Empowering Faculty to Incorporate Large Language Models in Nursing Education Using a Delegation Framework

Jason Blomquist; Sarah Llewellyn; Jenny Alderden; Kelley Connor

doi:10.1097/01.NEP.0000000000001246

Empowering Faculty to Incorporate Large Language Models in Nursing Education Using a Delegation Framework

Nurs Educ Perspect. 2024 Feb 23. doi: 10.1097/01.NEP.0000000000001246. Online ahead of print.

Authors

Jason Blomquist¹, Sarah Llewellyn, Jenny Alderden, Kelley Connor

Affiliation

¹ About the Authors The authors are faculty in the School of Nursing, Boise State University, Boise, Idaho. Jason Blomquist, MAOL, RN, NEA-BC, CHSE, is a clinical assistant professor. Sarah Llewellyn, PhD, RN, CNE, is an assistant professor. Jenny Alderden, PhD, APRN, CCRN, CCNS, is an associate professor. Kelley Connor, PhD, RN, ANEF, CHSE, is a professor. The authors acknowledge ChatGPT, a large language model developed by OpenAI, for providing helpful responses and insights during the research process. For more information, contact Jason Blomquist at jasonblomquist@boisestate.edu.

PMID: 38407340
DOI: 10.1097/01.NEP.0000000000001246

Abstract

Large language models (LLMs) can support nursing education but pose questions of validity, reliability, and ethical use. This article proposes using the five rights of nursing delegation framework by the National Council of State Boards of Nursing to teach nursing students about the appropriate use of LLMs in health care and nursing education. Nursing faculty can teach students how to assess the validity and reliability of the information provided by LLMs, document its use, and reference and cite information appropriately.