Changing assessment landscape in management education with AI-driven technologies: impacts and drivers

Abstract

Abstract AI-driven technologies are reshaping the higher education landscape, particularly in assessment design and setting, execution, grading, evaluation, and feedback, in the field of management education at universities. However, a comprehensive analysis of the extent to which assessment models have evolved and the driving forces behind these shifts remains unexplored. This study aims to explore the impacts and driving factors of AI-driven technologies, including generative AI tools, automated grading and feedback systems, plagiarism detection tools, and adaptive learning platforms, on the evolution of assessment models in management education, with a particular emphasis on Sri Lanka as the chosen research context. The data collected through 15 interviews and various documents, where the number of interviews was determined based on the attainment of data saturation, were analysed using the Substitution, Augmentation, Modification, and Redefinition (SAMR) model and the three isomorphic forces in new institutional sociology. When assessing the impact of AI integration into assessment tasks, this study finds that academics reflect the modification level and augmentation level of incorporation of AI in the design and setting of assessments. Further, it suggests that academics only exhibit the substitution level for both grading and evaluation, as well as the provision of feedback within assessments. Normative pressure emerges as the primary driving force behind these integrations of AI into assessment tasks, specifically in areas such as assessment design, setting, and the provision of feedback. Additionally, the study reveals promising prospects that could be harnessed by incorporating AI tools, especially in developing countries with comparable economic, technological, social, and cultural trajectories in AI adoption within education.

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