Michelle Blomberg

Student Journey Barriers Study

AI Strategy · Adoption · Case study

Fifty student personas, each built from real data on who the district enrolls and what stands in their way. People confirm every finding before it counts.

The study builds a fixed set of 50 personas, and each one is a whole student, never a single trait on its own. Race and ethnicity, first-generation status, food security, disability, language, academic preparedness, and enrollment path all live together in one person, in the proportions the district actually enrolls, so the barriers the set surfaces are the barriers real students hit, weighted by how common each kind of student is.

How each persona is built from the data

  1. Start from real numbers. Gather what is known about students in the district and the barriers they face, from district figures and national research, with every figure cited in the sources at the bottom of this page.
  2. Set the mix. Turn those rates into targets for the set of 50: about 44% food-insecure, 44% carrying a disability, 38% who would place into a developmental course, 16% LGBTQ+, in the district’s real proportions.
  3. Build whole students. Distribute the attributes across 50 people so each persona is one coherent student, for example a first-generation, food-insecure nursing hopeful with undiagnosed ADHD, not a trait in isolation.
  4. Write the limits, not just the labels. Fill each persona into one fixed template: what she does not know, her device, her reading speed, her language, and the exact moment she would give up, so the agent behaves like her, not like a capable AI.
  5. Set it loose on the site. Each persona becomes an AI agent that browses a college’s live public pages from a felt need, thinks aloud, and records where it breaks. How a run works and how people validate it is spelled out below.

Each persona agent carries one whole persona at a time: one persona, one college, one task that starts from a felt need, never from an office, because a student is never told where to go.

Modality is written into each persona (all online, some online, or all in person), because a fully online student cannot reach a service offered only on campus, and that unreachability is itself a finding, not an error.

The students behind the personas

The charts below hold the district totals first, then the 50-persona sample the study actually built.

Race and ethnicity, district totals only


Study modality, the 50-persona sample



What each persona also carries, counted across the 50

District totals from the district published data (Fast Facts and the college Student Right-To-Know pages); the study modality split follows a national public two-year baseline pending the district Institutional Research. District totals only, never per-college.

Neurotype, disability, and disclosure

Disability is written into the set the way it shows up in real life, unevenly and mostly out of sight. Twenty-two of the fifty carry a condition or are still working out whether they have one, but only six are known to their college. The undisclosed majority is the point: a student who never finds Disability Resource Services never gets the accommodation, and that gap is itself a barrier the study looks for.

Disability and disclosure across all 50 personas


Of the 22 who carry something, by type

These are attributes written into the personas, not measured outcomes. Shares are anchored to national data (about 20% of undergraduates report a disability, with ADHD, autism, learning, and mental-health conditions among them) and tuned to a community-college, Hispanic-Serving, first-generation, low-income population where under-identification runs high.

The sample by age and language

Age, the 50-persona sample


Home language, the 50-persona sample

Academic preparedness and placement

Arizona’s K-12 system is among the weakest in the country, so many students arrive underprepared, and the personas carry that.

The study writes preparedness in, and it writes in the specific barrier it creates: a student who self-advises or skips placement can land in a college-level course they are not ready for, or enroll in a below-100 course without realizing it will not count toward the degree.

Academic preparedness across the 50 personas

Of the 19, six self-advise or skip placement and risk landing in the wrong course, the placement-and-advising barrier the study tests directly. Share anchored to the national developmental-education rate (about 40%) and tuned to Arizona’s below-average K-12 outcomes; assigned by each student’s story (low literacy, long out of school, first-generation with no one to explain placement), kept distinct from disability and from language alone.

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, with classroom, curriculum, and faculty tools, belongs to the Teaching and Learning domain (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. No college is ever asked to rename its own service.

How a finding is confirmed

From a candidate to a confirmed finding

  1. Run to saturation. Each task repeats at every college with enough personas that new ones stop surfacing new barriers. That is the stopping rule, in place of a fixed quota.
  2. Validate with people. A human sample re-runs a share of the tasks and confirms severity (Cohen’s kappa on the agreement). A finding counts only when a person validates it, because severity is the judgment an AI tester gets wrong most often.

The personas carry the reach; people are the check. A small human sample of roughly ten testers, about one per campus, re-runs some of the same tasks as a spot-check, compared on whether a barrier is present and a kappa on severity. Synthetic runs scale only if that agreement holds, and if it is weak the study leans on the human sample.

How the agents carry out each run is on the Agents and ethics tab.

The evidence behind the personas

The persona attributes are grounded estimates, not measured outcomes. Where the district does not track something, disability a student never discloses, whether a student was homeschooled, the share is modeled from national data and labeled as such.