The wicked problem of AI and assessment

Abstract

Generative artificial intelligence (GenAI) has created significant assessment challenges in higher education. Universities and teachers have responded to these in various ways, including by way of new policies, revised assessment formats, and technological safeguards. These responses are typically predicated on an assumption that AI in assessment is a problem that can be solved if only the right approach can be found. However, in this paper we argue that GenAI may not be that kind of problem at all. Drawing on interviews with 20 teachers responsible for assessment design within a large Australian University, this paper applies Rittel and Webber framework of wicked problems to analyse educators’ experiences with assessment design in the context of GenAI. Our findings demonstrate that the GenAI-assessment challenge exhibits all ten characteristics of wicked problems. For instance, it resists definitive formulation, offers only better or worse rather than correct solutions, cannot be tested without consequence, and places significant responsibility on decision-makers. In the light of this redefinition of the AI and Assessment problem, we argue that educators require certain institutional permissions–including permission to compromise, diverge, and iterate–to appropriately navigate the assessment challenges they face.