Evidence beyond detector scores.
Support Reading's positive educational approach to AI with evidence of understanding rather than a claim about how the text was produced.
Integrevise for University of Reading
Reading's blind test showed that markers did not identify 94% of 33 submitted AI-generated answers as AI. Integrevise adds a short adaptive oral discussion after submission so students can explain, justify and defend their work, producing structured evidence for staff and personalised feedback for the student.
Value at every level
Integrevise adds a practical evidence layer while preserving Reading's positive approach to AI, academic judgement and the assessments faculty already use.
Support Reading's positive educational approach to AI with evidence of understanding rather than a claim about how the text was produced.
Use the existing brief, rubric and student work to create a structured explanation record.
Give students a fair chance to show their own understanding and receive feedback on where reasoning needs 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 Reading
Reading combines a three-category approach to AI in assessment with direct research showing that polished output is weak evidence of individual understanding: markers did not identify 94% of 33 AI-generated answers in its blind test. Integrevise makes the response constructive by creating direct evidence of reasoning while instructors retain every academic decision.
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.