Nurse Educators' Perceptions and Experiences of Generative Artificial Intelligence

As technology continues to transform education, the adoption of generative artificial intelligence is increasing in nursing education. However, concerns regarding the accuracy of AI-generated content and ethical issues exist. This study explores the perceptions/experiences of nurse educators in South Korea regarding the use of generative artificial intelligence. Using a cross-sectional survey, data were gathered from 120 nurse educators, and descriptive statistical analysis was applied to the data. Significantly 38.9% of participants reported no prior engagement with generative artificial intelligence. Meanwhile, 32.5% identified ChatGPT as their preferred source. The perceived usefulness of generative artificial intelligence was evaluated on average as 3.11 (SD = 0.31) on a 4-point scale, suggesting a generally favorable view of its potential to diversify learning resources, enhance student learning experiences, and improve educational quality. Despite these positive perceptions, the average engagement score with generative artificial intelligence was 2.76 (SD = 0.40), reflecting moderate actual use. This study contributes to the literature on generative artificial intelligence integration in education, revealing an overall positive attitude among nurse educators. It underscores the need for increased application and familiarity with such technologies to maximize teaching strategy benefits, student outcomes, and the efficacy of nursing education.

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Publication Details

Journal
CIN Computers Informatics Nursing
Published
2025-02-28
DOI
https://doi.org/10.1097/cin.0000000000001273
Citations
5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
3.27
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article

Nurse Educators' Perceptions and Experiences of Generative Artificial Intelligence

Minjoo Hong, Sang Suk Kim, Hyewon Shin, Jennie C. De Gagné
5 citations
CIN Computers Informatics Nursing
Artificial Intelligence in Healthcare and Education
3.27
article

Nurse Educators' Perceptions and Experiences of Generative Artificial Intelligence

Minjoo Hong, Sang Suk Kim, Hyewon Shin, Jennie C. De Gagné
article en
5 citations

Abstract

As technology continues to transform education, the adoption of generative artificial intelligence is increasing in nursing education. However, concerns regarding the accuracy of AI-generated content and ethical issues exist. This study explores the perceptions/experiences of nurse educators in South Korea regarding the use of generative artificial intelligence. Using a cross-sectional survey, data were gathered from 120 nurse educators, and descriptive statistical analysis was applied to the data. Significantly 38.9% of participants reported no prior engagement with generative artificial intelligence. Meanwhile, 32.5% identified ChatGPT as their preferred source. The perceived usefulness of generative artificial intelligence was evaluated on average as 3.11 (SD = 0.31) on a 4-point scale, suggesting a generally favorable view of its potential to diversify learning resources, enhance student learning experiences, and improve educational quality. Despite these positive perceptions, the average engagement score with generative artificial intelligence was 2.76 (SD = 0.40), reflecting moderate actual use. This study contributes to the literature on generative artificial intelligence integration in education, revealing an overall positive attitude among nurse educators. It underscores the need for increased application and familiarity with such technologies to maximize teaching strategy benefits, student outcomes, and the efficacy of nursing education.

CIN Computers Informatics NursingVol. 43(7)
Ewha Womans University (KR), International University of Korea (KR)
Quality Education
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
3.27
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Nurse Educators' Perceptions and Experiences of Generative Artificial Intelligence — Minjoo Hong, Sang Suk Kim, et al. · CIN Computers Informatics Nursing (2025) | TGRS Research Map | TGRS