Reframing Assessments

Abstract

This study addresses the limitations of traditional assessment practices and proposes a conceptual model to reframe assessments for authenticity in the context of generative artificial intelligence (AI). Traditional assessment practices often fail to capture diverse knowledge and can be exploited by students' misuse of generative AI tools for unfair academic advantages, which underscores the need for robust assessment mechanisms. This study explores how authentic assessments can be integrated with generative AI tools to mitigate academic dishonesty and enhance the learning experience. Building on existing AI approaches in higher education, this study develops a model integrating generative AI in authentic assessments. This model can serve as a framework for incorporating authenticity in assessment practices while leveraging the capabilities of generative AI. An example illustrating the conceptual model, along with several reimagined authentic assessment types, and mitigation strategies for reframing authentic assessment design, are provided.