HootPlanner

HootPlanner: AI-based personalized course scheduling, research opportunities, and extracurricular recommendations.
Course Scheduler: Find The Best Courses For You
Research Opportunities Finder: Discover Research Opportunities That You're Truly Passionate About
Club Discovery: Find Clubs That Suit You

Inspiration

Course discovery and scheduling is often overwhelming and confusing. Students frequently miss exciting research opportunities they never knew existed. They are also troubled with too many choices in clubs and activities. Seeing this, we started working on HootPlanner.

What it does

HootPlanner is the one-stop shop for students preparing for their careers in college. It:

Crafts perfect class schedules in seconds
Suggests exciting research projects tailored to students' passions
Recommends clubs and activities students will love
Reduces student stress and uncertainty in decision-making
Improves utilization of university resources (courses, research, and clubs)

How we built it

Our backend is driven by FastAPI that handles API endpoints for student onboarding, course recommendations, club suggestions, and research opportunities. We integrated LanceDB for vector search, using pre-trained language models to generate embeddings for semantic matching. This allowed us to provide highly relevant recommendations based on students' interests and academic profiles. On the frontend, we used Next.js and Tailwind CSS to create a responsive interface. Our full-stack approach enables students to input their academic information and interests, then receive personalized recommendations for courses, clubs, and research opportunities that align with their goals. By incorporating machine learning for matching and recommendation, we've created a system that offers tailored suggestions for each user.

Challenges we ran into

We encountered several challenges while training our Lance Vector DB. While developing our RAG pipeline, we dealt with issues related to batch processing, calculating cosine similarity, and figuring out which parameters and categories influence recommendations.

Accomplishments that we're proud of

In building this app, we're particularly proud of our recommendation engine that uses advanced language models to provide personalized suggestions for courses, clubs, and research opportunities. We successfully integrated academic planning with extracurricular activities. Our user-centric design guides students through exploring options tailored to their profiles, while offering data-driven insights through percentage matches. By efficiently managing data from various sources, we've created a comprehensive platform that goes beyond simple keyword matching to offer truly valuable recommendations for students' academic journeys.

What we learned

This project was difficult, especially due to the time pressure we were under. Despite these factors, we expanded our understanding of vector databases, embeddings, RAG, and working with LLMs. On the less technical side, we learned to communicate effectively and had to set our differences aside to work as a team to complete our project.

What's next for HootPlanner

Although Rice students struggle to decide on courses, clubs, and research opportunities, Rice students have much more individualized attention compared to many other universities. Therefore, we would like to expand this idea into other larger public, especially those that have less specialized attention for each student.