Turn course material into governed AI tutors.
IAYS Academic Tutor helps universities create course-specific AI teaching assistants governed by professors, aligned with approved material, and observable through real student questions.
Pain snapshot
- Students need help outside lecture hours, but professors cannot personally answer every repeated question.
- Generic AI tutors may explain confidently while drifting away from the actual syllabus, readings, or assessment expectations.
- Course forums reveal confusion too late, after the same concepts have already blocked many students.
- Teaching assistants vary in tone, depth, and interpretation of the professor's method.
- Universities need useful AI support without losing academic control, traceability, or pedagogical standards.
Why typical AI approaches fail here
Promise: Answer student questions instantly.
Where it breaks
- Answers are not reliably constrained to approved course material.
- The model may simplify complex theories in ways that distort the professor's framing.
- Professors get little visibility into recurring learning gaps.
Example: A student asks about Piaget and Vygotsky and receives a plausible comparison that misses the course's required emphasis.
Promise: Help students find slides, readings, and notes faster.
Where it breaks
- Finding the right passage does not guarantee understanding.
- Students still need help connecting theory, examples, and exam expectations.
- Search does not show professors where comprehension is breaking down.
Example: A student finds the slide on scaffolding but still cannot apply it to a classroom case study.
Promise: Centralize course resources and announcements.
Where it breaks
- Content remains passive unless students already know what to look for.
- Repeated questions accumulate without becoming structured teaching feedback.
- Updates to teaching guidance do not automatically reshape student support.
Example: The same exam-prep question appears across channels, but the course design does not adapt until much later.
Faster answers ≠ aligned decisions.
What changes with governed evaluation (IAYS)
Evaluation boundaries are defined before the model answers, so teams apply the same standards every time.
Only defined unknowns escalate, reducing noise while preserving oversight on genuine risk cases.
Decisions are linked to explicit rule sets, making reviews faster and policy updates easier to manage.
IAYS transforms probabilistic output into structured evaluation.
Self-serve trial approach
One workflow, one agent, four implementation phases.
Target outcomes (illustrative)
Results vary based on workflow maturity.
Baseline: Manual Trial: Always-on
Baseline: Low Trial: High
Baseline: Variable Trial: Governed
- Phase 1
Select workflow + capture edge cases
Define one workflow to improve and map the edge cases that currently create delays.
- Phase 2
Structure decision criteria
Turn policy and approval logic into clear governed criteria the agent can evaluate.
- Phase 3
Shadow-mode testing
Ship an agent in shadow mode and compare outcomes against current team decisions.
- Phase 4
Go-live with monitoring
Go-live with override controls, escalation visibility, and ongoing monitoring.