Mitigating Inappropriate AI Use with Process-Oriented Assignments

As generative AI tools become increasingly embedded in student life, many instructors feel caught between surveillance approaches that attempt to detect AI use and a sense of resignation that meaningful assessment is impossible. This session offers a third path: designing assignments that make student thinking visible throughout the learning process,  reducing opportunities for inappropriate AI use while supporting genuine learning.

We’ll explore how process-oriented assignments—including scaffolded drafts, reflective annotations, in-class components, and iterative feedback loops—create transparency into student work without relying on detection software. This workshop acknowledges the legitimate concerns driving surveillance-oriented approaches while presenting alternatives that are often more effective and less adversarial. You’ll leave with practical strategies you can adapt to your own courses.