Student Journey Gap Analysis

Case study · AI Tools & Strategy · UX Design

What happens after the findings: build the fixes with students, pilot them, and judge them by whether the student’s need was met.

Build with students

Each chosen fix becomes a real project for a multidisciplinary student team, assembled after the analysis runs. Teams draw from computer science, information systems, digital media, business and project management, and communication, so each build carries the skills it needs. The plan is three teams of four, one semester, January to May 2027, led by AI Resource Center members, working against a real user, a real constraint, and a real deliverable. This is paid work, not class labor, so the domain pursues grants (AmeriCorps and AI, technology, and workforce-development funding), and no team stands up until the students are paid. Every tool stays bound by the study’s rules: minimal use of student data, a human in the loop, and a fix that raises the number of students who reach a person who can help.

Pilot before rollout

A built tool does not go straight to every student. Each fix runs in staged pilots against the success measures, is revised, and only then rolled out. Every pilot has the same shape: a defined intake, a review, a secure sandbox separate from live student systems, a named success measure, and a path to production if it clears. Pilots are co-designed with the front-line staff whose work the fix touches, collect no student data, and keep a person in the loop. The domain has drafted the piloting framework the rounds depend on.

• Summer 2026 · Build and start. The 50 persona agents and the orchestrator are built, and Part 1 (no login) has begun.
• Summer to fall 2026 · Approvals and deeper access. District and Domain 1 sign-off, the sanctioned test account, then Parts 2 and 3 as they clear.
• Fall 2026 · Rank the gaps and decide where AI fits first, buy versus build.
• January to May 2027 · Student teams build the chosen fixes.
• Spring 2027 to January 2028 · Pilot in three rounds, then move cleared tools into monitored production while newer fixes keep piloting.

How success is judged

Success is the student’s need met, not an office kept busy. Every fix is measured four ways: whether the need was met, how easy it felt (the Single Ease Question), time to resolution, and persistence over time. These read against a baseline taken before any tool ships, so the study can show the lift: more students reaching support, and more of them persisting. Help is counted across every channel, so a question a tool answers immediately counts as help delivered even when it never becomes an office visit. A busy office is not a working journey, and a quiet one is not a failure.