Streamlining Pre-Placement Compliance with GPT-Based Tools: A Case Study from the University of Melbourne

Abstract

As Work-Integrated Learning (WIL) programs expand, universities face increasing complexity in managing pre-placement compliance. This case study describes the development and implementation of a generative pre-trained transformer (GPT)-based internal assistant within the Faculty of Medicine, Dentistry and Health Sciences (MDHS) at the University of Melbourne, to support a small team in drafting policy-aligned compliance communication. A five-component governance framework, BLAKE, was designed to ensure responsible and human-in-the-loop use. Drawing on verified institutional and government sources, the tool generates draft responses that reflect the team's tone and values that is then reviewed by staff before dissemination. Early indications suggest improvements in turnaround times, staff confidence, and communication consistency, were achieved without compromising student privacy or regulatory requirements. This case highlights the potential for carefully governed generative AI tools to support administrative functions in WIL. It may offer a practical and transferable approach for institutions operating in similarly resource-constrained environments.