Student Journey Barriers Study
AI Strategy · Adoption · Case study
A ten-college study of the district student journey, from finding a college through to work, mapping where students hit barriers reaching support. I designed the study and I lead it as co-chair of the Student Support and Success domain, working with advisors, financial aid specialists, and student-facing staff across the district.
Goal
Set a baseline of how many students use each service now, then find and rank where students hit barriers reaching support by how many students each gap touches, and hand staff located, severity-rated evidence they can act on. A successful AI tool answers routine questions without a booking or an office visit, so help is counted through the tool even as office traffic falls.
Audience
The Student Support and Success domain of the district AI Resource Center, made up of advisors, financial-aid, and other support staff across all ten colleges, plus the college leaders and faculty who help run the study and recruit the student build teams.
Process
Build fifty demographic-grounded personas as specialist agents, add one orchestrator agent that sequences the journey and routes the runs, and walk the path from finding the college through to work. Each service is walked at each of the ten colleges until added personas surface no previously unseen barrier, the saturation stopping rule. Log what breaks and how badly with more than one rater, rank the highest-reach gaps, decide where AI fits first, recommend and pilot the most promising fixes to prove they help before anything scales, then have student teams build them as human-in-the-loop tools. Humans decide what to fix. No one is replaced.
The walk runs in three parts by access.
| Phase | What it covers | Where it stands |
|---|---|---|
| Phase 1 | Public tasks that need no login. | Run twice. A third round is planned. |
| Phase 2 | Signed-in tasks, on one sanctioned test account. | Not started. Waits on district approval and the data-governance review. |
| Phase 3 | Tasks that depend on the district’s incoming Salesforce platform. | Not started. Waits on that platform. |
Scope
The study follows the student-services journey, finding and reaching advising, financial aid, basic needs, career services, and the rest, from how a prospective student first finds the system through to a job. It does not go inside the learning management system or the coursework itself; the in-course experience belongs to the Teaching and Learning domain (Domain 2).
In this domain’s lane
The student-services journey across all ten colleges: finding a college, applying, enrolling, paying, getting advising and support, belonging, and graduating. Mapping where students hit barriers reaching those services, and where AI can lift routine work off staff.
Owned by another domain
Classroom, curriculum, and faculty tools go to Teaching and Learning (Domain 2). Data governance and security go to Strategy, Security and Governance (Domain 1). Retention and institutional-research data is coordinated with Operational Efficiency (Domain 3).
The domain documents every barrier it finds, but acts only on the ones in its lane. It favors the lightest thing that works: a plug-in or feature configured inside a system already in use, a small purpose-built assistant such as a Google Gem, or a compact agent a student build team can produce with staff. No college is ever asked to rename its own service.
Technology
| Piece | How it works |
|---|---|
| The agents | A multi-agent system: fifty synthetic students, what UX researchers call synthetic users, plus one orchestrator and one research aggregator. Each synthetic student walks a college’s live site the way that student would, narrates what it is looking for, and returns a finding. |
| How the AI is kept honest | A growing “golden set” of known barriers every agent version is re-run against, several runs of each scenario to measure consistency, and versioned persona and agent templates so every finding traces to the exact build that produced it. |
| Structured output | Every run returns its finding in a fixed JSON schema: the barrier, its location, the path taken, the local service name, a severity flag, and the persona and campus that produced it. |
| The log form | Human and AI runs are logged on the same Google Form, so the two are directly comparable. The form collects no email and no student data. |
| How the gaps get decided | Affinity mapping so themes emerge from the data, then severity by reach: each theme placed on a matrix of how severe it is against how many of the roughly 140,000 students it touches. |
Agents carry breadth; people carry judgment. Severity is the one call the agents never make, because the research is clear that it is where AI is least reliable. Findings are candidates until human raters confirm them. How the agents work, and the research behind the method.
Outcomes
The study produces a prioritized, severity-rated map of where students hit barriers, ranked by how many students each gap touches, alongside a service crosswalk across all ten colleges and a human-in-the-loop AI roadmap routed to the right owners.
Status
Phase one is complete: the public student journey has been tested across all ten colleges. The next phase, inside the systems students use after they log in, begins once the domain’s two executive sponsors, a college president and the district’s vice chancellor and chief information officer, have briefed student services leaders at each college. Findings are published after the domain confirms them.