Import the term you already have
Bring sections in from a CSV or SIS export. No migration project, no queue behind IT — a department can be working in an afternoon.
CSV / SIS export
AI course scheduling for universities
The term, draftedbefore the first meeting.
AristAI places rooms, times, and instructors against your real constraints, flags the calls a human should make, and benchmarks the result against peer programs — while the term is still yours to shape.
FERPA-aware / data stays in your tenant / sign-off stays human
Fall term · CS department
AI co-pilot
Draft
FERPA-awareTenant-isolated dataOpt-in benchmarkingReversible importsHuman sign-offSIS-friendly CSV importAnonymized peer cohorts
How a term comes together
Spreadsheet in. Signed-off term out. Three moves.
Let the co-pilot draft it
Rooms, times, and instructors are placed against your constraints. Every placement shows its reasoning, and every conflict arrives with ranked ways out.
Reasoning shown inline
Decide with context, then sign off
Compare utilization and demand against an anonymized cohort of peer programs, resolve what matters, and lock the term knowing where it stands.
You approve every change
Inside the product
Four surfaces. One operating picture.
Scheduling work is split into focused surfaces instead of one dense screen — so chairs, schedulers, and deans each see the decisions that belong to them.
Workspace opens on your next action
AI-ranked scheduling work greets you first — conflicts, capacity pressure, and term health surface before any navigation.
Scheduling in stages
Sizing, grid work, conflicts, and review are separate, focused stages — not one catch-all screen.
Conflicts arrive with a way out
Every clash comes with ranked, one-click resolutions — and the reasoning behind each one.
Benchmarks built into the flow
Utilization and demand read against anonymized peer programs — not just last year’s spreadsheet.
See how benchmarking worksSee it on your own term. Import a CSV and the first draft is minutes away.
Built for how your programs actually teach.
One scheduler. Your division’s rules built in.
Most tools ship a gen-ed grid and bill consulting hours to make it fit. AristAI ships division presets — studio arts, clinical, law cohort — that work from a CSV export of your SIS, the same afternoon.
Clinical programs don’t run on a course grid.
Small-group rooms that each need a facilitator. Four-hour labs. Protected study time the schedule must defend. The clinical preset models blocks, small groups, and protected hours out of the box — and OptiMatch can rerun last year’s clerkship lottery side-by-side against an optimized match with a worst-case guarantee on a de-identified CSV, so you see the lift — and the appeal and swap-request pressure it removes — on your own cohort before you commit.
Clinical preset
Your studios aren’t underutilized. Your scheduler just can’t see them.
Three-hour studio blocks, sixteen-seat wheel caps, ensembles that need everyone in the room at once, one auditorium shared by theatre, music, and commencement. The arts preset ships legal block shapes and protected ensemble hours instead of exception flags — upload last fall’s schedule and see every real conflict the same day.
Arts preset
The 1L grid comes first. Everything else schedules around it.
Lockstep sections that move as one, an evening division living Monday–Thursday six-to-nine, adjuncts who can only teach around a practice, and clinic students who need daytime whitespace. The law preset ships the lockstep cohort grid, evening block patterns, and clinic-hour protection as defaults, not a services engagement.
Law preset
Presets, not professional services. CSV in, CSV out — SIS sync when you’re ready. See it on your own data first.
Explore the live demoCross-institution benchmarking
Know what good looks like before the term is locked.
Most scheduling teams plan against last year’s spreadsheet. AristAI reads your utilization, fill rates, and demand against an anonymized cohort of comparable programs — so “are we over-scheduled?” gets an answer, not a shrug.
Aggregate · anonymized · opt-in
Your raw data never leaves your tenant. Comparison is to a distribution, never a named institution, and you can leave the cohort at any time.
n = 40 programs · same Carnegie class
0%utilization100%
The decision edge
A shared reference point
Provosts, chairs, and schedulers argue from the same distribution — instead of from anecdote — before approvals start.
1
working session from CSV import to a drafted term
40
peer programs in a benchmark cohort
4
focused stages from import to sign-off
0
raw records leaving your tenant
For universities
Built to pass the meeting with your registrar.
FERPA-aware by design
Student-adjacent data is handled to the standard your registrar already holds you to.
Tenant isolation
Your institution’s data lives in its own tenant with row-level security. No pooled records, ever.
Opt-in and reversible
Benchmarking is off until you turn it on — and every import and AI change can be rolled back.
Humans sign off
The AI drafts and recommends. Publishing a term always requires a named person’s approval.
Questions, answered
The questions every scheduling office asks first.
Straight answers to the governance and workflow questions that decide whether a pilot happens.
How does our data get in?
A CSV or SIS export of your sections is enough to start. Imports are validated row by row, idempotent, and reversible — running the same file twice never duplicates a section.
Does the AI publish changes on its own?
No. The AI drafts schedules and proposes conflict resolutions with its reasoning shown. Applying a draft or a fix is always an explicit human action, and every applied change can be rolled back.
What exactly is shared for benchmarking?
Only aggregate, anonymized distributions — think “utilization percentiles across 40 programs,” never rows. Benchmarking is opt-in, comparisons are to a distribution rather than a named school, and you can leave the cohort at any time.
Is this FERPA-compliant?
AristAI is built FERPA-aware: student-adjacent data stays in your tenant, protected by row-level security, and is never used to train shared models or exposed to other institutions.
How long does a pilot take?
One department, one term. Most teams import a real term and see their first AI draft plus benchmark context inside the first working session, then run the pilot alongside their existing process.
Your next term deserves a first draft.
Import a term, watch the co-pilot draft it, and see your benchmark context — in your first working session, without changing how your team operates.