Understanding Academic Integrity Education: Case Studies from Two Australian Universities

Abstract

An increase in Academic Integrity (AI) breaches has resulted in higher education institutions implementing solutions to improve AI competence. It has been argued that to improve students’ AI understanding, concepts and skills should be taught at the classroom level and contextual factors should be considered. This article presents an investigation on how AI is taught at the classroom level across a range of disciplines, how contextual factors inform approaches to AI education, and how the approaches align with evidence-based recommendations. Purposeful sampling procedures were employed to select units of study from disciplines at two Australian universities. Qualitative data collection methods were used to capture ways AI education was approached and the collected data were analysed through grounded theory methods. The findings show that AI was primarily taught through explicit instruction and personal storytelling and assessed through summative assessment. Such approaches appear to be influenced by personal philosophies, institutional mandates and student backgrounds. While the approaches align with the notion that best practice includes an educative approach, other facets of best practice that have been promoted to combat the rise in AI breaches such as collusion, assessment outsourcing and cheating in exams were not evident.