Jasper Roe (Durham University), Mike Perkins (British University Vietnam), & Louie Giray (Mapua University), Assessment twins: An approach for strengthening assessment validity in the age of generative AI (6-4-26).

This article is worth a read and some consideration. For those of us who teach legal writing and upper-division writing seminars worry equally about learning and assessment accuracy resulting from student misuse of AI, this article offers a solution. Do we ensure fair assessment by abandoning take-home projects or continue to assess using these rich learning opportunities and hope our students don’t cheat? The article’s abstract introduces a third possibility:

In this study, we introduce the concept of assessment twins as a practical approach to redesigning assessment tasks. We use Messick’s unified validity framework to systematically map the ways in which GenAI threatens content, structural, consequential, generalisability, substantive, and external validity. Following this, we conceptualise assessment twins as two deliberately linked components that address the same learning outcomes through different modes of evidence, scheduled closely together to allow for cross-verification. We explain how the twin approach helps mitigate validity threats by triangulating evidence across pedagogically valuable, yet GenAI-vulnerable, assessment formats. To guide implementation, we propose an assessment twin design process: identifying vulnerabilities, aligning outcomes, selecting complementary tasks, and developing interdependent marking schemes