Applying Generative AI to the Learning Analytics Cycle to Support Competency Development in Health Professions Education

Abstract

Professional learning environments offer a unique opportunity for exploring professional competencies due to the use of workplace data, which captures important aspects of professional performance. This data is different from that used in formal learning analytics contexts, as it is not collected by systems designed to understand learning. Instead, workplace data represents the digital fingerprint professionals leave behind when interacting with technologies to do their jobs. Harnessing this data to support workplace learning is challenging for a range of reasons, including being able to identify meaningful metrics to identify individual performance and scaffold the use of this data to support professional learning interventions on knowledge and performance. Generative AI (GenAI) has great potential to enhance professional learning analytics by addressing some of the challenges inherent in using workplace data. These challenges include needing to process large amounts of unstructured data to understand individual performance and transform this data into interfaces and interventions that can support learning. In our paper, we modify Clow’s learning analytics cycle to inform a modified framework describing the intersection of learning analytics with professional learning. Subsequently, we illustrate the potential power of GenAI for supporting professional learning across the framework through three case studies in health professions education.