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Effects of a nursing leadership program on self-leadership, interpersonal relationships, clinical performance, problem-solving abilities, and nursing professionalism among nursing students in South Korea: a quasi-experimental study
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Sunmi Kim, Young Ju Jeong, Hee Sun Kim, Seok Hee Jeong, Eun Jee Lee
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J Korean Acad Nurs 2025;55(1):137-151. Published online February 25, 2025
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DOI: https://doi.org/10.4040/jkan.24110
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 Abstract
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- Purpose
This study investigated the effects of a nursing leadership program on self-leadership, interpersonal relationships, clinical performance, problem-solving abilities, and nursing professionalism among nursing students in South Korea.
Methods
A quasi-experimental study was conducted. The Practice-Driven Nursing Leadership Program for Students (PDNLP-S) was developed based on the ADDIE model (analysis, design, development, implementation, and evaluation). This quasi-experimental study design included 60 nursing students. The experimental group (n=30) participated in the PDNLP-S for 120-minute sessions over 5 weeks, while the control group (n=30) received usual lectures. The PDNLP-S included lectures, discussions, and individual and group activities to cultivate core nursing leadership competencies such as individual growth, collaboration, nursing excellence, creative problem-solving, and influence. Data were analyzed using descriptive statistics, the Mann-Whitney U-test, and the independent t-test with IBM SPSS Windows ver. 26.0.
Results
The experimental group demonstrated significant improvements in self-leadership (t=3.28, p=.001), interpersonal relationships (t=3.07, p=.002), clinical performance (U=268.50, p=.004), and problem-solving abilities (t=2.20, p=.017) compared to the control group. No significant difference was observed in nursing professionalism (t=0.50, p=.311).
Conclusion
This study demonstrates that the PDNLP-S improved nursing students’ self-leadership, interpersonal relationships, clinical performance, and problem-solving abilities. The PDNLP-S can play a significant role in cultivating future nurse leaders by enhancing these nursing leadership competencies among nursing students.
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Research trends in generative artificial intelligence (ChatGPT, etc.) in nursing: a scoping review
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Myung Jin Choi, Myoung Hee Seo, Jihun Kim, Sunmi Kim, Seok Hee Jeong
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Received January 22, 2025 Accepted June 4, 2025 Published online June 18, 2025
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DOI: https://doi.org/10.4040/jkan.25006
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Abstract
ePub
- Purpose
Generative artificial intelligence (AI) has yet to be comprehensively analyzed in the nursing literature. This study aimed to identify research trends in generative AI within the nursing field through a scoping review and propose strategies for its effective utilization in nursing.
Methods
A scoping review was conducted following Arksey and O’Malley’s six-stage framework. The inclusion criteria included: (1) studies conducted in nursing; (2) research related to generative AI; and (3) original research articles, theses, communications, editorials, letters, or commentaries published in academic journals. Database used PubMed, Embase, CENTRAL, CINAHL, KMbase, KoreaMed, KISS, ScienceON, RISS, DBpia, and 27 nursing-specific journals.
Results
In total, 387 studies were initially identified, and 58 were included in the final analysis. In the care domain, strengths included rapid information retrieval and improved nurse-patient communication, while limitations included the irreplaceable human element and low reliability. The administration domain had no relevant studies. In the research domain, generative AI exhibited strengths such as enhanced efficiency in the paper writing process and improved dissemination speed, but its weaknesses included lack of ethical and legal accountability and a risk of inaccurate or biased information. In the education domain, generative AI was effective in saving time in educational design and implementation, as well as supporting content creation, but challenges included algorithmic bias and risks of plagiarism.
Conclusion
This study identified potential benefits and limitations of generative AI across nursing domains. For effective application, it is essential to develop comprehensive guidelines and policies, provide user education and support, and create opportunities for nurses, educators, and students to learn about strengths and risks of generative AI.
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