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Updated: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Models in Nursing Education: Concept Analysis.

Julia Harrington1, Richard G Booth1, Kimberley T Jackson1

  • 1Arthur Labatt Family School of Nursing, Western University, 1151 Richmond Street, London, N6A 3K7, Canada, 1 519-661-3395.

JMIR Nursing
|August 22, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) offer accessible, personalized learning in nursing education. This analysis defines LLMs as innovative tools enhancing cognitive skills and teaching quality.

Keywords:
artificial intelligenceconcept analysisgraduate nursing educationlarge language modelsnursing educationundergraduate nursing education

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Area of Science:

  • Nursing Education
  • Artificial Intelligence
  • Concept Analysis

Background:

  • Conceptual foundations of Large Language Models (LLMs) in nursing education are underexplored.
  • Need for clarity on LLMs' relevance, meaning, and attributes in nursing education.
  • Application of Rodgers' evolutionary method for concept analysis.

Purpose of the Study:

  • To explore the evolutionary concept of LLMs in nursing education.
  • To provide a concept analysis of LLMs through literature review.
  • To define LLMs within the context of nursing education.

Main Methods:

  • Utilized Rodgers' evolutionary concept analysis.
  • Conducted a comprehensive literature search across PubMed, CINAHL, PsycINFO, Scopus, and Google Scholar.
  • Included 41 papers published within the last 5 years, focusing exclusively on LLMs in nursing education.

Main Results:

  • Developed a proposed definition of LLMs in nursing education.
  • Characterized LLMs as accessible, personalized, innovative, and interactive tools.
  • Identified LLMs' potential to enhance cognitive and skill development and improve learning/teaching quality.

Conclusions:

  • LLMs hold transformative potential for nursing education, improving resource access and individualized learning.
  • Integration of LLMs requires careful consideration of limitations and ethical implications.
  • Ensuring LLM integration aligns with nursing education values, especially in graduate programs.