Designing assessments for large university classes: Considering Artificial Intelligence, academic integrity and Bloom's Revised Taxonomy

This article seeks to report on the design and assessment of an undergraduate module in developmental psychology, as delivered to a cohort of 500+ students. This module is positioned in year one of an initial teacher education programme in an Irish university. Although this module had been delivered across preceding years, the surge in use of Artificial Intelligence (AI) amongst university students served as a catalyst for reconsidering the selected mode of assessment. During module design, the lecturer placed particular emphasis on ensuring the chosen assessment promoted broad levels of student learning across all domains of Bloom’s Revised Taxonomy of Educational Objectives (Anderson & Krathwohl, 2001), whilst minimising the potential threats of AI to academic integrity. Based on a review of students’ in-class attendance, online engagement, and performance in the terminal examination, strengths and limitations of the module and chosen assessment are acknowledged, with reference to implications for practice in the large class context.
Keywords: Bloom’s Revised Taxonomy