Keeping People Central in AI-Driven Nursing Education and Practice
Author: Prof Naoko Nishimura1,2
1. Otemae University
2. JBI Otemae University Implementation centre
AI is transforming nursing education by offering powerful simulation tools that enhance clinical learning – yet these advances must remain firmly human-centred, ensuring technology strengthens, rather than replaces, compassionate, ethical patient care.
1. Educational Challenges in Contemporary Nursing
In recent years, nursing education has faced structural challenges, such as decreasing clinical practicum hours, limited opportunities to perform procedures in real clinical settings, and increasing restrictions due to patient safety and institutional constraints. Meanwhile, healthcare itself is becoming more complex. Patients present with multifaceted medical and social needs, requiring nurses to demonstrate advanced clinical judgement, communication, and holistic care competencies. This creates a paradox: clinical learning opportunities are decreasing, while required competencies are increasing.
Additionally, students experience high levels of psychological pressure during clinical practice. Unfamiliar environments, relationships with patients and clinical educators, and limited experience often lead to anxiety and memorable experiences of failure. Traditional in-school simulations and role play may not fully replicate the complexity and cognitive demands of real clinical situations. These pressures prompted our team to seek new ways of supporting student learning before clinical exposure.
In Japan, the digital transformation (DX) of healthcare has accelerated rapidly. A national policy direction of the Japanese Ministry of Health, Labour and Welfare emphasises the need to cultivate healthcare professionals who can effectively respond to medical DX, including within basic nursing education.
At the same time, educational innovations are emerging globally. AI-driven simulation technologies, such as virtual patient systems and AI-supported feedback tools, are increasingly being integrated into nursing curricula. These technologies enable students to engage with realistic clinical scenarios and repeatable environments, improving engagement and clinical reasoning skills.
It was within this context that we turned our attention to AI-based simulation, particularly the Virtual Nurse Lab (VNL) developed at Chiang Mai University, which demonstrates how AI can provide interactive clinical training and immediate, objective feedback to learners in simulated environments. The project team at Otemae University has also begun implementing AI-based scenario simulations in nursing education, in collaboration with the faculty at Chiang Mai University.
2. What We Hoped the VNL Would Deliver
Our collaboration with Chiang Mai University, built through years of student exchange and academic partnership, provided a unique opportunity to explore the use of AI in simulation learning. During a special lecture delivered by Dr Piyanut, we were introduced to the VNL and immediately recognised its potential to address our shared challenge: preparing students more effectively for clinical practice.
We were particularly hopeful that the VNL could provide safe, repeatable, and standardised learning opportunities, improve clinical decision-making, enhance realism and interactivity in clinical scenarios, and deliver immediate, personalised feedback.
Such systems allow students to practice clinical judgement without risking patient safety and may alleviate anxiety associated with real-world practice. At that time, Chiang Mai University had not implemented the VNL using paediatric scenarios. Yet it is difficult to gain sufficient paediatric nursing practice through university-based training alone, and to address that issue, our collaboration enabled us to expand the range of VNL cases. This highlighted the mutual benefit of working together. Our joint project emerged from previous exchanges—both in-person visits and face-to-face discussions—combined with the use of AI technology. This experience reinforced the importance of building relationships where partners can ‘see each other’ and develop trust over time.
3. Learning Through Collaboration: Benefits and Tensions
Through our collaboration, we discovered important differences that shaped our implementation.
One key discussion centred on scenario development.
We debated whether to adapt Chiang Mai’s existing scenarios or fully redesign them to reflect Japan’s disease patterns and clinical practices. This was not a simple technical decision. Ultimately, we recognised that meaningful learning requires strong contextual relevance. This led us to redesign elements of the scenarios to better align with the Japanese healthcare context.
Another critical difference lay in clinical training approaches.
While Chiang Mai University places greater emphasis on hands-on technical skills, Japanese nursing education often limits such opportunities in clinical settings. As a result, our focus tends to be on patient assessment, individualised care planning, and patient education.
These differences influenced how we structured our VNL scenarios and learning objectives. After considerable trial and error, we decided that our scenarios should include questions that allow AI to easily evaluate student understanding and that remain adaptable even if details such as patient characteristics or diseases are modified.
4. Aligning AI Learning with National Standards
A particularly important decision was how to design assessments within the VNL environment.
We chose to incorporate multiple-choice questions (MCQs) aligned with the Japanese National Nursing Examination, ensuring that AI-supported learning would directly reinforce competencies required for licensure. Although the MCQs based on Otemae University’s scenarios were generated by the Chiang Mai University team using AI, we learned that human refinement and adjustment were essential to effectively support student learning and comprehension. This finding highlights a crucial point: AI can assist educators, but it cannot fully replace the clinical judgement required to design effective learning experiences. Global guidance emphasises that AI must not replace human judgement. Instead, it must support it. Moreover, respect for patient autonomy, transparency, and responsibility are essential.
5. What We Learned About AI in Nursing Education
Our experience with the VNL reinforced both the potential and the limitations of AI.
AI-enabled simulation can indeed expand access to practice opportunities, enhance student engagement, and provide structured feedback in ways that traditional teaching cannot.
Yet, without careful integration, the expansion of AI in education introduces important risks.
6. Risks: Losing the Human Centre
- Overreliance on Simulation: There is a concern that increased use of AI simulation could lead educators to reduce real clinical practice opportunities
- Misconceptions about Care: Because simulations can be repeated, students may develop the mistaken belief that real patient care is similarly reversible, which can undermine their sense of responsibility
- Dehumanisation of Care: Training focused heavily on AI systems risks becoming detached from real human interaction. There is a danger that education intended to prepare students for caring for people may become training that overlooks the human person
7. Ensuring a Human-Centred Approach
To ensure that people remain at the centre of AI development and use in nursing education, several strategies are essential:
1. Clarifying the Purpose of AI Simulation
Educators must clearly define that AI simulation is a supplement, not a replacement, for real clinical experience
2. Explicitly Differentiating Simulation from Reality
Students should be guided to understand the differences between simulated scenarios and real patient care, including ethical responsibility, unpredictability, and emotional complexity
3. Integrating AI Learning with Human-Centred Practice
AI-based learning should be combined with reflective exercises, communication training, and real patient interaction to ensure holistic development
4. Promoting Critical Reflection
Students must critically evaluate how AI supports clinical reasoning, where human judgement is indispensable, and what aspects of care cannot be replicated by AI
5. Embedding Ethical and Humanistic Competencies
Education must reinforce respect for human dignity, empathy and relational care, and accountability in decision-making
8. Recommendations for Educational Practice
Based on these principles, in-school simulation programs should be designed as follows:
- Not only to develop technical and cognitive skills but also to prepare students for human-centred care in real clinical settings
- After AI simulation training, educators should facilitate discussions, such as the following:
- How can clinical skills acquired through AI simulation be adapted to real patients?
- What additional competencies are required to provide individualised care?
- How should nurses respond to patients’ emotions, values, and lived experiences?
Such reflective dialogue is essential to bridge the gap between simulation and real-world practice
9. Conclusion: Moving Forward Through Global Collaboration
This project demonstrated that combining AI technology with international collaboration can significantly enrich nursing education. The process of negotiating differences – whether in curricula, clinical practice, or assessment – was not a barrier, but a powerful learning opportunity.
At the same time, our most important lesson is clear:
The effectiveness of AI in education depends not on the technology itself, but on how thoughtfully it is integrated by educators.
As healthcare continues to evolve globally, partnerships like ours—and tools like the VNL—will play an increasingly important role in preparing nurses who are not only clinically competent but also deeply grounded in the human values at the core of care.
References
Anthamatten, A., & Holt, J. (2024). Integrating artificial intelligence into virtual simulations to develop entrustable professional activities. The Journal for Nurse Practitioners, 20(9), 105192. https://doi.org/10.1016/j.nurpra.2024.105192
El Arab, R. A., Al Moosa, O. A., Sagbakken, M., Ghannam, A., Abuadas, F. H., Somerville, J., & Al Mutair, A. (2025). Integrative review of AI applications in nursing. Frontiers in Public Health, 13, 1619378. https://doi.org/10.3389/fpubh.2025.1619378
Hasan, S., Ahmed, A., & Ismail, F. W. (2026). The evolving role of AI in simulation-based medical education: A narrative review. Advances in Medical Education and Practice, 17, 581691. https://doi.org/10.2147/AMEP.S581691
Japanese Ministry of Health, Labour and Welfare (2025). Nursing DX handbook. https://www.mhlw.go.jp/kango_kyouiku/education/index.html
Jo, Y.-W., Lee, M., & Yang, H.-J. (2025). Large language model-based virtual patient simulations in medical and nursing education: A review. Applied Sciences, 15(22), 11917. https://doi.org/10.3390/app152211917
Jung S. (2023). Challenges for future directions for artificial intelligence integrated nursing simulation education. Korean Journal of Women Health Nursing, 29(3), 239–242. https://doi.org/10.4069/kjwhn.2023.09.06.1
World Health Organization. (2025). Ethics and governance of artificial intelligence for health. https://www.who.int/publications/i/item/978924002920
Xuto, P., Prasitwattanaseree, P., Chaiboonruang, T., Nimarangkul, K., & Khiaokham, L. (2024). Enhancing clinical performance self-efficacy among nursing students: A virtual clinical laboratory approach. Teaching and Learning in Nursing, 19(4), e667–e671. https://doi.org/10.1016/j.teln.2024.06.002
To link to this article - DOI: https://doi.org/10.70253/NAFU3074
Disclaimer
The views expressed in this World EBHC Day Blog, as well as any errors or omissions, are the sole responsibility of the author and do not represent the views of the World EBHC Day Steering Committee, Official Partners or Sponsors; nor does it imply endorsement by the aforementioned parties.
