Augmenting Case Based Learning with Dynamic Language Models

This paper describes a novel supporting tool for the case-based learning (CBL). Recent advances in deep-learning based language models (LMs) have enabled highly dynamic interactivity in dialog services and story generation. We leverage the progress in modelling language to develop a technologically augmented CBL pedagogy which we analyze with a standardized assessment. Our assessment shows reasonable case interactivity, low rates of factual inaccuracy, and no inappropriate machine-sourced responses. We also compare our assessment results across the case categories of Ethics, Chemistry, Biology, and Medicine, but find no statistically significant differences. In summary, we develop a framework for analyzing the ability of LMs to augment CBL, apply this framework to the GPT-3 LM, and discuss some of the challenges and potential solutions to ensuring proper usage in the classroom environment.