Augmenting case based learning with dynamic language models
Document Type
Conference Proceeding
Publication Date
3-10-2021
Abstract
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.
Recommended Citation
Czejdo, Conrad and Bhattacharya, Sambit, "Augmenting case based learning with dynamic language models" (2021). College of Health, Science, and Technology. 127.
https://digitalcommons.uncfsu.edu/college_health_science_technology/127