AI has not created an academic integrity crisis so much as exposed a weakness in how we assess learning. When a generative AI system can complete an assignment in seconds and earn a strong grade, the central question is no longer simply whether a student used AI. It is whether the assignment enables students to demonstrate that they can think, explain, apply, adapt and defend what they know.
More surveillance and better detection will not rebuild trust or ensure learning. Faculty and instructors need assessment designs that make students’ thinking visible, encourage the transparent and productive use of AI, and reduce opportunities for cognitive surrender.
This webinar introduces a practical approach to assessment redesign built around three actions: Build, Adapt and Defend. Participants will explore how process-based assessment, oral defence, witnessed performance, iterative work and competency demonstrations can strengthen academic integrity while developing both disciplinary knowledge and enduring human capabilities.
Three critical questions this webinar will address
- How can faculty distinguish productive cognitive offloading from the outsourcing of learning?
- What kinds of assignments require students to demonstrate their own judgement, understanding and ability to transfer learning?
- How can courses and programs move beyond AI detection toward transparent, coherent and defensible assessment practices?
Key takeaways
This webinar will help you to:
- Test whether your current assignments can be completed successfully by generative AI and identify where redesign is needed.
- Apply the Build, Adapt and Defend model to make student thinking, decision-making and understanding visible.
- Design assessments that combine process evidence, iterative drafts, oral defence, presentations, simulations and witnessed performance.
- Assess evergreen capabilities such as judgement, creativity, adaptability, collaboration and ethical awareness alongside disciplinary knowledge.
- Begin conversations about program-level AI literacy, assessment design and transparent expectations for students.
