Education & EdTech

Fairness comes from the mark being explainable. Neuros systems tie every mark to a rubric and highlight, on the text itself, how many points came from which criterion. When a teacher changes a mark, that correction is recorded and feeds the rubric's calibration. The feedback a student receives carries the criterion's wording, not the model's free commentary.

Student data is treated as children's data under KVKK and the architecture follows: access is role-based, a teacher sees only their own class and a parent only their own child. Retention is defined up front and records are deleted automatically when it expires. Neuros does not use student content to train models — the content stays inside that institution's own system.

Yes. Neuros integrates through the interfaces the existing system exposes; student, class and assignment data are read one way and results written back. Sitting inside the existing flow is preferred to opening a new portal, because any solution that makes a teacher log into two systems falls out of use by the end of the first term.