کاربست هوش مصنوعی در آموزش پرستاران
چکیده
هدف: آموزش پرستاری در قرن بیست و یکم با چالشهای بیسابقهای مانند کمبود شدید و مداوم نیروی پرستار ماهر، پیچیدگی مراقبتهای بهداشتی با پیشرفت فناوریهای پزشکی، افزایش بار بیماریهای مزمن، ضرورت ارایه مراقبتهای مبتنی بر شواهد، کمبود اساتید مجرب و باتجربه مواجه است. هدف این پژوهش "بررسی کاربست هوش مصنوعی در آموزش پرستاران" است.
روششناسی پژوهش: این پژوهش با استفاده از روش کیفی تحلیل مضمون به بررسی مضامین اصلی در کاربست هوش مصنوعی در آموزش پرستاران در مقالات منتشرشده بین سالهای ۲۰۱۵ تا ۲۰۲۵ میپردازد.
یافتهها: با بررسی ۴۲ مقاله معتبر، پنج مضمون اصلی شناسایی شد: 1- شبیهسازیهای مبتنی بر هوش مصنوعی و محیطهای یادگیری مجازی، 2- سیستمهای آموزش شخصیسازیشده و تطبیقی، 3- تحلیل پیشبینی کننده و مداخلات آموزشی زودهنگام، 4- چالشهای اخلاقی و حریم خصوصی و 5- آمادهسازی پرستاران برای محیطهای کاری هوشمند. یافتهها نشان میدهد که هوش مصنوعی پتانسیل قابلتوجهی برای افزایش کیفیت آموزش پرستاری دارد اما نیازمند رویکردی متعادل با توجه به ملاحظات اخلاقی و انسانی است.
اصالت/ارزشافزوده علمی: این مطالعه با بررسی پژوهشهای پیشین در زمینه کاربرد هوش مصنوعی در آموزش پرستاران جایگاه، نقش و کاربرد این فناوری نوین را برای جامعه پرستاری آشکار میسازد تا از در دستاوردهای آن در این جامعه دچار چالش بهره ببرند.
کلمات کلیدی:
هوش مصنوعی، آموزش پرستاری، تحلیل مضمون، فناوری آموزشیمراجع
- [1] Akyon, S., & Akyon, F. (2025). Digital transformation of clinical education through artificial intelligence: A strengths, weaknesses, opportunities, and threats (SWOT) analysis. Ankara medical journal, 25(1). https://doi.org/10.5505/amj.2025.63373
- [2] Takhdat, K., El fadely, A., Mohamed, E., Ouaamr, A., & El Adib, A. R. (2026). A systematic review of generative artificial intelligence-powered healthcare simulation for clinical reasoning skills development: Applications, outcomes and challenges. Medical science educator, 1–25. https://doi.org/10.1007/s40670-026-02804-6
- [3] Kang, J., & Ahn, J. (2025). Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: A narrative review. Ewha medical journal, 48(4), e53. https://doi.org/10.12771/emj.2025.00787
- [4] Organization, World Health. (2025). State of the world’s nursing 2025: Investing in education, jobs, leadership and service delivery. World Health Organization. https://www.icn.ch/sites/default/files/2025-05/SOWN 2025.pdf
- [5] Buchan, J. (2002). Global nursing shortages: Are often a symptom of wider health system or societal ailments. Bmj, 324(7340), 751-752. https://doi.org/10.1136/bmj.324.7340.751
- [6] Palander, S., Haapa, T., Juntunen, J., Lim, S., Zhou, W., Tomietto, M., & Mikkonen, K. (2026). The effectiveness of peer learning interventions in nursing students’ clinical practice: A systematic review. Nurse education in practice, 92, 1–15. https://researchportal.northumbria.ac.uk/en/publications/the-effectiveness-of-peer-learning-interventions-in-nursing-stude/
- [7] Barnett, T., Cross, M., Jacob, E., Shahwan-Akl, L., Welch, A., Caldwell, A., & Berry, R. (2008). Building capacity for the clinical placement of nursing students. Collegian, 15(2), 55–61. https://doi.org/10.1016/j.colegn.2008.02.002
- [8] Choperena, A., Rosa-Salas, V. La, Esandi-Larramendi, N., Diez-Del-Corral, M. P., & Jones, D. (2025). Nursing educational framework: A new nurse-driven, conceptually guided approach. International journal of nursing knowledge, 36(1), 29–38. https://doi.org/10.1111/2047-3095.12459
- [9] Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. https://www.amazon.com/Deep-Medicine-Artificial-Intelligence-Healthcare/dp/1541644638
- [10] Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial intelligence in healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
- [11] Jiang, J., Ye, M. Z., Kwok, T. T., & Wong, J. Y. H. (2026). GenAI-supported virtual patients in health care education: Systematic review. Journal of medical internet research, 28, e82756--e82756. https://europepmc.org/article/med/42098926
- [12] Tharalson, E., Morgan, M., Ilchak, D., Sebbens, D., & Shurson, L. (2023). Innovative digital pedagogy: Adaptive learning platform integration in nurse practitioner curriculum. The journal for nurse practitioners, 19(10), 104773. https://doi.org/10.1016/j.nurpra.2023.104773
- [13] Hannaford, L., Cheng, X., & Kunes-Connell, M. (2021). Predicting nursing baccalaureate program graduates using machine learning models: A quantitative research study. Nurse education today, 99, 104784. https://doi.org/10.1016/j.nedt.2021.104784
- [14] De Gagne, J. C., Hwang, H., & Jung, D. (2024). Cyberethics in nursing education: Ethical implications of artificial intelligence. Nursing ethics, 31(6), 1021–1030. https://doi.org/10.1177/09697330231201901
- [15] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
- [16] Bichel-Findlay, J., Koch, S., Mantas, J., Abdul, S. S., Al-Shorbaji, N., Ammenwerth, E., ... & Wright, G. (2023). Recommendations of the international medical informatics association (IMIA) on education in biomedical and health informatics: Second revision. International journal of medical informatics, 170, 104908. https://doi.org/10.1016/j.ijmedinf.2022.104908
- [17] Xie, W., Liu, F., Liu, J., & Liu, S. (2026). Beyond literacy to clinical competency: A framework for integrating generative AI into nursing education. Nurse education today, 107108. https://doi.org/10.1016/j.nedt.2026.107108
- [18] Buonaccorso, L., Soncini, S., Bassi, M. C., Mecugni, D., & Ghirotto, L. (2025). Training healthcare professionals to dignity-in-care: a scoping review. Nurse education today, 146, 106543. https://doi.org/10.1016/j.nedt.2024.106543
- [19] Sezer, E. (2026). What AI cannot teach: An epistemological reconceptualisation of the nurse educator role based on an analysis of carper’s ways of knowing. Nursing inquiry, 33(3), e70118--e70118. https://doi.org/10.1111/nin.70118
- [20] Chiaranai, C. (2025). Bringing telehealth and artificial intelligence into nursing practice according to the International Council of Nurses’ vision. Pacific rim international journal of nursing research, 29(4), 689–695. https://doi.org/10.60099/prijnr.2025.275177
- [21] Abualrahi, A., Habobi, S., Almutar, S., Al-Khwaildi, F., Alalq, M., Bomurah, R., … Al-Sadah, F. (2024). Paradigm shift: A systematic review of integrating artificial intelligence in nursing education. American journal of nursing research, 12(3), 50–56. https://elibrary.ru/item.asp?id=79240170
- [22] Hwang, G. J., Tang, K. Y., & Tu, Y. F. (2024). How artificial intelligence (AI) supports nursing education: Profiling the roles, applications, and trends of AI in nursing education research (1993--2020). Interactive learning environments, 32(1), 373–392. https://doi.org/10.1080/10494820.2022.2086579
- [23] Bum, E., Chun, Y. E., & Hwang, S. W. (2025). The effectiveness of AI-based personalized adaptive learning: Focusing on adult nursing. Journal of internet of things and convergence, 11(3), 19–28. http://doi.org/10.20465/KIOTS.2025.11.3.019
- [24] Arian, M., Kamali, A., Dalir, Z., Hajiabadi, F., & Mazloum, S. R. (2025). Identifying predictors of nursing dropout and attrition before and after Bachelor’s Graduation based on the IPOD model: A machine learning approach. Nurse education in practice, 104580. https://doi.org/10.1016/j.nepr.2025.104580
- [25] Kurt, E., Nazik, E., & Zaybak, A. (2025). The effect of three different simulation methods used in urinary catheterization training. Clinical simulation in nursing, 102, 101729. https://doi.org/10.1016/j.ecns.2025.101729
- [26] Rosalind, S. C. J., Jacinto, I. M. J., Serena, K. O. H. S. L., Magdelene, T. J. Y., & Lydia, L. A. U. S. T. (2026). Usability and feasibility of a Socratic Llm-supported learning tool for clinical reasoning in undergraduate nursing education. Nurse education today, 107092. https://doi.org/10.1016/j.nedt.2026.107092
- [27] O’Connor, S. (2025). Digital nursing skills prioritised by the WHO: A personal reflection. https://www.kcl.ac.uk/digital-nursing-skills-prioritised-by-the-who
- [28] El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025). AI literacy and competency in nursing education: preparing students and faculty members for an AI-enabled future-a systematic review and meta-analysis. Frontiers in medicine, 12, 1681784. https://doi.org/10.3389/fmed.2025.1681784
- [29] Yoon, Y. S., Baek, W., Jo, H., Hong, C., & Ji, Y. (2026). The potential impact of generative AI across Miller’s pyramid of clinical competence: a systematic review. Nurse education in practice, 104775. https://doi.org/10.1016/j.nepr.2026.104775
- [30] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77–101. https://www.tandfonline.com/doi/abs/10.1191/1478088706QP063OA
- [31] Liaw, S. Y., Tan, J. Z., Lim, S., Zhou, W., Yap, J., Ratan, R., … & Chua, W. L. (2023). Artificial intelligence in virtual reality simulation for interprofessional communication training: Mixed method study. Nurse education today, 122, 105718. https://doi.org/10.1016/j.nedt.2023.105718
- [32] Topaz, M., Peltonen, L.-M., Michalowski, M., Stiglic, G., Ronquillo, C., Pruinelli, L., … & Fukahori, H. (2025). The ChatGPT effect: Nursing education and generative artificial intelligence. Journal of nursing education, 64(6), e40--e43. https://doi.org/10.3928/01484834-20240126-01
- [33] O’Connor, S., & Booth, R. G. (2022). Algorithmic bias in health care: Opportunities for nurses to improve equality in the age of artificial intelligence. Nursing outlook, 70(6), 780–782. https://doi.org/10.1016/j.outlook.2022.09.003
- [34] Zhai, X., Wang, Y., Liang, L., Wang, K., Pei, F., & Fu, E. Y. (2025). Personalized e-learning resource recommendation using multimodal-enhanced collaborative filtering. Knowledge-based systems, 319, 113605. https://doi.org/10.1016/j.knosys.2025.113605
- [35] Park, S. A., & Kim, H. Y. (2025). Development and effects of a scenario-based labor nursing simulation education program using an artificial intelligence tutor: A quasi-experimental study. Women’s health nursing, 31(2), 143–154. https://doi.org/10.4069/whn.2025.06.18
- [36] Albloushi, M., Innab, A., Mofdy Almarwani, A., Alqahtani, N., Anazi, M., Roco, I., & Alzahrani, N. S. (2023). The influence of internship year on nursing students’ perceived clinical competence: A multi-site study. Sage open, 13(3), 21582440231193200. https://doi.org/10.1177/21582440231193198
- [37] Alghtany, S., Madhuvu, A., Fooladi, E., & Crawford, K. (2024). Assessment of academic burnout and professional self-concept in undergraduate nursing students: A cross-sectional study. Journal of professional nursing, 52, 7–14. https://doi.org/10.1016/j.profnurs.2024.03.003
- [38] Sengul, T., Sariköse, S., & Gul, A. (2025). Ethical decision-making and artificial intelligence in nursing education: An integrative review. Nursing ethics, 32(8), 2490–2515. https://doi.org/10.1177/09697330251366600
- [39] Huggins-Manley, A. C., Booth, B. M., & D’mello, S. K. (2022). Toward argument-based fairness with an application to AI-enhanced educational assessments. Journal of educational measurement, 59(3), 362–388. https://doi.org/10.1111/jedm.12334
- [40] Tomlinson, E., Schoch, M., & McDonall, J. (2025). A curriculum framework for embedding artificial intelligence literacies in pre-registration nursing education. Nurse education today, 158, 106928. https://doi.org/10.1016/j.nedt.2025.106928
- [41] Hemmer, P., Schemmer, M., Riefle, L., Rosellen, N., Vössing, M., & Kühl, N. (2022). Factors that influence the adoption of human-AI collaboration in clinical decision-making. https://arxiv.org/abs/2204.09082