Purpose
This study aimed to develop an artificial intelligence (AI)-based pediatric medication safety competence (PMSC) virtual simulation program and evaluate its effectiveness among nursing students.
Methods
This quasi-experimental study used a nonequivalent control group pretest-posttest design and was conducted in December 2025. The AI-based PMSC virtual simulation program was developed based on Kolb’s experiential learning theory, following its 4-stage cycle of concrete experience, reflective observation, abstract conceptualization, and active experimentation. Participants were 48 third-year nursing students in South Korea who were assigned to either the experimental group (n=24) or the control group (n=24) through a nonrandomized allocation procedure. The experimental group participated in the PMSC virtual simulation program. Outcomes included PMSC, medication calculation confidence (MCC), Simulation Task Load Index (SIM-TLX), and learning immersion (LI). Data were collected before and after the intervention using validated instruments, and statistical tests were used to evaluate program effectiveness.
Results
Compared with the control group, the experimental group showed significant improvement in PMSC (t=2.48, p=.017). No significant between-group differences were observed in MCC (t=−0.87, p=.389) or LI (t=0.97, p=.336). In addition, SIM-TLX scores decreased significantly from pretest to posttest in the experimental group (t=−2.61, p=.016).
Conclusion
The AI-based PMSC virtual simulation program may help strengthen PMSC among nursing students and may be suitable for use in pediatric nursing education. Future research should refine the program to better address individual learning needs and integrate complementary simulation-based strategies into pediatric medication nursing education.

Purpose
This study compared healthy lifestyle (HLS) practices and awareness regarding the use of chatbots (A-uC) for health management between childhood and adolescent cancer survivors (CACSs) and their parents, with the aim of assessing the feasibility of tailored artificial intelligence (AI) chatbot-based interventions for holistic survivorship care.
Methods
A descriptive comparative design was employed involving 80 CACSs and 80 parents (N=160) recruited through the Korean Pediatric Cancer Foundation. HLS practices were assessed using a validated seven-domain instrument encompassing physical activity, nutrition, interpersonal relations, stress management, positive life perspective, health responsibility, and spiritual health. A-uC was evaluated using an extended technology acceptance model-based tool. Responses to the open-ended question addressing unmet HLS practices were analyzed using latent Dirichlet allocation topic modeling.
Results
No significant differences were observed between CACSs and parents in overall HLS (CACSs: 3.16±0.80; parents: 3.18±0.36, p=.74). While perceptions across most A-uC domains did not differ significantly, parents demonstrated a significantly higher “intention to use” chatbots for health management than CACSs (p=.03). The mean A-uC scores exceeded 4 (out of 5) in both groups, reflecting positive perceptions of chatbot-based HLS support. Topic modeling identified “exercise,” “healthy diet,” and “regular lifestyle” as common unmet areas.
Conclusion
CACSs and their parents share largely concordant views on HLS and A-uC, with a strong interest in chatbot interventions. These findings underscore the potential of tailored AI chatbot programs to address unmet lifestyle needs and promote holistic survivorship care.

Citations

• What do we want to know from a chatbot? Identifying inquiries among adult survivors of childhood and adolescent cancer in South Korea
  Min Ah Kim, Mirae Kim, Yehwi Park, Chaerim Park, Chungyeon Lee, Kyubum Hwang, Hayoung Oh
  European Journal of Oncology Nursing.2026; 82: 103202.     CrossRef
• What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study
  Hongzhan Jiang, Wanting Shen, Meng Zhou, Yinyin Lyu, Meiqi Meng, Dan Yang, Xuejing Li, Bohan Zhang, Yufang Hao
  Frontiers in Oncology.2026;[Epub]     CrossRef
• AI and Chatbot-Supported Interventions for Physical Activity and Obesity-Related Lifestyle Behaviors: Scoping Review With Attention to Family Involvement
  Qianxia Jiang, Xiayu Summer Chen, Dev Patel, Keith Brazendale, Sualba Alejandro
  JMIR Pediatrics and Parenting.2026; 9: e98889.     CrossRef

Purpose
This study develops a chatbot for school violence prevention (C-SVP) among elementary school students.
Methods
Among the analysis, design, development, implementation, and evaluation (ADDIE) models, ADD phases were applied to develop a C-SVP. Students’ learning needs were identified by constructing content with a design that attracted their attention. Subsequently, a formative evaluation was conducted on the developed C-SVP to test its applicability by ten elementary school students targeting the 5th and 6th grades.
Results
The chatbot was designed using KakaoTalk and named “School Guardian Angel.” The formative evaluation revealed that the developed C-SVP was easily accessible and useful for elementary school students.
Conclusion
The developed C-SVP is expected to be effective in preventing violence among elementary school students. However, further research involving children of various age groups is required.

Citations

• Effects of School Violence Prevention Education Using a Chatbot (SVPE-C) on Sixth-Grade Students in South Korea
  Shin-Jeong Kim, Sunyeob Choi, Kyung-Ah Kang
  The Journal of School Nursing.2026; 42(2): 113.     CrossRef
• Implementation of the ADDIE Model in Chatbot Development using Diagramflow
  Fadhil Mureno Ega Nugraha , Muhammad Setiyawan
  Journal of Technology and System Information.2025; 2(2): 9.     CrossRef
• Intelligent conversational agent as an active strategy for school safety management
  Martha Vidal-Sepúlveda, Cristian Olivares-Rodríguez, Alejandro Sanhueza
  Journal of Risk Research.2025; 28(9-10): 1010.     CrossRef
• Essential Elements for Implementing AI Tools in Elementary School: A Systematic Literature Review
  Jorge Arriola-Mendoza, Gabriel Valerio-Ureña
  Education Sciences.2024; 14(12): 1292.     CrossRef

Purpose
Artificial intelligence (AI) has had a profound impact on humanity; in particular, chatbots have been designed for interactivity and applied to many aspects of daily life. Chatbots are also regarded as an innovative modality in nursing education. This study aimed to identify nursing students' awareness of using chatbots and factors influencing their usage intention.
Methods
This study, which employed a descriptive design using a self-reported questionnaire, was conducted at three university nursing schools located in Seoul, South Korea. The participants were 289 junior and senior nursing students. Data were collected using self-reported questionnaires, both online via a Naver Form and offline.
Results
The total mean score of awareness of using chatbots was 3.49±0.61 points out of 5. The mean scores of the four dimensions of awareness of using chatbots were 3.37±0.60 for perceived value, 3.66±0.73 for perceived usefulness, 3.83±0.73 for perceived ease of use, and 3.36±0.87 for intention to use. Significant differences were observed in awareness of using chatbots according to satisfaction with nursing (p<.001), effectiveness of using various methods for nursing education (p<.001), and interest in chatbots (p<.001). The correlations among the four dimensions ranged from .52 to .80. In a hierarchical regression analysis, perceived value (β=.45) accounted for 60.2% of variance in intention to use.
Conclusion
The results suggest that chatbots have the potential to be used in nursing education. Further research is needed to clarify the effectiveness of using chatbots in nursing education.

Citations

• Predicting nursing students’ behavioral intentions to use AI: The interplay of ethical awareness, digital literacy, moral sensitivity, attitude, self-efficacy, anxiety, and social influence
  Mohammad Abuadas, Zainab Albikawi
  Journal of Human Behavior in the Social Environmen.2026; 36(7): 579.     CrossRef
• Nursing Students' Perceptions and Attitudes on the Application of Artificial Intelligence in Nursing Education: A Mixed‐Methods Systematic Review
  Yuhang Li, Shi Chen, Xiaohui Dong, Xianying Lu, Xinyu Chen, Dingxi Bai, Wen Luo, Ting Cao, Zihao Song, Chaoming Hou, Jing Gao
  Journal of Advanced Nursing.2026; 82(7): 7000.     CrossRef
• Applications, attitudes and ethical considerations of Generative Artificial Intelligence (Gen AI) in nursing education: a scoping review
  Philip Hardie, Andrew Darley, Rosemarie Derwin, Jessica Eustace-Cook, Sean Kearns, Barry Mc Brien, Aysha Siddiquee, David Zheng, Mary Mooney
  BMC Nursing.2026;[Epub]     CrossRef
• Adoption of AI in nursing education- A systematic review of factors influencing student intentions
  Abdulaziz Mofdy Almarwani
  Applied Nursing Research.2026; 88: 152068.     CrossRef
• Educational Applications of AI-Based Chatbots in Nursing: A Scoping Review
  Francisco Fernandes, Rúben Encarnação, José Alves, Carla Pais-Vieira, Suzinara Beatriz Soares de Lima, Paulo Alves
  Nursing Reports.2026; 16(3): 87.     CrossRef
• The effect of using a chatbot integrated with the ARCS motivation model in physiology education: a mixed-methods study
  Yasin Ali Cimen, Gunes Bolatli
  Frontiers in Physiology.2026;[Epub]     CrossRef
• Conceptual analysis of Chatbots in nursing education applications
  Lianhua Li, Xia Chen, Shaoyong Ma, Zhengxia Yang, Yingying Wang
  Nurse Education Today.2026; 166: 107188.     CrossRef
• Importance of AI Chatbot in Nursing Education: A Systematic Review
  Tahira Ghaffar, Rajan Balakrishnan, Mian Adnan Aslam Javaid
  Biological and Clinical Sciences Research Journal.2026; 7(4): 37.     CrossRef
• Effects of nursing students’ attitudes toward artificial intelligence on self-directed learning: Analysis of the mediating effects of digital literacy sub-dimensions
  Younghye Go, Jeong Miri
  The Journal of Korean Academic Society of Nursing .2026; 32(3): 326.     CrossRef
• Integrating artificial intelligence ethically in nursing education
  Dawn L. Riess
  Nursing.2025; 55(4): 56.     CrossRef
• The Turkish Version of the Technology Acceptance Model-Based Scale TAME-ChatGPT: A Validity and Reliability Study
  Ebru Küçük, Buket Meral, Kıymet Yesilçiçek Çalık, Cantürk Çapık
  International Journal of Human–Computer Interactio.2025; 41(23): 14734.     CrossRef
• A Study of Student Demographic Variables as Predictors of Intentions to Adopt Computer-Based Testing in Nursing School Clinical Examination
  Olusegun Ojo Bakare, Adebola Arike Bolarinwa
  ETDC: Indonesian Journal of Research and Education.2025; 4(2): 376.     CrossRef
• The use of generative artificial intelligence (AI) in nursing education
  Mollie Ostick, Bette Mariani, Catherine Lovecchio
  Teaching and Learning in Nursing.2025; 20(4): e1296.     CrossRef
• Development and effects of a scenario-based labor nursing simulation education program using an artificial intelligence tutor: a quasi-experimental study
  Seo-A Park, Hye Young Kim
  Women's Health Nursing.2025; 31(2): 143.     CrossRef
• Driving AI chatbot adoption: A systematic review of factors, barriers, and future research directions
  Norah Alharbi, Fareed Ud Din, David Paul, Edmund Sadgrove
  Journal of Open Innovation: Technology, Market, an.2025; 11(3): 100590.     CrossRef
• Applying the technology acceptance model to examine factors influencing chatbot use in HPV prevention education among nursing students
  Ching-Yi Lai, Wei-Sho Ho
  BMC Nursing.2025;[Epub]     CrossRef
• Integrating a knowledge-based artificial intelligence chatbot into nursing training programs: a comparative quasi-experimental study in Egypt and Saudi Arabia
  Eman A. Shokr
  BMC Nursing.2025;[Epub]     CrossRef
• Effect of using artificial intelligence chatbot about electronic fetal monitoring on maternity nursing students’ performance
  Amal Mohamed Talaat Abdelwahab, Marwa Ibrahim Hamdy Aboraiah, Hanan Elsayed Mohamed Elsayed
  BMC Medical Education.2025;[Epub]     CrossRef
• Evolution of Chatbots in Nursing Education: Narrative Review
  Fang Zhang, Xiaoliu Liu, Wenyan Wu, Shiben Zhu
  JMIR Medical Education.2024; 10: e54987.     CrossRef
• The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care
  Hyewon Shin, Jennie C. De Gagne, Sang Suk Kim, Minjoo Hong
  CIN: Computers, Informatics, Nursing.2024; 42(10): 704.     CrossRef
• Artificial intelligence tools utilized in nursing education: Incidence and associated factors
  Samar Thabet Jallad, Khitam Alsaqer, Baker Ishaq Albadareen, Duaa Al-maghaireh
  Nurse Education Today.2024; 142: 106355.     CrossRef
• The association of artificial intelligence ethical awareness, attitudes, anxiety, and intention-to-use artificial intelligence technology among nursing students
  Moh''d Khair Migdadi, Islam Ali Oweidat, Mohammad R. Alosta, Khalid Al-Mugheed, Amany Anwar Saeed Alabdullah, Sally Mohammed Farghaly Abdelaliem
  DIGITAL HEALTH.2024;[Epub]     CrossRef
• UNLOCKING THE TRANSFORMATIVE POWER OF ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT IN HIGHER EDUCATION
  TIJJANI MUHAMMAD, MARY DAVID
  Innovare Journal of Social Sciences.2024; : 19.     CrossRef

Purpose
This study aimed to identify students' awareness of the use of a chatbot (A-uC), a type of artificial intelligence technology, for violence prevention among elementary school students.
Methods
The participants comprised 215 students in the fourth to sixth grades in Chuncheon, South Korea, and data were collected via a self-reported questionnaire.
Results
The mean A-uC score was 3.43±0.83 out of 5 points. The mean scores for the 4 sub-dimensions of the A-uC tool were 3.48±0.80 for perceived value, 3.44±0.98 for perceived usefulness, 3.63±0.92 for perceived ease of use, and 3.15±1.07 for intention to use. Significant differences were observed in A-uC scores (F=59.26, p<.001) according to the need for the use of chatbots in violence prevention education. The relationships between intention to use and the other A-uC sub-dimensions showed significant correlations with perceived value (r=.85, p<.001), perceived usefulness (r=.76, p<.001), and perceived ease of use (r=.64, p<.001).
Conclusion
The results of this study suggest that chatbots can be used in violence prevention education for elementary school students.

Citations

• Effects of School Violence Prevention Education Using a Chatbot (SVPE-C) on Sixth-Grade Students in South Korea
  Shin-Jeong Kim, Sunyeob Choi, Kyung-Ah Kang
  The Journal of School Nursing.2026; 42(2): 113.     CrossRef
• Parental and child perspectives on healthy lifestyles and artificial intelligence chatbot use among childhood and adolescent cancer survivors: a descriptive comparative study in South Korea
  Kyung-Ah Kang, Shin-Jeong Kim, In-Hye Song, Hee-Jin Yoon
  Child Health Nursing Research.2026; 32(1): 27.     CrossRef
• Scientific Mapping of Chatgpt Usage in Education: A Bibliometric Perspective
  İsmail Eray Dursun, Mustafa Taktak
  Sınırsız Eğitim ve Araştırma Dergisi.2025; 10(1): 123.     CrossRef
• Unpacking artificial intelligence in elementary education: A comprehensive thematic analysis systematic review
  Taoufik Boulhrir, Mahmoud Hamash
  Computers and Education: Artificial Intelligence.2025; 9: 100442.     CrossRef
• A systematic literature review of the acceptability of the use of Metaverse in education over 16 years
  Hui Wen Chua, Zhonggen Yu
  Journal of Computers in Education.2024; 11(2): 615.     CrossRef
• Development of a chatbot for school violence prevention among elementary school students in South Korea: a methodological study
  Kyung-Ah Kang, Shin-Jeong Kim, Byoung-doo Oh, Yu-Hyeon Kim
  Child Health Nursing Research.2024; 30(1): 45.     CrossRef
• ความตระหนักรู้ในการใช้ปัญญาประดิษฐ์เพื่อการเรียนรู้สำหรับนักศึกษาระดับปริญญาตรี คณะศึกษาศาสตร์ มหาวิทยาลัยรามคำแหง
  หนึ่งฤทัย ดิษฐ์โรจน์, วศิน นุชคง , จุฑาภรณ์ มาสันเทียะ
  Interdisciplinary Academic and Research Journal .2024; 4(6): 43.     CrossRef
• Essential Elements for Implementing AI Tools in Elementary School: A Systematic Literature Review
  Jorge Arriola-Mendoza, Gabriel Valerio-Ureña
  Education Sciences.2024; 14(12): 1292.     CrossRef
• Awareness of using chatbots and factors influencing usage intention among nursing students in South Korea: a descriptive study
  So Ra Kang, Shin-Jeong Kim, Kyung-Ah Kang
  Child Health Nursing Research.2023; 29(4): 290.     CrossRef