Inspiration
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Autoclass_select system
🔍 Inspiration
As a community college student navigating the complex UC transfer process, I often found it overwhelming to plan classes, check prerequisites, and ensure I was on track to transfer on time. Many peers shared the same frustration—spending hours on assist.org, guessing which courses to take next, and fearing missed requirements. That’s what inspired me to create AutoClass Agent: an AI-powered scheduling assistant to simplify academic planning and empower students with clarity and confidence.
đź§ What I Learned
Through this project, I learned how to: • Integrate large language models into real applications • Build scalable APIs using FastAPI • Design intuitive UIs with Next.js and Tailwind CSS • Engineer prompts that enforce strict structure and factuality • Work with messy, real-world education data
🛠️ How It Was Built • Backend: Python with FastAPI to handle scheduling logic and Sonar API calls • Frontend: Built with Next.js and styled using Tailwind CSS • AI Engine: Perplexity Sonar Pro for generating personalized, quarter-by-quarter transfer plans • State Management: Zustand and React Hook Form for managing form state and user preferences • Output: JSON and clean visual tables for student-friendly viewing
⚠️ Challenges Faced • Parsing accurate data from assist.org without a public API • Preventing AI hallucinations while ensuring complete course sequencing • Balancing usability for students and flexibility for developers • Enforcing strict JSON structure in AI responses
Built With
- javascript-?-frontend:-next.js-14
- languages:-python-3.12
- markdown
- postman
- react-hook-form-?-ai-engine:-perplexity-sonar-pro-api-?-hosting/dev-environment:-localhost-?-version-control:-git-&-github-?-output-formats:-json
- tailwind-css-?-backend:-fastapi-?-state-management:-zustand
- typescript
- visual-course-tables-?-tools:-vs-code
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