by Geneveave Barbo, RN, MN, MClSc, Ph(c)
Doctoral Student, College of Nursing, University of Saskatchewan;
Corresponding author

Donald Leidl RN, BSN, MN, EdD
Assistant Professor, Faculty of Nursing, University of New Brunswick

Hua Li, RN, PhD
Associate Professor, College of Nursing, University of Saskatchewan

Marjorie Montreuil, RN, PhD
Assistant Professor, Ingram School of Nursing, McGill University

Pammla Petrucka, RN, PhD
Professor, College of Nursing, University of Saskatchewan

Citation: Barbo, G., Leidl, D., Li, H., Montreuil, M., & Petrucka, P.  (2025). Development of immersive virtual reality simulation for migrant mental health nursing education: Lessons learned. Canadian Journal of Nursing Informatics, 20(2). https://cjni.net/journal/?p=14789

Abstract

Immersive virtual reality (IVR) offers a novel and promising approach to nursing education, particularly related to migrant mental health care. This paper details the development of an IVR simulation designed to enhance cultural competency, cultural humility, and therapeutic communication skills among undergraduate nursing students. The development process incorporated an integrative review, the use of Unity and ChatGPT-4o, and a participatory framework to ensure authenticity and relevance. Despite challenges, this process demonstrates how nurses, even without coding experience or extensive financial resources, can utilize emerging technologies to drive innovation in education while maintaining a strong foundation in evidence-based care.

Background

Migrants face significant mental health challenges, often exacerbated by stigma, discrimination, and cultural or linguistic differences (Salami et al., 2019). These challenges frequently delay care, worsening conditions and reducing quality of life (Salami et al., 2019). Nurses play a critical role in addressing these challenges, yet many report inadequate training to meet the unique needs of migrant populations upon graduation (Donlan, 2018; Salami et al., 2019). Immersive virtual reality (IVR) shows great promise in bridging this training gap and transforming mental health nursing education (Ho Yan Lam et al., 2020).

Unlike non-immersive virtual reality (VR), IVR leverages head-mounted displays to fully immerse users in dynamic, interactive environments (Paes et al., 2021). This heightened sense of presence fosters deeper engagement, enabling emotionally resonant learning experiences that better equip nurses to navigate the complexities of migrant mental health care (Paes et al., 2021). While existing research has explored non-immersive VR in nursing education (e.g., Kidd et al., 2012; Liu, 2021; Piot et al., 2022; Sunnqvist et al., 2016), studies on IVR’s role in training nurses to address migrant mental health challenges—particularly in overcoming access barriers—are scarce.

Even more groundbreaking is the emergence of nurse-led IVR content creation, which leverages AI-powered tools rather than traditional software developer partnerships. This shift represents a paradigm change, positioning nurses not only as end-users but as creators of immersive learning experiences tailored to the realities of clinical practice. To the authors’ knowledge, this is the first IVR learning scenario developed entirely by nurses using AI-driven simulation editors, marking a significant advancement in nursing education technology.

This paper outlines the development of an IVR simulation designed to enhance cultural competence, cultural humility, and therapeutic relationship skills among undergraduate nursing students. Serving as the intervention for the lead author’s doctoral research, the simulation’s acceptability and preliminary effects will be evaluated and published separately. The following sections provide an overview of the IVR simulation, detailing the design methodology, the integration of AI-powered content generation (including ChatGPT-4o), and the participatory research approach. By articulating the blueprint for this nurse-led IVR development model, this paper offers a foundation for future researchers and IVR content creators to refine and expand upon.

IVR Simulation Overview

The IVR simulation immerses nursing student participants in two healthcare settings: an urban tertiary hospital (three scenes) and a community health centre (two scenes). The hospital scenes focus on triage and assessment of clinical, cultural, and linguistic factors, crisis management, and outpatient transition with resource mobilization. The community health centre scenes address stigma, discrimination, and collaborative care planning. Throughout the simulation, participants interact with Mrs. S., a Filipina immigrant experiencing severe headaches alongside major depressive and anxiety disorders. Interaction occurs through communication prompts, requiring participants to speak aloud one of the four response options —three promoting cultural competency, cultural humility, or therapeutic partnership, and one deliberately dismissive for contrast. Appropriate responses advance the storyline, while dismissive choices require re-evaluation before proceeding. The simulation progresses intuitively without the need for external support, and participant choices are neither recorded nor evaluated.

Development Process

Foundation and Design

The creation of the IVR simulation began with an integrative review of mental healthcare service delivery for migrants, with a specific focus on anti-stigma and anti-discrimination, and cultural competence strategies. While its detailed methodology and findings will be published elsewhere, the review’s insights profoundly shaped the simulation’s design. Case studies from the literature were adapted into Mrs. S.’s backstory and the review also helped identify core skills—cultural competence, cultural humility, therapeutic alliance, and effective communication—that became the simulation’s primary learning objectives. Additionally, the review guided the translation of these theoretical concepts into actionable communication strategies that participants can practice within the simulation and apply in their nursing practice. From this, the simulation blueprint was developed which details each scene’s sub-objectives, tasks, and contexts. Decision trees were then prepared for each scene using Draw.io to visually map out the dialogue sequence and branching paths based on the participants’ choices. ChatGPT-4o assisted in generating the dialogue scripts, as detailed in Table 1, which also details its other contributions to the simulation’s development.

Table 1

Role of ChatGPT-4o in Simulation Development

Building the Simulation

Table 2 provides a comprehensive overview of the tools and websites utilized or evaluated during the simulation development process, including a brief description of each tool’s strengths and limitations. Unity, a robust game engine for interactive 2D and 3D experiences, was selected as the development platform for the IVR simulation.

Table 2

Tools and Websites Employed or Evaluated for Simulation Development

While Unity required a significant manual setup, its extensive resources and step-by-step guidance from ChatGPT-4o made the process manageable. The development began with configuring the extended reality toolkit to establish the VR camera, controls, and participant navigation. Next, assets, such as environments and virtual characters were sourced from Sketchfab.com and Mixamo.com and imported into the Unity project. ChatGPT was then further used to configure the animator controller, edit 3D models, and integrate essential components, including video players, background audio, lighting setups, hand tracking, haptic feedback, and interactive VR capabilities.

With the foundational elements in place, the focus shifted to the simulation’s core functionality: the dialogue interactions between the participant and Mrs. S. ChatGPT played a crucial role in coding the dialogue manager script, which integrates with dialogue nodes and voice command scripts to manage these interactions seamlessly. Each node represents a single exchange between Mrs. S. and the participant, beginning with Mrs. S.’s dialogue, accompanied by context-specific animations and audio playback. The dialogue options are then displayed, and once the participant selects and vocalizes their choice, the next node is triggered.

The creation of these complex scripts followed an iterative process. Initially, the priority was to ensure that Mrs. S.’s dialogue and audio were properly displayed and played, that the four response options appeared correctly, and that the next node activated upon selection. This phase took approximately four days, involving constant back-and-forth with ChatGPT, which frequently revised the code—sometimes omitting critical sections. As a result, progress often had to be backtracked, leading to the implementation of version control through GitHub to precisely identify functional and non-functional lines of code (see Table 1 for details on the challenges encountered with ChatGPT-4o and adaptations implemented). This approach streamlined the process of adding new scripts, testing, troubleshooting, and debugging, making revisions more efficient and significantly reducing development time for subsequent scripts.

Other key scripts generated by ChatGPT included the teleport player script, which moves participants to designated locations; the room trigger script, which activates the dialogue manager when the participant approaches Mrs. S.; and the simulation controller script, which enables participants to exit the simulation using the voice command “end simulation.” ChatGPT-4o also assisted in assessing the feasibility of integrating certain features to the simulation or whether alternative approaches were necessary. The entire building process of the simulation required continuous learning, as no blueprint or guide existed to direct these decisions—each step involved problem-solving and adaptation in real time. From this, the initial simulation draft was completed, which was then shared with the advisory committee members, whose role and process are described herein.

Participatory Refinement

An advisory committee was established to enhance the simulation’s realism and ensure its relevance to nursing education, as well as its sensitivity to newcomers’ mental health needs and access barriers.  The 12-member committee included two patient-partners, four students, five nurses (some of whom also served as mental health educators or VR experts), and one user-experience consultant. To ensure balanced input, equal representation was maintained from both research sites—the University of Saskatchewan and McGill University. Patient-partners were compensated for their contributions, while other members volunteered their time. Members participated by reviewing the blueprint, attending one-hour Zoom walkthroughs, and/or independently testing the VR simulation with a headset. Some students also reviewed the study protocol, but no changes were suggested.

The advisory committee’s feedback primarily focused on dialogue improvements, including simplified language, shorter text, and better text visibility. Dialogue options were also suggested to appear only after the main character finished speaking to reduce cognitive overload. Moreover, the committee recommended concise animations, enhanced body language, and consistent integration of cultural and migration elements to improve realism and clarity.

Improvements to the onboarding process were also recommended, such as clearer introduction, an option to skip or exit the introductory video, and an exit mechanism for user comfort. Despite these suggestions, the committee expressed satisfaction with the simulation’s overall quality and alignment with research objectives.

Discussion

VR excels at transforming abstract concepts into practical real-world scenarios; however, during the development of our simulation, this proved to be a significant challenge. Cultural competency and humility training require a deep, evolving understanding of culture and identity, along with subtle yet meaningful distinctions across cultures and subcultures. These nuances—often conveyed through tone of voice, body language, and facial expressions—are difficult to replicate with virtual characters, which remain limited in their ability to mirror real-world human interactions. While the actual words spoken by Mrs. S. were easily modified, much of the non-verbal communication could not be fully captured, limiting the depth of the simulation.

Nevertheless, IVR can immerse students into virtual experiences where they must confront the discomfort, uncertainty, and urgency similar to those of real-world clinical interactions. As newcomers like Mrs. S. navigate complex challenges such as language barriers, stigma, and culturally specific expressions of distress, students engaged in IVR simulation are forced to engage beyond textbook knowledge, adapting their responses in real time. This active participation compels them to internalize clinical decision-making under pressure, reinforcing both their cognitive and affective responses. While virtual characters may not fully capture human nuance, the ability to repeatedly practice and refine communication skills within a controlled, safe, and virtual realistic environment enhances students’ confidence and preparedness for real-world patient interactions.

Looking ahead, nursing professionals are well-positioned to lead advancements in emerging technologies while upholding evidence-based care. This project demonstrates how accessible tools, such as Unity, ChatGPT, and online resources can empower nurses without coding experience to innovate. By integrating AI and advanced simulations, nursing education can deliver patient-centered learning experiences that enhance empathy, decision-making, and professional growth.

Conclusion

Developing IVR simulations for nursing education is both promising and challenging. Insights from this phase highlight the need for better tools and collaborative efforts to maximize their educational value. Future research should refine these tools, address technical challenges, and explore the effective integration of theory into practice. Additionally, studies should assess the long-term impact of IVR training on nursing students’ competencies and patient outcomes. Advancing in these areas can unlock IVR’s full potential, ultimately improving care for diverse populations.

References

Donlan, P. (2018). Developing affective domain learning in health professions education. Journal of Allied Health, 47(4), 289. https://www.proquest.com/docview/2162376796/abstract/F11FC63706C74013PQ/1

Ho Yan Lam, A., Lin, J. J., Wan, A. W. H., & Wong, J. Y. H. (2020). Enhancing empathy and positive attitude among nursing undergraduates via an in-class virtual reality-based simulation relating to mental illness. Journal of Nursing Education and Practice, 10(11), 1. https://doi.org/10.5430/jnep.v10n11p1

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Authors Bios

Geneveave Barbo, RN, MN, MClSc, Ph(c)

Geneveave Barbo is a doctoral student at the College of Nursing, University of Saskatchewan. Her research focuses on virtual reality, mental health care, and migrant health.

Donald Leidl, RN, BSN, MN, EdD

Dr. Leidl is an Assistant Professor at the University of New Brunswick’s Faculty of Nursing. His work emphasizes nursing education, virtual reality, and mental health.

Hua Li, RN, PhD

Dr. Li is an Associate Professor at the College of Nursing, University of Saskatchewan. Her research focuses on mental health and addiction, with particular emphasis on the needs of pregnant and postpartum women, as well as children and youth.

Marjorie Montreuil, RN, PhD

Dr. Montreuil is an Assistant Professor at the Ingram School of Nursing, McGill University. Her expertise includes mental health nursing, children’s services, ethics, and participatory research approaches.

Pammla Petrucka, RN, PhD

Dr. Petrucka is a Professor at the College of Nursing, University of Saskatchewan. Her research spans global health, community-based participatory research, and women’s health.

Acknowledgments

We would like to express our sincere gratitude to Alicia Johnson, Andrew Dear, Anissa Jeeroburkhan, April Mackey, Ilyass Rbihi, Jeanne Capati, Mariam Khan, and our patient-partners for their invaluable contributions, insights, and support throughout the development of this work. Your expertise and dedication were instrumental in shaping this project.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declarations of Interest

The authors declare that they have no competing interests.

Authors Contributions

GB- Conceptualization, Software, Writing-original draft; Writing – review & editing;

DL- Conceptualization, Validation, Writing – review & editing;

HL- Validation, Writing – review & editing;

MM- Validation, Writing – review & editing;

PM- Conceptualization, Supervision, Writing – review & editing