more efficiently.

Key features include:

Smart Task Management: Create and organize academic tasks with priorities, durations, and deadlines.
Study Planning: Organize study sessions around existing classes and commitments.
AI Assistance: Integrate Google's Gemini API to support intelligent academic planning and personalized recommendations.
Authentication and Data Storage: Use Supabase to support user authentication and persistent application data.
Integration Support: Provide a Model Context Protocol (MCP) endpoint to support connections with external tools.
Fallback Handling: Include rule-based behavior to keep parts of the application usable when AI inference is unavailable.

How We Built It

We built CampusMate AI using Next.js, React, and TypeScript. Supabase supports authentication and data storage, while Google's Gemini API provides the foundation for AI-assisted interactions. We use GitHub for version control and Vercel for deployment.

The project brings together frontend development, backend API routes, database integration, AI services, and scheduling logic in a single application.

Challenges We Faced

One of our biggest challenges was integrating AI services reliably in a deployed environment. Although the application can detect Gemini configuration, production inference and fallback behavior required additional troubleshooting.

We also worked on improving task creation, preventing duplicate tasks, and organizing study sessions around existing classes and deadlines. These experiences highlighted the importance of testing real-world scheduling constraints rather than relying only on ideal scenarios.

What We Learned

Building CampusMate AI gave us practical experience with full-stack web development, third-party API integration, database management, deployment, and AI-assisted application design.

We learned that an AI-powered application requires more than connecting an API. Reliable error handling, clear fallback behavior, secure environment configuration, and careful testing are equally important.

What's Next

Our next priorities are improving Gemini reliability in production, strengthening schedule conflict detection, refining batch task creation, and expanding personalized study recommendations.

Our long-term vision is to make CampusMate AI a dependable student life assistant that helps students spend less time organizing their workload and more time learning.