Publications

@article{seo_utilization_2024,

title = {Utilization of Generative Artificial Intelligence in Nursing Education: A Topic Modeling Analysis},

author = {W. J. Seo and M. Kim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210378858&doi=10.3390%2feducsci14111234&partnerID=40&md5=417127fbeb94cc40d893efe11a149ad3},

doi = {10.3390/educsci14111234},

issn = {22277102 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Education Sciences},

volume = {14},

number = {11},

abstract = {The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students’ learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed “healthcare”, “use”, and “risk” were common keywords. The limited emphasis on “threats”, “strengths”, and “weaknesses” compared to “opportunities” in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines. © 2024 by the authors.},

keywords = {generative artificial intelligence, Nursing, nursing education, nursing education research, patients, Students, topic modeling},

pubstate = {published},

tppubtype = {article}

}