datons for Education

An institution’s data lives split across the virtual campus, the registrar, grades, enrolment and each department’s spreadsheets. Questions that should be immediate — who is dropping out and since when, which course blocks a cohort, whether the last curriculum reform improved anything — get answered by hand, late, and only when somebody formally asks. And then there is everything else: students already use AI daily, half of any technical syllabus ages within a year, and the faculty has to decide what to teach about tools that did not exist last term.

Most of that work no longer needs a person. Preparing the tracking report, crossing attendance with results, reviewing which material is out of date: a machine does it today. What it does need is ground to stand on, and judgement: an AI pointed at student data with no governance or permissions is a legal problem before it is an analytical one, and an AI syllabus written by someone who does not use it in production shows up at the first hard question. We give both, because we teach what we build.