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GradSteps

GradSteps

A degree planner that builds a student's whole path to graduation in seconds.

Live site Next.js React Tailwind CSS Framer Motion

Built by Tanmai Kalisipudi.


The GradSteps landing page: the headline, and Jamie's undergraduate plan with semesters, courses, GPA and an MCAT prep marker

What it is

A student enters a major, a minor and a pre-professional track. GradSteps builds a semester-by-semester plan that meets every requirement and shows when they will graduate. Advisors see every student's plan in one dashboard and can tell who is off track.

The first version targets the University of Pittsburgh. This repo holds the marketing site at gradsteps.com and janitor, the internal tool that builds training data for the requirement parser.

The site

The landing page walks through the product the way a student and an advisor would use it. Every mock on the page is a live React component animated with Framer Motion, so the plan, the course cards and the advisor table move as you scroll.

For students

Pick a major, a minor and a track like pre-med. The card fills in the total credits and an estimated graduation term. Course cards carry an AI summary of the class, its syllabus, a contact for the department and how hard past students found it.

The plan generator for Justin, with major, minor and pre-professional tracks, and course cards below
A tuition-saved counter and the advisor section with a student status table

For advisors

A running counter shows the tuition a student saves by graduating on time. The advisor dashboard lists every student with a status of on track, check in or needs attention.

For the university

Cohort charts track 4-year graduation and first-year retention, with a projection for the current class.

Charts of 4-year graduation rate and fall-to-spring retention for the last and current cohort

janitor

Course catalogs write enrollment rules as free text. janitor is a small Next.js app that builds training data to parse them. It shows one rule at a time. You type the structured version, or accept the output of the model trained so far. Every 50 or so labels, you fine-tune again, so the model does more of the work each round.

Run it

npm install
npm run dev

Open http://localhost:3000. The page uses a fixed desktop layout, so view it in a wide window. The site needs no environment variables. It deploys to Vercel on every push to main.

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