Governing generative artificial intelligence in professionally accredited higher education courses: A case study of exercise science education in Australia

Abstract

Professional accreditation standards are increasingly formalising digital practice competencies as graduate requirements. In parallel, institutional governance of generative artificial intelligence (genAI) remains pre-dominantly oriented towards risk management. This study examines the alignment between university policy and evolving professional standards across 35 Australian higher education providers offering Exercise and Sports Science Australia (ESSA) accredited courses.

Through a systematic document analysis of publicly available policies and guidance materials, the research identifies two primary governance models: embedded integrity governance, which integrates genAI into existing misconduct frameworks, and standalone genAI policies providing detailed operational guidance.

Results show that while all institutions permit conditional genAI use, the prevailing policy discourse is framed through risk management, disclosure, and misconduct prevention. Across the sample, only two (6%) of the 35 institutions included any language connecting genAI to professional practice or graduate capability, and none of these connections were discipline specific to exercise science or exercise physiology. This pattern suggests that during the regulatory transition, universities are prioritising assessment security over the integration of genAI as a core professional competency. These results provide a critical baseline for institutions as they prepare for the implementation of revised ESSA Exercise Physiology accreditation standards in January 2027. Future research directions include course-level investigation of how genAI competencies are integrated into curriculum design, graduate outcomes, and cross-disciplinary accreditation governance.