Large Language Models in Nursing Education: Concept Analysis

Abstract

Background: Large language models (LLMs) are increasingly used in nursing education, yet their conceptual foundations remain abstract and underexplored. This concept analysis addresses the need for clarity by examining the relevance, meaning, contextual applications, and defining attributes of LLMs in nursing education, using Rodgers' evolutionary method.

Objective: This paper aims to explore the evolutionary concept of LLMs in nursing education by providing a concept analysis through a comprehensive review of the existing published literature.

Methods: Rodgers' evolutionary concept analysis method was used. PubMed, CINAHL, PsycINFO, Scopus, and Google Scholar were used to search for relevant publications. A total of 41 papers were included based on inclusion criteria that focused on studies published in English within the last 5 years to ensure relevance to the current use of LLMs exclusively in nursing education. Studies were excluded if they focused on clinical nursing applications, were not available in English, lacked full-text accessibility, or examined other artificial intelligence (AI) technologies unrelated to LLMs (eg, robotics).

Results: As a result of this analysis, a proposed definition of LLMs in nursing education has been developed, describing them as accessible, personalized, innovative, and interactive tools that create revolutionary learning experiences, often leading to enhanced cognitive and skill development and improvement in learning and teaching quality.

Conclusions: This concept analysis highlights LLMs' transformative potential to enhance access to resources, support individualized learning, and augment nursing education. While promising, careful attention must be given to their limitations and ethical implications, ensuring their integration aligns with the values and goals of nursing education, particularly in specialized areas such as graduate nursing programs.

Keywords: artificial intelligence; concept analysis; graduate nursing education; large language models; nursing education; undergraduate nursing education.