Policies, procedures, and guidelines: are universities effectively ensuring AI (academic integrity) in the era of generative AI?

Abstract

The objective of this study was to analyze Generative AI guidelines and policies at Canadian universities, examining how these universities are ensuring academic integrity in the face of challenges posed by using Generative AI tools in academic work. Focusing on assessment redesign, AI-content citation, and AI-detection, the study employed qualitative document analysis of policies and guidelines from the top twenty Canadian universities according to Times Higher Education World Rankings. This purposive sampling strategy, focused on leading institutions from different provinces, aimed to provide a representative overview of best practices and emerging trends in Generative AI policy and guideline development. The analysis revealed both commonalities and differences in institutional approaches. While universities generally emphasize transparency through documentation, updated academic integrity policies, and instructor autonomy in AI use, they differ in their approaches to AI detection tools, as well as AI acknowledgment and citation. These results show Canadian universities' varied strategies to address the complexities of Generative AI in academic environments. The study identifies key recommendations for instructors, students, researchers, and staff, offering a foundation for developing comprehensive Generative AI guidelines at the university level.