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Inspect the boundary before switching AI on

Tutoring between lessons, with staff in control.

Useful AI, with visible ownership

See what the tutor does and who approves it.

Use permitted course context

Teaching starts from approved curriculum context and materials, with unverified coverage stated plainly.

Inspect the evidence

Staff can see the learner attempt and teaching response behind a suggested follow-up.

Keep people accountable

Assignments, family communication and consequential changes remain reviewable human decisions.

A controlled route from one cohort to wider use

Start small. Review the evidence. Expand carefully.

Inside grademy

Define the boundary before the first learner enters

Agree permitted courses, materials, users and escalation routes before running a controlled cohort.

Inside grademy

Give every learner one managed tutor workspace

Within that boundary, learners move between explanation, voice, visual work and checked practice without stitching together separate AI tools.

Inside grademy

Inspect why the system acted

A learning signal should be traceable from approved source to learner attempt, tutor response and proposed follow-up.

Inside grademy

Expand only after a controlled review

Evaluate learning evidence, workload, incidents and policy fit from one cohort before changing the boundary.

See who owns what

Inspect the decision boundary

Teaching

AI may

Explain and check.

A person owns

Set the boundary.

Started at a Google hackathon. Won $10,000.

The prototype won in London. We have been building the real learning loop ever since.

Amir Gulubayli and Ashfi Dewan at the Google hackathon in London.
The Grademy team presenting the winning concept at the Google hackathon.

Plan a governed school workspace