Integrating Artificial Intelligence Technologies into Health Workforce Education: A Scoping Review of Digital Health Tools in Nursing Curricula
DOI:
https://doi.org/10.58418/ijni.v5i1.186Keywords:
Artificial Intelligence, Nursing Education, Digital Health, Health Workforce, Scoping Review, Patient SafetyAbstract
The rapid expansion of digital health demands a transformation in health workforce education, yet the mapping of Artificial Intelligence (AI) modalities and their structural integration in nursing curricula remains fragmented. To address this gap, this scoping review aimed to systematically map global AI applications in nursing education from 2020 to 2026, offering a distinct contribution by synthesizing pedagogical innovations and structural implementation barriers to guide future curriculum design. Guided by the PRISMA-ScR framework, a systematic screening was conducted across Scopus, PubMed, and CINAHL databases. Results mapped five core AI technologies, including intelligent tutoring systems, virtual patient simulations, adaptive platforms, natural language processing, and predictive analytics, which significantly enhance students' clinical reasoning, critical thinking, and professional competence without compromising patient safety. However, global adoption is geographically skewed and heavily hindered by deficient technological infrastructure, high financial costs, ethical data privacy issues, and a pronounced gap in faculty digital readiness. This study concludes that successful AI integration must shift from ad-hoc usage toward structured, policy-driven curricular frameworks. Ultimately, this review provides a critical strategic benchmark for educational administrators and policy makers to standardize digital health competencies, mitigate regional educational disparities, and safely future-proof the next generation of the healthcare workforce.
References
Abawaji, M. A., Cardwell, R., & McKenna, L. (2024). Missed nursing care among nursing students: A scoping review. Nurse Education Today, 137, 106169. https://doi.org/10.1016/j.nedt.2024.106169
Abou Hashish, E. A. (2025). Compassion through technology: Digital empathy concept analysis and implications in nursing. DIGITAL HEALTH, 11. https://doi.org/10.1177/20552076251326221
Alharbi, A., Nurfianti, A., Mullen, R. F., McClure, J. D., & Miller, W. H. (2024). The effectiveness of simulation-based learning (SBL) on students’ knowledge and skills in nursing programs: a systematic review. BMC Medical Education, 24(1), 1099. https://doi.org/10.1186/s12909-024-06080-z
Alkhudiry, R. (2022). The Contribution of Vygotsky’s Sociocultural Theory in Mediating L2 Knowledge Co-Construction. Theory and Practice in Language Studies, 12(10), 2117–2123. https://doi.org/10.17507/tpls.1210.19
Alrazeeni, D. M., Alharrasi, M., Khan Rony, M. K., Biswas, R. K., Tama, I. J., Halder, C. R., Deb, B., Bashar, F., & Akter, F. (2026). Transforming Nursing Education with Artificial Intelligence: A Systematic Review (2010–2025). Sage Open Nursing, 12, 23779608261424596. https://doi.org/10.1177/23779608261424597
Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616
Azizah, Z., Ohyama, T., Zhao, X., Ohkawa, Y., & Mitsuishi, T. (2024). Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data. Computers and Education: Artificial Intelligence, 7, 100261. https://doi.org/10.1016/j.caeai.2024.100261
Banta-Wright, S. A., Wright, B. M., Taha, A. A., & Miehl, N. (2024). Branching Path Simulation for Pediatric Nurse Practitioner Students to Promote Critical Thinking: A Quasi-Experimental Study. Journal of Pediatric Health Care, 38(5), 737–746. https://doi.org/10.1016/j.pedhc.2024.03.004
Booth, R. G., Strudwick, G., McBride, S., O’Connor, S., & Solano López, A. L. (2021). How the nursing profession should adapt for a digital future. BMJ, n1190. https://doi.org/10.1136/bmj.n1190
Bresolin, P., Steindal, S. A., Bingen, H. M., Zlamal, J., Gue Martini, J., Petersen, E. K., & Nes, A. A. G. (2024). Technology-Supported Guidance Models to Stimulate Nursing Students’ Self-Efficacy in Clinical Practice: Scoping Review. JMIR Nursing, 7, e54443. https://doi.org/10.2196/54443
Brett, J., Davey, Z., Wood, C., Dawson, P., Papiez, K., Kelly, D., Watts, T., Rafferty, A. M., Henshall, C., Watson, E., Butcher, D., Bekaert, S., Ramluggun, P., Aveyard, H., Merriman, C., Waite, M., Strumidlo, L., Ramsay, M., Serrant, L., … Malone, M. (2024). Impact of nurse education prior to and during COVID-19 on nursing students’ preparedness for clinical placement: A qualitative study. International Journal of Nursing Studies Advances, 7, 100260. https://doi.org/10.1016/j.ijnsa.2024.100260
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review. JMIR Nursing, 4(1), e23933. https://doi.org/10.2196/23933
Cho, M.-K., & Kim, M. Y. (2024). Enhancing nursing competency through virtual reality simulation among nursing students: a systematic review and meta-analysis. Frontiers in Medicine, 11, 1351300. https://doi.org/10.3389/fmed.2024.1351300
Creaghe, N., & Kidd, E. (2022). Symbolic play as a zone of proximal development: An analysis of informational exchange. Social Development, 31(4), 1138–1156. https://doi.org/10.1111/sode.12592
De Mattei, L., Morato, M. Q., Sidhu, V., Gautam, N., Mendonca, C. T., Tsai, A., Hammer, M., Creighton-Wong, L., & Azzam, A. (2024). Are Artificial Intelligence Virtual Simulated Patients (AI-VSP) a Valid Teaching Modality for Health Professional Students? Clinical Simulation in Nursing, 92, 101536. https://doi.org/10.1016/j.ecns.2024.101536
Ferguson, C., van den Broek, E. L., & van Oostendorp, H. (2022). AI-Induced guidance: Preserving the optimal Zone of Proximal Development. Computers and Education: Artificial Intelligence, 3, 100089. https://doi.org/10.1016/j.caeai.2022.100089
Fletcher, R. R., Nakeshimana, A., & Olubeko, O. (2021). Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Frontiers in Artificial Intelligence, 3, 561802. https://doi.org/10.3389/frai.2020.561802
Foronda, C. L., Fernandez-Burgos, M., Nadeau, C., Kelley, C. N., & Henry, M. N. (2020). Virtual Simulation in Nursing Education: A Systematic Review Spanning 1996 to 2018. Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare, 15(1), 46–54. https://doi.org/10.1097/SIH.0000000000000411
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024). The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective. Life, 14(6), 652. https://doi.org/10.3390/life14060652
Göçer, H., Durukan, A. B., & Özyüksel, A. (2026). Artificial Intelligence in Medical Education: Curriculum Design, Assessment Models, and Educational Infrastructure Across Undergraduate and Residency Training – A Narrative Review. Turk Kardiyoloji Dernegi Arsivi-Archives of the Turkish Society of Cardiology, 54(5), 382–387. https://doi.org/10.5543/tkda.2026.40172
Görücü, S., Türk, G., & Karaçam, Z. (2024). The effect of simulation-based learning on nursing students’ clinical decision-making skills: Systematic review and meta-analysis. Nurse Education Today, 140, 106270. https://doi.org/10.1016/j.nedt.2024.106270
Harrington, J., Booth, R. G., & Jackson, K. T. (2025). Large Language Models in Nursing Education: Concept Analysis. JMIR Nursing, 8, e77948–e77948. https://doi.org/10.2196/77948
Izquierdo-Condoy, J. S., Arias-Intriago, M., Montero Corrales, L., & Ortiz-Prado, E. (2026). Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications. JMIR Medical Education, 12, e77127–e77127. https://doi.org/10.2196/77127
Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-maghaireh, D. (2024). Artificial intelligence tools utilized in nursing education: Incidence and associated factors. Nurse Education Today, 142, 106355. https://doi.org/10.1016/j.nedt.2024.106355
Jarva, E., Oikarinen, A., Andersson, J., Pramila‐Savukoski, S., Hammarén, M., & Mikkonen, K. (2024). Healthcare professionals’ digital health competence profiles and associated factors: A cross‐sectional study. Journal of Advanced Nursing, 80(8), 3236–3252. https://doi.org/10.1111/jan.16096
Jensen, N., Kelly, A. H., & Avendano, M. (2022). Health equity and health system strengthening – Time for a WHO re-think. Global Public Health, 17(3), 377–390. https://doi.org/10.1080/17441692.2020.1867881
Kamel Rahimi, A., Pienaar, O., Ghadimi, M., Canfell, O. J., Pole, J. D., Shrapnel, S., van der Vegt, A. H., & Sullivan, C. (2024). Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers. Journal of Medical Internet Research, 26, e49655. https://doi.org/10.2196/49655
Kleib, M., Arnaert, A., Nagle, L. M., Ali, S., Idrees, S., Kennedy, M., & da Costa, D. (2023). Digital health education and training for undergraduate and graduate nursing students: a scoping review protocol. JBI Evidence Synthesis, 21(7), 1469–1476. https://doi.org/10.11124/JBIES-22-00266
Kleinheksel, A. J., Chen, W., Rudd, M. J., Drowos, J., Gupta, S., Minor, S., & Bailey, J. M. (2023). Putting reflection back into practice: Kolb’s theory of experiential learning as a theoretical framework for just-in-time faculty development. SN Social Sciences, 3(3), 59. https://doi.org/10.1007/s43545-023-00649-z
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198
Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69. https://doi.org/10.1186/1748-5908-5-69
Levett‐Jones, T., & Cant, R. (2020). The empathy continuum: An evidenced‐based teaching model derived from an integrative review of contemporary nursing literature. Journal of Clinical Nursing, 29(7–8), 1026–1040. https://doi.org/10.1111/jocn.15137
Li, K. C., Wong, B. T. M., & Liu, M. (2024). A survey on predicting at-risk students through learning analytics. International Journal of Innovation and Learning, 36(5), 1–15. https://doi.org/10.1504/IJIL.2024.140170
Lifshits, I., & Rosenberg, D. (2024). Artificial intelligence in nursing education: A scoping review. Nurse Education in Practice, 80, 104148. https://doi.org/10.1016/j.nepr.2024.104148
Liu, M., Kong, A., Huang, F., Lau, N., & Hoorn, J. F. (2025). Exploring technology-assisted mindfulness: a systematic progressive review on the role of virtual reality. Virtual Reality, 29(3), 143. https://doi.org/10.1007/s10055-025-01224-y
Livesay, K., Walter, R., Petersen, S., Abdolkhani, R., Zhao, L., & Butler-Henderson, K. (2024). Challenges and Needs in Digital Health Practice and Nursing Education Curricula: Gap Analysis Study. JMIR Medical Education, 10, e54105–e54105. https://doi.org/10.2196/54105
Longhini, J., Rossettini, G., & Palese, A. (2024). Digital health competencies and affecting factors among healthcare professionals: additional findings from a systematic review. Journal of Research in Nursing, 29(2), 156–176. https://doi.org/10.1177/17449871241226899
Lucas, H. C., Upperman, J. S., & Robinson, J. R. (2024). A systematic review of large language models and their implications in medical education. Medical Education, 58(11), 1276–1285. https://doi.org/10.1111/medu.15402
Ma, H., Niu, A., Tan, J., Wang, J., & Luo, Y. (2024). Nursing students’ perception of digital technology in clinical education among undergraduate programs: A qualitative systematic review. Journal of Professional Nursing, 53, 49–56. https://doi.org/10.1016/j.profnurs.2024.04.008
Mainz, A., Nitsche, J., Weirauch, V., & Meister, S. (2024). Measuring the Digital Competence of Health Professionals: Scoping Review. JMIR Medical Education, 10, e55737. https://doi.org/10.2196/55737
Makhlouf, E., Alenezi, A., & Shokr, E. A. (2024). Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design. Nurse Education Today, 137, 106159. https://doi.org/10.1016/j.nedt.2024.106159
Mariano, B. (2020). Towards a global strategy on digital health. Bulletin of the World Health Organization, 98(4), 231-231A. https://doi.org/10.2471/BLT.20.253955
McGowan, J., Straus, S., Moher, D., Langlois, E. V., O’Brien, K. K., Horsley, T., Aldcroft, A., Zarin, W., Garitty, C. M., Hempel, S., Lillie, E., Tunçalp, Ӧzge, & Tricco, A. C. (2020). Reporting scoping reviews—PRISMA ScR extension. Journal of Clinical Epidemiology, 123, 177–179. https://doi.org/10.1016/j.jclinepi.2020.03.016
Medel, D., Bonet, A., Jimenez Herrera, M., Sevilla, F., Vilaplana, J., Cemeli, T., & Roca, J. (2025). Interactive Virtual Simulation Case: A Learning Environment for the Development of Decision-Making in Nursing Students. Teaching and Learning in Nursing, 20(1), e60–e68. https://doi.org/10.1016/j.teln.2024.08.002
Miles, J. M., & Lee, M. A. (2024). Effects of a Mobile App on Nursing Students’ Clinical Reasoning and Decision-Making. Journal of Nursing Education, 63(12), 835–843. https://doi.org/10.3928/01484834-20240726-02
Morris, T. H. (2020). Experiential learning – a systematic review and revision of Kolb’s model. Interactive Learning Environments, 28(8), 1064–1077. https://doi.org/10.1080/10494820.2019.1570279
Motsaanaka, M. N., Makhene, A., & Ndawo, G. (2024). Technology-based approaches to enhance clinical learning opportunities for student nurses in a nursing education institution in Gauteng. International Journal of Africa Nursing Sciences, 21, 100790. https://doi.org/10.1016/j.ijans.2024.100790
Nasarudin, N. A., Al Jasmi, F., Sinnott, R. O., Zaki, N., Al Ashwal, H., Mohamed, E. A., & Mohamad, M. S. (2024). A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction. Artificial Intelligence Review, 57(9), 249. https://doi.org/10.1007/s10462-024-10876-2
Nugent, L., Murray, B., Moore, Z., Patton, D., O’Connor, T., Watson, C., Walsh, K., Renjith, V., & George, J. (2026). Teaching with intelligence; AI-enhanced pedagogies in nursing education A scoping review. Nurse Education in Practice, 94, 104878. https://doi.org/10.1016/j.nepr.2026.104878
O’Connor, S. (2021). Artificial intelligence and predictive analytics in nursing education. Nurse Education in Practice, 56, 103224. https://doi.org/10.1016/j.nepr.2021.103224
Oshodi, T. O., & Sookhoo, D. (2025). Nursing students’ perceptions of inadequate nurse staffing in the clinical learning environment – a systematic narrative review. Nurse Education in Practice, 82, 104221. https://doi.org/10.1016/j.nepr.2024.104221
Ouanes, K., & Farhah, N. (2024). Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery. Journal of Medical Systems, 48(1), 74. https://doi.org/10.1007/s10916-024-02098-4
Padilha, J. M., Costa, P., Sousa, P., & Ferreira, A. (2025). The integration of virtual patients into nursing education. Simulation & Gaming, 56(2), 178–191. https://doi.org/10.1177/10468781241300237
Park, S., Shin, H. J., Kwak, H., & Lee, H. J. (2024). Effects of Immersive Technology–Based Education for Undergraduate Nursing Students: Systematic Review and Meta-Analysis Using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) Approach. Journal of Medical Internet Research, 26, e57566. https://doi.org/10.2196/57566
Peters, M. D., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., & Khalil, H. (2024). Scoping reviews. In JBI Manual for Evidence Synthesis. JBI. https://doi.org/10.46658/JBIMES-24-09
Rahman, M. I. (2026). Algorithmic bias and transparency in artificial intelligence tools for nursing education: A scoping review. Nurse Education in Practice, 92, 104756. https://doi.org/10.1016/j.nepr.2026.104756
Remorosa, M. M. R., Capili, S. R., Decir, E. G. B., Delacruz, J. B., Balase, M. M. H., & Escarlos, G. S. (2024). Vygotsky’s social development theory: The role of social interaction and language in cognitive development. International Journal of All Research Writings, 6(6), 10–13.
Robertson, S. T., Hoffman, J., Sullivan, C., Donovan, R., & Woods, L. (2026). Measuring digital health competence among healthcare professionals: A rapid review of assessment tools. International Journal of Medical Informatics, 214, 106434. https://doi.org/10.1016/j.ijmedinf.2026.106434
Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855
Rony, M. K. K., Ahmad, S., Tanha, S. M., Das, D. C., Akter, M. R., Khatun, M. A., Begum, M. H., Khalil, M. I., Peu, U. R., Parvin, M. R., Alrazeeni, D. M., & Akter, F. (2025). Nursing Educators’ Perspectives on the Integration of Artificial Intelligence Into Academic Settings. SAGE Open Nursing, 11. https://doi.org/10.1177/23779608251342931
Roveta, A., Castello, L. M., Massarino, C., Francese, A., Ugo, F., & Maconi, A. (2025). Artificial Intelligence in Medical Education: A Narrative Review on Implementation, Evaluation, and Methodological Challenges. AI, 6(9), 227. https://doi.org/10.3390/ai6090227
Sahin Karaduman, G., & Basak, T. (2024). Virtual Patient Simulations in Nursing Education: A Descriptive Systematic Review. Simulation & Gaming, 55(2), 159–179. https://doi.org/10.1177/10468781231224836
Schlicht, L., Wendsche, J., Melzer, M., Tschetsche, L., & Rösler, U. (2025). Digital technologies in nursing: An umbrella review. International Journal of Nursing Studies, 161, 104950. https://doi.org/10.1016/j.ijnurstu.2024.104950
Siddique, S. M., Tipton, K., Leas, B., Jepson, C., Aysola, J., Cohen, J. B., Flores, E., Harhay, M. O., Schmidt, H., Weissman, G. E., Fricke, J., Treadwell, J. R., & Mull, N. K. (2024). The Impact of Health Care Algorithms on Racial and Ethnic Disparities. Annals of Internal Medicine, 177(4), 484–496. https://doi.org/10.7326/M23-2960
Sim, J. J. M., Rusli, K. D. Bin, Seah, B., Levett-Jones, T., Lau, Y., & Liaw, S. Y. (2022). Virtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis. Clinical Simulation in Nursing, 69, 26–39. https://doi.org/10.1016/j.ecns.2022.05.006
Srinivasan, M., Venugopal, A., Venkatesan, L., & Kumar, R. (2024). Navigating the Pedagogical Landscape: Exploring the Implications of AI and Chatbots in Nursing Education. JMIR Nursing, 7, e52105. https://doi.org/10.2196/52105
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16. https://doi.org/10.1007/s11423-019-09701-3
Tischendorf, T., Heitmann-Möller, A., Ruppert, S.-N., Marchwacka, M., Schaffrin, S., Schaal, T., & Hasseler, M. (2024). Sustainable integration of digitalisation in nursing education—an international scoping review. Frontiers in Health Services, 4, 1344021. https://doi.org/10.3389/frhs.2024.1344021
Tricco, A. C., Robinson, E., Straus, S. E., Colquhoun, H., Godfrey, C. M., Moher, D., Munn, Z., Pollock, D., Logan, S., Elsman, E. B. M., Hasanoff, S., Lai, Y., McMahon, J., Dourka, J., Neupane, D., Khalil, H., Peters, M. D. J., Alexander, L., Jia, R. M., … Veroniki, A. A. (2026). Updating the PRISMA reporting guideline for scoping reviews: a scoping review. Journal of Clinical Epidemiology, 196, 112314. https://doi.org/10.1016/j.jclinepi.2026.112314
Twabu, K. (2025). Enhancing the cognitive load theory and multimedia learning framework with AI insight. Discover Education, 4(1), 160. https://doi.org/10.1007/s44217-025-00592-6
Valizadeh, A., Moassefi, M., Nakhostin-Ansari, A., Hosseini Asl, S. H., Saghab Torbati, M., Aghajani, R., Maleki Ghorbani, Z., & Faghani, S. (2022). Abstract screening using the automated tool Rayyan: results of effectiveness in three diagnostic test accuracy systematic reviews. BMC Medical Research Methodology, 22(1), 160. https://doi.org/10.1186/s12874-022-01631-8
World Health Organization. (2025). Global strategy on digital health 2020-2027. World Health Organization.
Yang, Y. (2024). Influences of Digital Literacy and Moral Sensitivity on Artificial Intelligence Ethics Awareness Among Nursing Students. Healthcare, 12(21), 2172. https://doi.org/10.3390/healthcare12212172
Zhao, C., Zhu, J., Liu, J., Zhao, W., & Pang, Y. (2026). Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial. BMC Medical Education, 26(1), 1026. https://doi.org/10.1186/s12909-026-09469-0
Zhao, L., Abdolkhani, R., Walter, R., Petersen, S., Butler‐Henderson, K., & Livesay, K. (2024). National survey on understanding nursing academics’ perspectives on digital health education. Journal of Advanced Nursing, 80(12), 4888–4899. https://doi.org/10.1111/jan.16163
Zhao, L., Yan, H., Liu, L., Zhou, R., Yang, Y., Li, K., Wan, F., & Li, Y. (2026). Effect of desktop virtual patient simulation on clinical reasoning skills of nursing students: A systematic review and meta-analysis. Nurse Education in Practice, 93, 104822. https://doi.org/10.1016/j.nepr.2026.104822
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