Illini Planner

As UIUC students, we’ve seen how confusing course planning is, with requirements scattered across pages. We scraped and organized the data ourselves to build a clear, interactive planning tool.

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Inspiration

As students at the University of Illinois Urbana-Champaign, we’ve experienced how confusing it can be to plan a CS or CS+X degree. Requirements are spread across different department pages, prerequisite chains are unclear, and there’s no single place where everything is structured and easy to explore. We wanted to build a tool that turns static degree requirements into something interactive, structured, and actually helpful for students making real academic decisions.

What It Does

Our project is an interactive course planning and elective recommendation tool specifically for CS and CS+X majors at UIUC. We structured prerequisite data with correct AND/OR logic and built a clean frontend interface that allows students to:

  • Explore CS and CS+X degree requirements
  • Understand prerequisite chains clearly
  • Check course eligibility based on structured logic
  • Get elective recommendations based on their interests Instead of manually scanning course catalogs across multiple websites, students can see a clearer academic pathway in one place.

How We Built It

We built the project using:

  • HTML, CSS, and JavaScript
  • Structured CSV datasets for majors and prerequisites
  • A logic engine that correctly separates AND vs OR prerequisite groupings
  • A rule-based elective recommender that matches user interests to tagged courses Because there was no single dataset containing all CS and CS+X requirements in a structured format, we had to manually scrape and compile the information from official UIUC department pages. We then cleaned, standardized, and normalized the data so it could be parsed logically instead of just displayed as text.

Challenges

One of the main challenges was correctly structuring prerequisite logic. Many courses mix AND and OR requirements, and modeling that incorrectly breaks the entire eligibility system. Another challenge was collecting and standardizing data, since requirements were distributed across multiple web pages with inconsistent formatting and no centralized dataset.

Accomplishments

We’re proud that we transformed complex CS and CS+X degree requirements into an interactive planning tool. Building our own structured dataset from scratch and modeling prerequisite logic accurately allowed us to create a system that feels practical and genuinely useful for students.

What We Learned

We learned how important clean data modeling is when building intelligent systems. Small logical errors in prerequisite structure can completely change course eligibility. We also learned how much work goes into transforming unstructured web information into structured, machine-readable data.

What’s Next

Next, we plan to expand the platform beyond CS and CS+X to include additional majors across UIUC. We also want to integrate an AI-powered advising feature that can take a student’s goals and interests and generate personalized course path suggestions conversationally. Our long-term vision is to build a smarter academic planning assistant for students navigating complex degree requirements.

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