Artificial Intelligence-Assisted Learning in Emergency Nursing Education: A Scoping Review

Nurwahidah

doi:10.58545/jkmi.v5i1.877

Authors

Nurwahidah Universitas Muhammadiyah Jember, Indonesia https://orcid.org/0009-0005-6614-4087

DOI:

https://doi.org/10.58545/jkmi.v5i1.877

Abstract

Background: Artificial intelligence (AI) is increasingly used to support nursing education, particularly in preparing learners to manage emergency and critical care situations. However, evidence from randomized controlled trials (RCTs) remains fragmented. Objective: To map and characterize RCT evidence on AI-assisted learning in nursing education, with emphasis on emergency, resuscitation, and critical-care contexts. Methods: Scoping review following the PRISMA-ScR guideline and the Joanna Briggs Institute framework. Data Sources: PubMed/MEDLINE, Scopus-indexed journals, ProQuest, and reference-list searching were conducted through July 2026. The articles screened studies and extracted data on study characteristics, interventions, comparators, and educational outcomes. Results: Fifteen RCTs (2016–2026) involving over 1,300 nurses and nursing students from nine countries were included. Nine studies evaluated AI-assisted learning in general nursing education, while six focused on emergency, resuscitation, or critical care. AI-based interventions included generative AI, chatbots, intelligent tutoring systems, virtual and augmented reality, and AI-integrated simulation. Most studies reported improvements in knowledge, clinical skills, or learner satisfaction. However, several found no significant advantage over conventional teaching and reported increased cognitive load or anxiety. Conclusion: AI-assisted learning has the potential to enhance emergency and critical care nursing education, particularly in psychomotor skills and clinical decision-making. Nevertheless, current evidence is limited by heterogeneous interventions, small single-center trials, and short follow-up, highlighting the need for larger multicenter RCTs with standardized outcome measures.

Keywords:

Artificial Intelligence, Emergency Nursing, Nursing Education, Simulation Training

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References

Akutay, S., Yüeler Kaçmaz, H., & Kahraman, H. (2024). The effect of artificial intelligence supported case analysis on nursing students’ case management performance and satisfaction: A randomized controlled trial. Nurse Education in Practice, 80, 104142. https://doi.org/10.1016/j.nepr.2024.104142

Arkan, B., Erbay Dallı, Ö., & Varol, B. (2025). The impact of ChatGPT training in the nursing process on nursing students’ problem-solving skills, attitudes towards artificial intelligence, competency, and satisfaction levels: Single-blind randomized controlled study. Nurse Education Today, 152, 106765. https://doi.org/10.1016/j.nedt.2025.106765

Benfatah, M., Elazizi, I., Lamiri, A., Belhaj, H., Nejjari, C., & Youlyouz-Marfak, I. (2025). AI-assisted prebriefing to enhance simulation readiness in nursing education. Teaching and Learning in Nursing, 21, e57–e63. https://doi.org/10.1016/j.teln.2025.07.030

Bodur, G., Turhan, Z., Altun, Y. E., Kilicaslan, K., Alikan, B., Özer, F., & Can, G. (2025). Evaluating the effectiveness of virtual reality simulation in CPR training for nursing students: A randomized controlled trial. Nurse Education in Practice, 87, 104486. https://doi.org/10.1016/j.nepr.2025.104486

Castillo-Rodríguez, J. M., Gómez-Urquiza, J. L., García-Oliva, S., & Suleiman-Martos, N. (2025). Effectiveness of virtual and augmented reality for emergency healthcare training: A randomized controlled trial. Healthcare, 13(9), 1034. https://doi.org/10.3390/healthcare13091034

Cobbett, S., & Snelgrove-Clarke, E. (2016). Virtual versus face-to-face clinical simulation in relation to student knowledge, anxiety, and self-confidence in maternal-newborn nursing: A randomized controlled trial. Nurse Education Today, 45, 179–184. https://doi.org/10.1016/j.nedt.2016.08.004

DeGagne, J. C. (2023). The state of artificial intelligence in nursing education: Past, present, and future directions. International Journal of Environmental Research and Public Health, 20(6), 4884. https://doi.org/10.3390/ijerph20064884

Fealy, S., Jones, D., Hutton, A., Graham, K., McNeill, L., Sweet, L., & Hazelton, M. (2019). The integration of immersive virtual reality in tertiary nursing and midwifery education: A scoping review. Nurse Education Today, 79, 14–19. https://doi.org/10.1016/j.nedt.2019.05.002

Gu, Y., Zou, Z., & Chen, X. (2017). The effects of vSIM for NursingTM as a teaching strategy on fundamentals of nursing education in undergraduates. Clinical Simulation in Nursing, 13(4), 194–197. https://doi.org/10.1016/j.ecns.2017.01.005

Jembu, J. P., & Lee, S. (2025). The effectiveness of Artificial Intelligence (AI) in learning outcomes of nursing students: A meta-analysis of randomized controlled trials (RCTs). STEM Education, 5(4), 546–563. https://doi.org/10.3934/steme.2025026

Kim, H., & Suh, E. E. (2018). The effects of an interactive nursing skills mobile application on nursing students’ knowledge, self-efficacy, and skills performance: A randomized controlled trial. Asian Nursing Research, 12(1), 17–25. https://doi.org/10.1016/j.anr.2018.01.001

Lifshits, I., & Rosenberg, D. (2024). Artificial intelligence in nursing education: A scoping review. Nurse Education in Practice, 104148. https://doi.org/10.1016/j.nepr.2024.104148

Lo, Y. T., Yang, C. C., Yeh, T. F., Tu, H. Y., & Chang, Y. C. (2022). Effectiveness of immersive virtual reality training in nasogastric tube feeding education: A randomized controlled trial. Nurse Education Today, 119, 105601. https://doi.org/10.1016/j.nedt.2022.105601

Ma, J., Dai, W., Yuan, X., Zhu, X., Ding, G., Song, Y., Wu, H., Zhang, Y., Zhou, Y., Chen, W., & Nan, J. (2026). Immediate learning outcomes of AI- and sensor-integrated versus face-to-face CPR retraining for chest compression quality: A randomized trial. Digital Health, 12. https://doi.org/10.1177/20552076251411030

Othman, S. Y., Ghallab, E., Eltaybani, S., & Mohamed, A. M. (2024). Effect of using gamification and augmented reality in mechanical ventilation unit of critical care nursing on nurse students’ knowledge, motivation, and self-efficacy: A randomized controlled trial. Nurse Education Today, 142, 106329. https://doi.org/10.1016/j.nedt.2024.106329

Padilha, J. M., Machado, P. P., Ribeiro, A., Ramos, J., & Costa, P. (2019). Clinical virtual simulation in nursing education: Randomized controlled trial. Journal of Medical Internet Research, 21(3), e11529. https://doi.org/10.2196/11529

Ricci, S., Calandrino, A., Borgonovo, G., Chirico, M., & Casadio, M. (2022). Viewpoint: Virtual and augmented reality in basic and advanced life support training. JMIR Serious Games, 10, e28595. https://doi.org/10.2196/28595

Rizany, I., Pertiwiwati, E., Setiawan, H., Jumbri, M., Rahmaniah, L., & Rahman, M. R. (2021). Kompetensi perawat terhadap Keselamatan Pasien di Beberapa Rumah Sakit Pinggiran Sungai Aliran Barito. Dunia Keperawatan: Jurnal Keperawatan Dan Kesehatan, 9(2), 319. https://doi.org/10.20527/dk.v9i2.11946

Saatçi, G., Korkut, S., & Ünsal, A. (2024). The effect of the use of artificial intelligence in the preparation of patient education materials by nursing students on the understandability, actionability and quality of the material: A randomized controlled trial. Nurse Education in Practice, 81, 104186. https://doi.org/10.1016/j.nepr.2024.104186

Şimşek-Çetinkaya, Ş., & Çakır, S. K. (2023). Evaluation of the effectiveness of artificial intelligence assisted interactive screen-based simulation in breast self-examination: An innovative approach in nursing students. Nurse Education Today, 105857. https://doi.org/10.1016/j.nedt.2023.105857

Terzi, B., Şahin, H., Emir, G., Uysal, G., Sönmez Düzkaya, D., Yalnız, N., & Cengiz, M. (2026). Effect of virtual reality training on novice intensive care unit nurses: A randomized controlled pilot study. Research Square (preprint, belum ditinjau sejawat). https://doi.org/10.21203/rs.3.rs-8585355/v1

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850