Generative Artificial Intelligence and Academic Integrity Policies and Practices of UNE Business School: Challenges and the Way Forward

Abstract

This article considers generative artificial intelligence (gAI) in the context of UNE Business School (UNEBS). In particular, it discusses the challenges surrounding the formulation and implementation of academic integrity policies and processes. Despite the growing body of literature on gAI in the higher education sector, there is limited guidance on how business schools might harness and manage gAI with appropriate policies and processes. While UNE has noted the potential of gAI to enhance student learning, policy responses at the faculty or school level have been hurried and haphazard. For example, processes to prevent gAI-specific plagiarism are not supported by adequate resources to handle a burgeoning number of cases. More importantly, the need for a more equitable approach to policy development and practices that ensures the diversity of student voices and staff concerns is taken into account has been overlooked. This exploratory research responds to this gap and draws on two work-in-progress research projects the author is currently involved in to identify gAI-specific academic integrity policy challenges. The findings presented here have practical implications for unit coordinators, academic integrity officers, and course managers at the UNEBS and beyond.