Turn AI-resilient design into direct evidence.
Add a comparable record of student reasoning across assignments while preserving disciplinary variation and faculty ownership.
Integrevise for Washington University in St. Louis
Make authentic student reasoning visible across AI-resilient assessment.
WashU is helping faculty redesign assignments and courses for meaningful AI use while recommending authentic, oral and project-based alternatives. Integrevise adds a short adaptive discussion grounded in the student’s submitted work, giving academics reviewable evidence of understanding without AI detection or universal live exams.
One practical evidence layer
Support authentic assessment without adding a live viva to every submission.
Integrevise works from materials teaching teams already use. It creates a structured opportunity for explanation while academic judgment remains with the university.
Ask the next question without adding a viva timetable.
Review an adaptive discussion tied to the work, brief and outcomes, focusing academic attention where it adds most value.
Demonstrate understanding beyond the artifact.
Give students a transparent opportunity to explain decisions, reflect on process and receive feedback grounded in their own submission.
Peer-reviewed university study2026One university setting
Early evidence, clearly bounded
A practical approach with preliminary university evidence.
A published, multi-cycle study at one private US liberal-arts college offers preliminary qualitative evidence that post-submission oral verification can surface comprehension gaps and implementation issues. It does not establish broad efficacy or scalability.
Read the peer-reviewed studyAI-resilient assignments at WashU
WashU is investing in authentic assessment and thoughtful course redesign.
WashU’s Center for Teaching and Learning recommends authentic assignments and non-traditional demonstrations such as oral exams and projects when faculty want AI-resilient learning. Its 2026 AI Curriculum Corps supports faculty to revise individual assignments and whole courses with peer, student and CTL review.
Integrevise offers one configurable way to capture student explanation after submission. It complements faculty-led redesign and academic-integrity practice; it does not prescribe AI rules, detect AI use or replace academic judgement.
These official sources establish context; they do not imply that the university has endorsed Integrevise.WashU CTL guidanceAuthentic tasks, oral alternatives and meaningful AI integration support learning.
Integrevise addsReviewable evidence through student explanation.
A focused first conversation
Compare priorities for authentic, reviewable student evidence.
Discuss WashU’s assignment-design, faculty-workload and student-support context before considering any closer evaluation.
Book a 20-minute conversation No module selection or assessment redesign required.