Making sense of generative AI for assessments: Contrasting student claims and assessor evaluations

Abstract

The rapid growth of generative AI usage in higher education has left educators looking urgently for insights into student usage and guidance on good practice. This case study examines an experiential exercise involving 118 postgraduate management students at a UK business school, where students were asked to write a 500-word reflection on their use of AI for a 2500-word essay-style assessment. Using sensemaking as a theoretical lens, we compare students' claims with assessors' evaluations of students' AI usage. Our findings indicate that students predominantly use generative AI for writing, paraphrasing, and rephrasing, rather than for fostering critical thinking or engaging in the more advanced stages of sensemaking, a level achieved by only one-tenth of the cohort. The consistency between this study's findings and pre-generative AI research suggests that higher education has yet to adapt adequately in ways to integrate AI to mitigate, rather than exacerbate, current sector deficiencies. We call on university leaders to develop institutional strategies that allow for effective and responsible integration of generative AI, and on educators to develop students' critical evaluation and academic writing skills that build on generative AI's affordances, with several specific recommendations made in this article. • We examine students' use of generative AI in a written assessment. • We analyse data by deploying sensemaking theory in an educational setting. • Students currently only explore partly the generative AI potential. • By focusing on generative AI for writing, critical thinking skills are neglected. • We propose recommendations for setting assessments where students may use AI.

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