For academic leadership

The professor, in command of the AI their students learn with

Your faculty design the AI their students learn with, and you get evidence you can defend.

Data and compliance posture

Written for security and procurement

Designed for UAE data protection requirements. Your own ethics, procurement, and data policies sit above them, and we work to them.

Residency

Hosting, data location and retention are agreed with your institution.

Consent

Capture is consent bound. Students are told what a lesson records before they begin it.

Subject rights

Access, correction, and erasure requests are supported, and routed through your institution.

Automated decisions

No consequential decision is made about a student without faculty attestation. That is architecture, not a setting.

What your faculty receive

Evidence you can defend

An Evidence Summary for each student, on each lesson: how they worked, what they produced, the AI assistance behind it, how they explained the idea in their own voice, and any open flags. Not a grade, not a prediction.

Every figure traces back to the recorded work, so your faculty and reviewers can inspect it and check it.

A student view for one student in three panels: a Lesson AI conversation where the AI answers a question with a question and points to the assigned reading; outcome status across the last four lessons with gaps marked; and AI assistance for the faculty member, whose question about where the student relied most on AI gets an answer citing lesson records, marked as a draft for faculty review with Attest and Return controls.

For the university

What Mizain gives a university

AI inside every lesson, on the university's terms, with evidence faculty stand behind.

Integrity without policing

Students work with AI inside rules faculty set, so integrity comes from how lessons are built, not from detection after the fact.

Graduates ready for AI

Lesson by lesson, students practise when to use AI, how to check it, and how to show their own reasoning.

Faculty in command

Faculty decide how AI takes part in each activity, try each lesson before students see it, and attest every record.

Evidence for programme review

Attested Evidence Summaries show, by course section or across a cohort, where students struggled and where they leaned heavily on AI.

Your data, your control

Runs on open-weight models hosted by Mizain or your institution. Every exchange records which model answered, and no model is trained on student work.

Beside your LMS

An activity layer next to your learning management system, with university sign-in. The LMS stays the course home.

Put your faculty in command of AI

Bring one course. We will show how its lessons run with AI assistance your faculty govern, and the evidence each one leaves.