Redesigning Assessments for AI-Enhanced Learning: A Framework for Educators in the Generative AI Era

Abstract

The emergence of generative artificial intelligence (Gen AI) in education offers both opportunities and challenges, particularly in the context of student assessment. This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose. A qualitative methodology was employed, gathering data through semi-structured interviews and focus groups, along with examples of redesigned assessments. Sixty-one faculty members participated in the study, and the data were analyzed using both deductive and inductive thematic approaches. Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies. However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns. The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era. Based on these insights, the study developed a conceptual framework titled “Against, Avoid, Adopt, and Explore”. Future research is needed to validate this framework and further refine its application in educational contexts.

Ähnliche Arbeiten