Consistent evidence across varied AI use.
Create reviewable learning evidence across assignment-level AI expectations, supporting fairness, quality and programme review.
Integrevise for University of Kentucky
Integrevise adds a short adaptive oral discussion after submission, grounded in the assignment brief, rubric and the student’s own work. It gives UK faculty reviewable evidence of individual understanding and returns personalised feedback—without relying on AI detection or scheduling a live viva for every learner.
Value at every level
Integrevise adds a practical evidence layer while preserving assignment-level expectations, faculty judgement and the assessments already in place.
Create reviewable learning evidence across assignment-level AI expectations, supporting fairness, quality and programme review.
Retain the selected AI-use level, brief, rubric and final decision while reviewing structured oral evidence without live interviews.
Give students a transparent way to explain choices and receive feedback grounded in their own work.
Evidence before adoption
Integrevise's adaptive oral approach has been evaluated in a university setting and published in the peer-reviewed journal Trends in Higher Education.
Read the peer-reviewed studyDirectly aligned with Kentucky
Kentucky's Student AI Use Scale gives instructors and students a shared vocabulary for assignment-specific expectations, supported by institution-wide faculty guidance and explicit concern for fairness beyond detection. Integrevise makes that framework operational by creating human-reviewed evidence of understanding at any permitted-use level.
A simple next step
A short conversation is enough to compare priorities, explore fit and decide whether the approach deserves a closer look.
Book a 20-minute conversation No preparation, module selection or assessment redesign needed.