AI course copilot

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Inspiration

Course registration is confusing and fragmented—students rely on scattered sources like portals, reviews, and word of mouth. We wanted to simplify this and make it more personalized and accessible.

What it does

AI Course Copilot helps students choose the right courses based on their profile, past coursework, GPA, and career goals. Users can ask questions in natural language and get tailored recommendations instantly.

How we built it

We built a Streamlit frontend for user interaction and profile input, a FastAPI backend, and integrated QWEN AI model to generate recommendations using structured course data and user profiles.

Challenges we ran into

Structuring course data for meaningful AI responses

Personalizing recommendations effectively

Debugging frontend-backend integration under time constraints

Accomplishments that we're proud of

Built a fully working end-to-end AI tool

Achieved personalized recommendations (not generic outputs)

Created a clean, usable interface in a short time

What we learned

Prompt design is critical for good AI output

Simple data structures can power strong results

Fast prototyping + clear team roles = success in hackathons

🚀 What's next for AI Course Copilot Integrate real course and review data

Add schedule optimization and conflict detection

Build a smarter recommendation engine with user feedback

Expand to other universities

Built With

• python
• qwen
• streamlit