Nursing Education

AI and Nursing Education

by Paulina Guerrero

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The impact of AI in Medicine has become substantial, and it can be difficult to know where to start in terms of what research or resources are available. To start with, AI as a term is too unwieldy and large to really capture what it is-it has become a bellwether for so many different aspects of technology, and it seems like a sleeping giant in the mountains that has only recently descended, causing mass havoc and excitement for the last four years. 

But of course, technology history shows something more interesting and complex-Artificial Intelligence within Medicine has existed since the 1950s and really started making impacts in the 1970s, when it began to be used for biomedical research in a problem-solving capacity.  (Kulikowski, 2019) That being said, it had severe limitations, in that it didn’t have enough data points to generate conclusive output because it had to rely on word by word input. Additionally, early AI could only exist through sets of rules. Given the nuance and complexity of medicine, early AI failed in being able to provide any tangible results for diagnostic reasoning and evaluation. 

That all changed of course, in the early 2000s when the world of medicine shifted to digital. What was once cornered off in paper records in separate rooms and buildings, suddenly became widely available to just about anyone. This brought in the age of “Big Data” when medical charts became electronic health records (EHRs), imaging became digital, and wearable health tech became accessible. AI needs millions of data points for input and generation, and since 2000 we’ve been creating buffets of data for a machine that has an insatiable appetite. 

The other major advancement that occurred in the last 20 years was AI’s capacity for Deep Learning and Transformer Architecture. Seemingly quite suddenly, AI could mimic neural networks with many layers like the human brain structure and transformer architecture could create platforms like ChatGPT. These models process contextual relationships in text and data simultaneously, rather than word-by-word. 

So what are the main impacts and changes in healthcare and nursing with integrations of AI? According to Gemini CoPilot, the output states: 

  • Predictive Analytics & Sepsis Detection: A study published in Nature Medicine evaluated AI early-warning systems (like Epic’s Sepsis Model and Johns Hopkins’ TREWS) across multiple hospitals, finding that real-time AI alerts enabled nurses and physicians to initiate treatment nearly two hours earlier, significantly reducing in-hospital mortality.
  • Operational Optimization & Staffing: Peer-reviewed research in healthcare management journals (including Frontiers in Digital Health) shows that AI predictive modeling accurately forecasts patient census and acuity levels, optimizing nurse-to-patient staffing ratios and reducing overtime-induced burnout.
  • Administrative Burden & Virtual Assistants: Empirical studies on ambient AI documentation and conversational triage systems demonstrate a 30% to 50% reduction in manual charting time for nursing staff, freeing up substantial clinical hours for direct bedside care.
  • Diagnostics & Pharmacogenomics: Clinical trials published in journals such as The Lancet Digital Health confirm that AI-driven decision-support tools match or exceed traditional triage accuracy, helping bedside nurses flag high-risk cardiac or oncological complications faster.

Some of these changes are truly exciting, while others are deeply troubling. Recent conversations in the AI world show growing concern over AI’s agentive capability, security concerns, and capacity to create biochemical weapons. As the quite terrifying recent article “What if Automating AI R&D triggers an intelligence explosion?” signed by many of AI tech’s foremost leaders states convincingly, it is still very difficult to create vaccines, while viruses can replicate themselves with no human assistance. AI is now capable of creating both. (Chan et al., n.d.)

More resonant for nursing education, there is burgeoning research gauging nursing students’ experiences with AI, AI Literacy, and readiness for AI in medicine. Findings indicate that AI perceptions and knowledge among nursing students varied depending on resources and highly fluctuated among different groups and demographics. What seems to be a common theme though, is nursing students’ rising anxiety and feelings around a lack of preparedness with AI technology in medicine and clinical settings. (Karimtabar & Alee, n.d.; LaGore et al., 2025; Younis et al., n.d.)

While tackling the larger question of AI seems overwhelming, there are possibilities for addressing AI and Medicine in various teaching curriculums across multiple subject areas. For example, the excitement and apprehension around Ambient AI (a silent AI recording during medical visits that transcribes all notes and creates charting instantly) has some supporters seeing it as an opportunity for improving healthcare; clinicians can strictly pay attention to the patient, maintain eye contact, and cut down on administrative charting time. At the same time, ambient AI raises significant questions around privacy, data breach, surveillance, and patient and clinician trust. (Bracken et al., 2026; Lawrence et al., 2025; Martinez-Martin et al., 2021)

If you’re interested in learning more, one place to consider is The Nursing and Artificial Intelligence Leadership Collective which features various articles from its members. Articles such as “ITADEL: An automated audit of citation integrity across 2.5 million biomedical papers uncovers 4,046 fabricated citations — growing more than 12× in three years” and “An AI-Enabled Nursing Future with No Documentation Burden: A Vision for a New Reality” shows a range of attitudes and perspectives on these overarching questions. 

Happy reading! May Rodney the Ram be beloved as both your favorite 3d sports mascot, and as your favorite digital avatar. 

References: 

Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., Chen, E. S., Saheed, M. O., Henley, M., Miranda, S., Houston, K., Linton, R. C., Ahluwalia, A. R., Wu, A. W., & Saria, S. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455–1460. https://doi.org/10.1038/s41591-022-01894-0

Alamoudi, A., Kontopantelis, E., Zghebi, S. S., & Brown, B. (2026). AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence. Journal of Medical Internet Research, 28, e88396. https://doi.org/10.2196/88396

Bracken, A., Babu, A. R., Whelehan, S., Merghani, K., Sheehan, E., & Feeley, I. (2026). Ambient AI reduces documentation time and enhances quality in a simulated inpatient setting. The Surgeon: Journal of the Royal Colleges of Surgeons of Edinburgh and Ireland, 24(2), 119–125. https://doi.org/10.1016/j.surge.2025.10.008

Chan, A., Winter, C., Barto, A., Pachocki, J., Hinton, G., Horvitz, E., Bengio, Y., Song, D., Clark, J., Greaves, H., Korinek, A., & Manning, S. (2026). What if automating AI R&D triggers an intelligence explosion? arXiv. https://doi.org/10.48550/arXiv.2609.36054 

Frontiers | Artificial intelligence in nursing: An integrative review of clinical and operational impacts. (n.d.). Retrieved October 1, 2026, from https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1552372/full

Johns Hopkins Engineering for Professionals. (2025, September 1). AI in healthcare: Applications and impact. https://ep.jhu.edu/news/ai-in-healthcare-applications-and-impact/ 

Karimtabar, H., Alee, M., & Oghli, S. H. (2026). Technology anxiety and artificial intelligence readiness in nursing students: A cross-sectional study. BMC Nursing. Advance online publication. https://doi.10.1186/s12912-026-05053-5

Kulikowski, C. A. (2019). Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art – with Reflections on Present AIM Challenges. Yearbook of Medical Informatics, 28(1), 249–256. https://doi.org/10.1055/s-0039-1677895

Kumah, E., Antwi, J., Boakye, D. S., Botchwey, C. O.-A., & Anyimadu, E. (2026). The ethics of listening walls: Patient autonomy and consent in the age of ambient clinical AI. Npj Digital Medicine, 9(1), 506. https://doi.org/10.1038/s41746-026-02973-z

LaGore, A., Colby, L., Sinsabaugh, K., Grobbel, C., & Piscotty, R. (2025). The Effect of AI Education on Nursing Student AI Literacy and Anxiety. The Journal of Nursing Education, 64(9), 587–590. https://doi.org/10.3928/01484834-20250415-04

Lawrence, K., Kuram, V. S., Levine, D. L., Sharif, S., Polet, C., Malhotra, K., & Owens, K. (2025). Informed Consent for Ambient Documentation Using Generative AI in Ambulatory Care. JAMA Network Open, 8(7), e2522400. https://doi.org/10.1001/jamanetworkopen.2025.22400

Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S. S., Wieten, S., Cho, M. K., Magnus, D., Fei-Fei, L., Schulman, K., & Milstein, A. (2021). Ethical issues in using ambient intelligence in health-care settings. The Lancet. Digital Health, 3(2), e115–e123. https://doi.org/10.1016/S2589-7500(20)30275-2

Younis, M. B., Ayed, A., Alqudah, H. N., Alkhalaileh, M., Habahbeh, A., Khaled, M. H. B., & Abu-Shennar, J. A. (n.d.). Preparedness and Attitudes of Nursing Students toward the Integration of Artificial Intelligence in Nursing Education. https://doi.org/10.2174/0118744346487793260629120139

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