AIRE Analyst™

You are driven to put a number on the school or the college that will still be true after the board packet or the accreditation exhibit leaves. On an intervention, assessment, retention, or learning-outcome claim that means every figure traces to a cohort, a comparison group, and an assumption you can defend. You are the colleague who asks what the number actually measures before you let it onto the intervention dashboard, the gradebook category, the board slide, the IR retention report, or the accreditation evidence binder. You measure success in claims caught in the assumption log, not in being the most confident person in the MTSS meeting or the assessment committee.

You work by treating the model as a second analyst that has to survive last year's cohort. You upload historical intervention rosters or de-identified course-section aggregates, current gradebook category weights or DFW patterns, and attendance or engagement flags, then ask which lines move when the comparison group changes or the window is a semester versus a year. You immediately rerun the same query against a hold-out grade, a prior year, or a second section so a hallucinated effect size dies before it hits the principal's slide or the dean's accreditation exhibit. Decision-making is a range with the assumption log open, then a number you will sign. Communication is one page a PLC or assessment committee can use, not a workbook. You iterate by changing one variable (cohort definition, attendance window, proficiency cut, withdrawal coding) and watching whether the range still closes. You do not paste identifiable student work into an unapproved tool; named individual records stay inside the published Named-versus-Anonymous commitment, and public claims stay on anonymous aggregates.

Your name is on the number, so you refuse to carry what you cannot trace. You break a claim that the reading intervention is working — or that the gateway course redesign raised pass rates — into what the dashboard logs versus what comprehension or learning actually is. You flag when a vendor growth score or a course-eval composite violates last year's comparison group. You keep the assumption log a principal, dean, registrar, or provost can follow. You translate a model output into a one-pager that belongs in the MTSS meeting or the assessment committee, not in a slide. You are interested in anything that speeds cohort lookup, category-weight checks, or gap detection, and you kill anything that prints an effect with no definition behind it.

The same rigor can freeze an MTSS meeting or an assessment committee while the intervention or redesign window is still open. You may demand another year of comparison data after the board packet or the accreditation exhibit is due. You can discount a veteran teacher's or instructor's warning that a student is not reading or not mastering the outcome because the dashboard is green, and lose the referral to someone willing to name what the spreadsheet cannot see.

When the board packet is due, an accreditation exhibit is due, or an MTSS or assessment stack is still open on the meeting date, you open more models rather than fewer. The trigger is any room that wants a single number before the metric's definition is closed. In those moments you may withhold the finding until the range is prettier, and the window to actually change the intervention or the course redesign closes.

Principals, deans, IR leads, and coaches come to you because the intervention or outcome claim feels real instead of vendor-smooth. They trust your ranges. They sometimes wish you would say the dashboard is fine without another tab. You fill the role of the person whose number survives a parent question, a board question, a registrar question, or a provost question.

You adopt AI the moment it traces a dashboard line to a cohort and a comparison you can check against a prior year. You resist tools that emit a growth score or a retention lift with no assumption log. Once a model matches your historical pattern on one metric, you lock that prompt into the research template and move to the next metric. You stay inside the institution-approved tool list and the Named-versus-Anonymous commitment for any record that could identify a student.

The assessment researchers who asked whether a growth score measured learning or minutes logged, the district research leads who rebuilt an intervention claim from the actual comparison group instead of from the vendor slide, and the institutional-research and faculty assessment leads who rebuilt a retention or DFW claim from the actual cohort definition instead of from the dashboard tile.

"Option A I can trace to a cohort and a comparison. Option B still has an open definition. I'm not signing B."