Assessment conversations around generative AI often default to concerns about academic integrity, leading to a binary distinction between “secure” and “open” assessment environments. In this framing, any unproctored or open context is assumed to involve AI use, limiting how faculty approach assessment design. Other frameworks present tiered-use approaches, but do not address design elements or task examples.
This session introduces a practice-informed framework for distinguishing AI-integrated and AI-resilient assessment approaches. Drawing on faculty development and instructional design work in higher education, the session will highlight key design characteristics that move beyond the secure/open binary and support more intentional alignment between learning goals and AI use.
Participants will explore:
- design characteristics of AI-integrated and AI-resilient assessments
- examples of assessment tasks across disciplines
- practical strategies for adapting existing assignments
The session is designed for faculty and instructional designers seeking concrete ways to rethink assessment in response to generative AI.
