Content area
Aim
To explore the perceptions and experiences of mental health student nurses when interacting with artificial intelligence driven virtual patients during a simulated placement as a complementary teaching approach.
Background
Higher education institutions are increasingly adopting simulated placements to tackle the challenges posed by limited placement capacity. However, logistical challenges and resource constraints, remain significant obstacles for these institutions. Artificial intelligence and virtual reality, present a promising solution by offering nursing students flexible, immersive and safe learning environments.
Design
This qualitative descriptive study followed the Consolidated Criteria for Reporting Qualitative Research checklist and collected data through student focus groups.
Methods
A convenience sample of fifteen (n = 15) mental health nursing students were divided in two focus group to share their perspectives and experiences of engaging with the artificial intelligence driven patients in a virtual reality platform during history taking assessment. All participants had direct communication with artificial intelligence driven patients using both software functionalities, a menu-controlled and voice-controlled. A six-step reflexive thematic analysis of group discussions transcripts was employed to identify themes of their experiences and perceptions.
Results
Five key themes were constructed from the data analysis. 1) Confidence Building; 2) Communication skills development and application; 3) Information gathering within the assessment process; 4) Innovative tool and technology acceptance and 5) Knowledge enrichment through self-reflection.
Conclusion
Artificial intelligence driven virtual patients were perceived as an innovative, engaging and good complementary pedagogical approach for simulated placements to develop confidence, communication skills, nursing assessment skills and readiness for clinical practice.
1 Introduction
In response to the evolving challenges in healthcare and the need to enhance the quality of nursing education, the Nursing and Midwifery Council (NMC), the regulatory body for nurses and midwives in the United Kingdom (UK), has recently adjusted its pre-registration standards and their practice requisites to graduate as a registered nurse ( NMC, 2023).
At the same time, the British National Health Service (NHS) introduced the long-term workforce plan ( NHS, 2023) which highlights the need to address a depleted nursing workforce that is currently impacting patient care and student nurse’s placement capacity as a strategic priority. These changes include a new definition of simulation-based learning, an increase in the allowable number of simulated practice learning hours in the nursing curriculum and strong collaboration with higher education institutions (HEIs) to support the integration of the latest technologies to embed hybrid learning across healthcare programmes.
This move aims to ensure the nursing workforce continues to develop and adapt to current healthcare demands, creating opportunities to fully integrate digital technology into training pathways, support more efficient and flexible learning methods and enhance the overall learner experience ( NHS, 2023; NMC, 2023).
In response, some HEIs have explored the use of simulated practice placements to address the limited availability of clinical placements with NHS healthcare partners ( Health Education of England. (n.d.)). Nevertheless, this transition is often hindered by logistical challenges, including limited access to simulation facilities, scheduling difficulties and resource constraints ( Liaw et al., 2020; Mills et al., 2020).
The rapid advancement of technology, particularly in artificial intelligence (AI) and virtual reality (VR), offer a promising solution to support the expansion of simulated practice. These tools can enable students to continue developing their clinical skills in flexible, self-directed ways, without the constant need for a facilitator or clinical resources. Several reviews and studies have described the application of VR in healthcare education, demonstrating its effectiveness in procedural training, anatomy and physiology learning and scenario-based case learning. Most of these studies have mainly focused on knowledge acquisition, technical skill development or assessment and treatment in mental health disorders ( Steen et al., 2024; Kyaw et al., 2019).
AI-chatbot technology in healthcare has explored on the development of soft skills such as patient-clinician communication by streamlining interactions and providing instant responses based on participant questions ( Milne-Ives et al., 2020). However, AI-chatbot platforms often lack visual elements that are crucial for accurate and holistic patient assessments. Non-verbal signals, such as body language and facial expression, are often key indicators in the mental health context and may help healthcare professionals detecting issues that may not be apparent through text or voice alone ( Abdulghafor et al., 2022).
The integration of AI within VR clinical environments introduces a further level of interactivity, enabling responsive simulations that adapt in real time based to learners’ decisions by combining both verbal and non-verbal dynamics. These systems can recreate realistic clinical settings and with audiovisual cues when engaging with their tasks or performing their nursing assessments ( Berg and Steinsbekk, 2020), allowing students to practice essential soft skills such as communication techniques, body language and clinical decision-making ( Shorey et al., 2020, Teixeira., et al. 2024). Developing these soft skills is particularly relevant for mental health nursing, where effective communication is central but remains a difficult skill to teach and assess through traditional methods ( Furnes et al., 2018).
All VR scenarios in this study incorporated AI to simulate realistic, adaptive patient responses based on learner input and scenario progression. The AI also powered voice control, enabling natural, conversational interactions that mirror real clinical encounters allowing students to ask relevant questions during patient assessments in complex healthcare scenarios and develop their communication skills ( Liaw et al., 2020; Shorey et al., 2020).
This is supported by machine learning (ML), a subset of AI that enables systems to learn from data and improve over time ( Apell and Eriksson, 2023), enhancing their ability to support complex decision-making in healthcare training.
It is important to note that this VR-AI technology offers various simulation formats, from desktop-based to fully immersive headset experiences, with interaction modes such as hand-held and speech control ( Liaw et al., 2023). Speech control provides an intuitive and efficient way to engage with virtual environments, offering a more realistic experience while enhancing clinical thinking, decision-making and confidence ( Padilha et al., 2019).
Using VR-AI driven patient simulations, students engage with patients exhibiting diverse health conditions and backgrounds. These competencies form the foundation of nursing decision-making and are essential for students to develop during clinical placement and to progress toward becoming a registered nurse ( NMC, 2023, PLPAD, 2022). Students can learn in their own pace and repeat the scenario as many times as they need.
Educators also benefit from these technologies, as they can personalise student learning experiences and assess practice competencies by observing student interactions with AI-driven virtual patients. Additionally, educators can leverage auto-generated software feedback for debriefing and to gain insights into student behaviour, learning patterns and progress, which facilitates early identification and support for struggling learners ( Luctkar-Flude et al., 2021; Rourke, 2020).
While VR in healthcare education is well-researched, there is limited understanding of how AI-enhanced VR can support nursing students, especially in mental health or in developing soft skills like therapeutic communication and history taking. Existing studies, such as Kim et al. (2024), Peddle et al.(2016) and Teixeira et al. (2024), highlight improvements in communication, critical thinking and clinical confidence through AI and VR simulations. However, there remains a gap in qualitative research specifically examining history-taking and communication skills in mental health nursing using AI-VR simulations. Addressing the current gap could lead to more tailored, evidence-based approaches that prepare students for the complex interpersonal demands of mental health care. This area of practice requires nurses to communicate empathetically, manage challenging behaviours and collect sensitive information, skills that are difficult to teach through traditional methods. Integrating AI-VR into nursing curricula could enhance student preparedness, reduce placement-related stress and develop more confident and competent graduates.
This study aims to address this gap by exploring the use of AI-integrated VR simulations in developing communication and history-taking skills in mental health nursing education. It considers the potential of this technology to support both learner autonomy and faculty-led assessment, contributing to the evolving landscape of simulation-based education. By exploring students' perceptions of educational tools, such as AI-integrated VR simulations, educators can identify barriers to learning and adjust teaching strategies to better meet students' needs. Understanding perception also provides valuable insights into the emotional and cognitive responses to learning environments, which can impact motivation, confidence and overall academic success ( Karimi Mirzanezam et al., 2024).
2 Aim and objective
This qualitative descriptive study explores the perceptions and experiences of mental health nursing students as they engage with VR AI-driven patients during a simulated placement. The study aims to evaluate how VR-AI patient-led simulations influence students' skill development, particularly their communication during the assessment process. It also investigates whether AI-VR learning is perceived as a valuable complement to simulated placements and a meaningful addition to nursing education.
2.1 Research question
How do nursing students experience and perceive the impact of AI-driven VR simulations on their history taking assessment skill development and to what extent can AI-VR learning enhance and complement traditional simulated practice in nursing education?
3 Materials and methods
3.1 Theoretical framework
This study was underpinned by Kolb ( 1984), which conceptualises learning as a process whereby knowledge is created through the transformation of experience. The AI-driven VR simulations were designed to support this experiential cycle offering students concrete experiences, abstract conceptualisation and opportunities for reflective observation and active experimentation.
This learning strategy is also aligned with the problem-based learning (PBL) approach, giving students the opportunity to practice student decision making skills, their problem solving and clinical reasoning to overcome environmental constraints in clinical practice ( Song and Kim, 2023).
4 Methods
4.1 Research design
This study used a descriptive qualitative research design ( Doyle et al., 2019) and followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist ( Tong et al., 2007) to enhance the rigor and transparency of the research process across three domains: research team and reflexivity, study design and data analysis/reporting. Each of these domains was considered during study planning, data collection and manuscript preparation to ensure clear documentation of methodological decisions, researcher positionality and analytic procedures.
4.2 Research settings
Candidates engaged with two distinct scenarios delivered via a 2D computer screen in the faculty simulation centre during their simulated placement. Each session lasted at least 20 min and was conducted synchronously. The two scenarios used different interaction modalities: one was menu-based, while the other was voice-controlled.
In the menu-based scenario, participants selected questions from a dialogue menu to gather the patient’s presenting complaint, as well as their physical, social and psychological history. These questions were organised under various categories commonly used in clinical history-taking (
Both VR simulations employed sophisticated AI-driven software to create realistic patient interactions. The software company verified that the scenarios used in this research incorporated use of AI.
4.3 Study participants
Mental health nursing students (n = 15) from a UK university were recruited via convenience sampling. Inclusion required enrolment in a pre-registration program and experience with both AI-VR interaction modalities during simulated placements: AI-led menu-based interface and AI-led speech control. The only exclusion was lack of exposure to both scenarios.
Of 26 eligible students invited through in-person announcements and follow-up emails, 15 agreed to participate. The participant information sheet clarified voluntary participation and minimal research risks. Consent was obtained before data collection.
The final sample comprised second-year students representing diverse ages, genders and ethnic backgrounds reflective of the wider student population. Specific demographic data were not collected due to data protection considerations and the potential limitations in accurately capturing the cohort’s diversity.
4.4 Data collection methodology
To gather insights into mental health nursing students' perceptions and their reflections, a focus group methodology was employed.
The focus group guide (Table A) was developed and reviewed collaboratively by the research team (NCM, SM and BJS) to ensure alignment with the study objectives of exploring students' perceptions and experiences of the use of AI-driven VR simulations. It included open-ended prompts to encourage discussion and allow for the emergence of unanticipated themes.
Two in-person focus groups were conducted in November 2023, both led and moderated by the same facilitator (SM). The first focus group consisted of seven (n = 7) students, while the second focus group was composed of eight (n = 8) students.
Participants were seated in a simulated centre classroom arranged in a circle to promote open and inclusive discussion. The facilitator (SM), also seated within the circle and asked a set of open-ended questions while moderating the discussion and ensuring that all participants had the opportunity to contribute. To ensure an accurate interpretation of the data, field notes were taken to provide additional context and support the transcripts.
Both group discussions lasted between 45 and 50 min. Another author (BJS) was present in the room to operate the digital recording device but did not interfere during the group discussion.
All contributions were recorded and transcribed verbatim using automated transcription software. To ensure the accuracy of the data and a faithful representation of participants’ perspectives and experiences, the recordings were reviewed and compared with transcripts ( Nowell et al., 2017). A few spelling errors were identified and some participant quotes were initially misattributed.
The anonymity and confidentiality of participants were strictly maintained throughout the study. Personally identifiable information was removed from the transcripts to uphold ethical research standards. Anonymised transcripts a were stored in the HEI's OneDrive personal storage space, which was password-protected. Only the three authors of this paper had access to data during the data analysis process. These files were deleted on completion.
4.5 Ethical approval
Prior to commencing this research, ethical approval was sought and granted by the relevant institutional ethics review board, which are congruent with the MRC/NHS governance frameworks, the Statement on Safeguarding Good Scientific Practice issued by the create a reference citation: Biotechnology and Biological Sciences Research Council and the ESRC’s Research Ethics Framework for Good Research Practice ( UK Research and Innovation, 2022). The study adhered strictly to ethical guidelines, ensuring the protection of participants' rights, privacy and confidentiality throughout the research process. All participants provided informed consent and were assured their data would be anonymised to maintain their privacy and confidentiality in compliance with established ethical standards.
4.6 Data analysis
An in-depth reflective thematic analysis of the collected qualitative data followed Braun and Clarke's (2022) six-step process, which encourages critical self-reflection and recognition of researcher bias ( Naeem et al., 2023). Two researchers (SM and NCM) with nursing background experience and interest in healthcare simulation acknowledged their clinical perspective might influence interpretations, while the third researcher (BJS), without clinical experience, provided balance. None were directly linked to the students or course leadership, minimising bias and allowing consideration of broader learning outcomes, including emotional and psychological aspects.
Transcripts underwent familiarisation and manual coding via spreadsheets. Researchers coded individually then met regularly to compare results using triangulation ( Korstjens and Moser, 2018), enhancing trustworthiness ( Guba and Lincoln, 1994). From the initial 23 codes, researchers categorised thematic similarities into preliminary themes. Discrepancies were resolved through peer consensus-building discussions. This collaborative process ensured reliability and validity of identified themes, strengthening analytical rigor ( Korstjens and Moser, 2018). Despite the small sample, data saturation was achieved as no new themes emerged during analysis of the second focus group.
4.7 Results
Participants' responses demonstrated alignment with experiential learning theory. For instance, their engagement with VR-AI driven scenarios offered concrete experiences, while the focus group discussion facilitated reflective observation. Students described developing new insights and clinical reasoning strategies, indicative of abstract conceptualisation, which they intended to apply in future clinical settings, suggesting active experimentation. Most students positively perceived their exposure to VR-AI-driven scenarios as a complementary learning opportunity to engage with patients presenting different health conditions and backgrounds, some flaws were also identified. The following five key themes were constructed by the researcher through deep, interpretive engagement with the data: 1) Confidence building; 2) Communication skills; 3) Information gathering within the assessment process; 4) Innovative tool and technology acceptance and 5) Knowledge enrichment through self-reflection [
4.8 Theme 1: confidence building
Confidence building was one of the major themes that were generated from this study. Most participants using AI-driven VR case scenarios increased their overall confidence in conducting nursing assessments and preparation for clinical placement. They felt more prepared and knowledgeable when interacting with real patients as it gives the opportunity to practice (Q1; Q2; Q3; Q4; Q5; Q6). Students stated: “ I think that it has given me more confidence.” (Q1, Participant 2); “You're getting that empowerment […] But it's just building our confidence.” (Q4, Participant 5). Some participants specifically noted confidence improvement in their questioning skills, helping them avoid common errors and ensuring more effective communication with patients: “ This AI, for me personally, it has given my confidence that in reality, if I'm asking patient questions, at least I know how to word them.” (Q3, Participant 3).
This has also led to a perception of increased confidence in competency, especially in patient assessment, with a student noting, “ (the AI-driven patient scenario) actually increased my competency in most parts of the placement” (Q2, Participant 1). That said, one student mentioned that the AI-driven patient interaction did not contribute to building up their confidence and compared this method to video-case scenarios (Q7, Participant 10). " I think it's just developing a lazy way of learning. It's not really developing the confidence." (Q10, Participant 10).
4.9 Theme 2: communication skills
The AI-driven scenarios have also played a pivotal role in honing students’ communication skills and active listening abilities. Most of students mentioned how VR-AI voice control technology help them to rethink on how questions need to be formulated, use plain language and the importance of using short sentences (Q8; Q9; Q10). Some students quoted: "I feel like it's just really helped me on my line of questioning because there's always two types, open and closed questions." (Q8, Participant 3); "If you're asking a long question, too many questions at a time, they won't understand it. So, you just have to just say, reframe it, summarise it.” (Q9, Participant 4).
Another participant reconfirms above statements but complements with “It helps in our communication skills and listening” (Q11, Participant 15). Students agree and reported that the VR-AI driven scenarios encouraged them to listen carefully and enabled them to collect observational data alongside subjective information, fostering analytical thinking and enabling them to identify issues or symptoms and draw conclusions: “It added to our observation skills, in this case that we were able to, as being taught, collect that as subjectively and objectively. After that, you analyse and be able to come up with a conclusion that a particular thing wrong with this person” (Q12, Participant 6).
However, it is important to highlight that some participants identified challenges with voice recognition in the VR-AI voice control scenario. Some participants felt frustrated as the AI-patient did not understand some of their questions, word incompatibility, or the length of the questions and stating (Q13; Q15; Q16). “ One of the negative experiences, one thing I've noticed, apart from what I said, you have to re-word them. You have to reword it properly, appropriately." (Q13, Participant 7).
Another important element highlighted by one participant was the limitation posed by the patient avatar's lack of non-verbal expression and body language, which they felt was a key element when communicating with patients, particularly mental health patients. Participant stated: “Also, in practice, when you are in placement, when you are interacting with the patient, when you are doing your visual look, you may see something the person is trying to hide from you. But here you cannot find it. You cannot see.” (Q16, Participant 4).
4.10 Theme 3: information gathering within the assessment process
Most participants discussed how the integration of VR-AI technology in patient care enhanced their ability to gather information during nursing assessments. They observed that the VR-menu-based patient tool facilitated the assessment process by guiding and prompting them when collecting pertinent patient data by selecting from a menu choice. Instead, the VR-AI tool with voice-control feature helped them realise the importance of formulating questions appropriately. It also stimulated their thinking about the assessment structure and sequence of questions to obtain relevant information more effectively by stating: “I t helps with gathering information (…) and then I can do my assessment with confidence." (Q17, participant 15); " it has been beneficial in the area of assessment and accurately diagnosing the patient's condition"(Q18, Participant 4).
One student pointed out that while VR-AI technology offers innovation in patient assessment, there is a concern that it may limit the physical examination assessment aspect. They remarked, " it will take away the physical assessment of the patient” (Q19, Participant 15). This highlights a potential drawback of relying solely on VR and AI tools for patient evaluation, as it may not capture the nuanced uncertainties that can arise during physical assessments. Conversely, one participant made a point of developing transferability skills on assessing patients from and to different geographical locations or like telemedicine using digital technologies, stating: “ I would say the world is changing. (…) the time might come that the nurse is in a different location and the patients in a different location. (…) this is the future” (Q20, Participant 8).
4.11 Theme 4: innovative tool and technology acceptance
Most participants affirmed that VR is an innovative pedagogical tool (Q21; Q22; Q23; Q24). Some participants were hesitant of using AI-VR software tool, but one student admitted: " Initially, I was very apprehensive with this technology, but I think it has a very good part to play to enhance the future nurses" (Q25, Participant 5). They gradually recognised its value. Most participants acknowledged a realism sensation to the scenarios, but some challenges arose with the AI's grasp of accents, limited vocabulary and patient avatar body-language absenteeism, indicating the need of room for improvement in these VR features. Despite this, VR and AI technologies were seen as innovative tools for improving learning and patient interaction preparation for placement. A student remarked: " there are certain things there which I don’t know about. But seeing them there [in the VR scenario with menu] , I will bring that on to the patient in practice" (Q26, Participant 9).
4.12 Theme 5: knowledge enrichment through self-reflection
Students perceived that their exposure to VR-AI driven patient scenarios expanded their clinical knowledge, encouraged self-reflection and prompted them to consider questions they might not have otherwise thought about, particularly through the review of autogenerated feedback (Q28). A student stated: “It does actually give very good feedback (…) I think it enlightens you. It furnishes your clinical knowledge, physical and mental” (Q27, Participant 15). Collaboration and sharing of insights with peers of their performance and feedback were seen as valuable in the learning process, allowing students to make informed decisions and compare their notes (Q28). Several participants suggested that VR-AI driven scenarios ought to be introduced earlier in their education, ideally before their first placement (Q29). Additionally, a few emphasised the importance of integrating such tools into the nursing programme (Q30).
5 Discussion
The results of this research demonstrated that participants perceived an increase in confidence following their interaction with the AI-driven VR patient. This outcome aligns with findings that show VR simulation not only improves knowledge acquisition and clinical skills ( Kyaw, 2019) but also enhance self-confidence and self-efficacy ( Bani Salameh et al., 2024; Liaw et al., 2023; Jallad and Isık, 2022). Similar results were presented in Mickiewicz, et al. (2021) study, where VR was used for surgical training. All medical students indicated higher self-confidence in relation to their own surgical skills and emphasised the importance of including VR training in their routine medical educational program because it provided a structured, safe and supportive environment for familiarisation with the situation.
The perception of enhancing communication and listening skills during VR-AI simulation was another of the most common themes discussed between participants of this study. Liaw et al. (2023) mixed-method study on using VR with an AI-chat bot also identified significant improvements in communication, knowledge and interprofessional communication self-efficacy. Peddle et al.(2016) found that in their integrative review focusing on virtual patients and non-technical skills acquisition, sequencing learning activities surrounding the virtual patient, supports the development of key nursing competencies such as communication, teamwork and decision-making.
Knowledge enrichment through self-reflection was another theme that emerged from this research study. Rourke (2020) and Abbas et al., (2023) systematic reviews compilated evidence demonstrating that studies evaluating VR groups performed favourably in comparison to simulation groups in post-test knowledge scores, cognitive gain, skill performance scores and skill success rate. A systematic review and meta-analysis by Kyaw et al. (2019) found that VR simulations, compared with traditional or digital learning methods, led to small improvements in knowledge acquisition and moderate-to-large improvements in clinical skills.
There is limited evidence available about the student reflection in action process involved during VR-AI driven case scenarios. Participants in this study highlighted the value of their reflection process throughout their VR-AI patient interactions, particularly when formulating history taking questions repeatedly and after reviewing the autogenerated feedback upon completing the scenario, for their knowledge and cognitive skills development. According to Teixeira et al., (2024), the AI-driven feedback in VR scenarios provides consistent and immediate insights, reinforcing learning and enabling students to adapt their communication techniques dynamically. Supporting this approach, Shin et al. (2019) integrative review, highlighted that VR simulation aligns with previous simulation pedagogical approaches based on experimental learning, as it intends to solve contextual problems. Furthermore, obtaining effective feedback and debriefing are a pedagogical factor that should be taken into consideration when designing VR-AI simulation sessions, as it provides learning direction and helps learners to reach their goals, giving the opportunity to improve knowledge, self-confidence and technical or non-technical skills ( Luctkar-Flude et al., 2021; Motola et al., 2013).
The acceptance of technology and the challenges encountered during VR-AI experimentation are commonly discussed topics in the literature. Abbas et al., (2023) and others have reported similar findings where students expressed apprehension about this learning modality and encountered technical difficulties or challenges navigating through VR platforms, impacting on their enjoyment of using VR ( Alvarez and Dal Sasso, 2011). While some participants in this study found that speaking directly to the patient poses some advantages and makes navigating through scenario more intuitively and realistic, some limitations identified were regarding AI-patient avatar not displaying verbal cues nor body expressions ( Allred and Gerardi, 2017; Liaw et al., 2023). On some occasions, voice-controlled AI-patients did not understand some word pronunciations or specific words or long sentences and this was also highlighted in other studies ( Liaw et al., 2023; Kolachalama and Garg, 2018; Chan and Zary, 2019). This posed challenges and led some student frustrations and verbalising further tool improvement is required to address some of these eventualities.
Overall, VR simulation provides a safe and controlled environment for learners to practice clinical skills ( Shin et al., 2019). The integration of AI into VR case scenarios in nursing education has the potential to enhance knowledge acquisition and cognitive skills ( Chen et al., 2020), clinical reasoning ( Dubovi, 2018, 2019; Sim et al., 2022), as well as soft skills development ( Liaw et al., 2023; Peddle et al., 2016). Still, there remains a lack of robust evidence on the specific impact of AI on student learning outcomes and limited insight into how the reflective processes of students using AI-integrated simulations differ from those engaged in traditional simulation methods ( Luctkar-Flude et al., 2021).
5.1 Facilitator perceptions
During implementation of AI-driven VR simulations, the facilitators observed several noteworthy aspects. Contrary to initial expectations, students preferred the structured menu-base scenario, which enhanced confidence and questioning competency. Students often transferred this structured approach when later using voice-controlled scenarios, developing systematic communication skills despite reduced autonomy.
Current limitations include AI's inability to detect tone nuances and limited body language features that have an impact on assessment outcomes. Some participants viewed these challenges as opportunities to refine communication skills, particularly in simplifying language for clearer patient interactions.
While generally well-received as an educational tool, AI-led VR scenarios were considered complementary to, not replacements for, clinical placements. Participants suggested implementing menu-based scenarios earlier in the curriculum to scaffold communication skills before introducing voice-controlled scenarios later when students have developed clinical experience and can better formulate relevant questions and conduct structured assessments without a framework. Given the importance of communication in nursing practice, integrating VR with AI-driven scenarios into simulation-based learning is recommended, with further research warranted.
5.2 Limitations
While the study offered valuable insights, its limitations must be acknowledged. Firstly, the focus group methodology may have restricted the exploration of individual experiences. Additionally, the study primarily focused on student perspectives, indicating a need for further research into educators' viewpoints and the broader implications for nursing education. The research involved a small sample of mental health student nurses from a single UK university. Thus, although the findings might be applicable to other nursing fields or healthcare disciplines, further research is necessary to generalise the results.
6 Conclusion
The integration of AI-driven simulations into VR scenarios shows great promise for nursing education by enhancing students' confidence, soft-skills and self-efficacy. The research indicated that VR with AI-led patients improves confidence, communication, clinical reasoning and knowledge acquisition. The immersive environment was particularly valued for opportunity to experiment in different situations and self-reflection. The study also identified challenges such as technical issues and limitations in AI interactions, including the absence of certain visual cues and difficulties in language comprehension, highlighting the need for ongoing technological improvements.
In summary, while VR and AI have significant potential in nursing education, further research is needed to generalise findings and refine the technology. Addressing these challenges can make VR AI-driven simulations an even more effective tool, offering safe, engaging and impactful learning experiences. Embracing VR and AI in nursing education represents a transformative leap forward. It can help bridge the gap between theory and practice, reduce the demand on simulation resources and support the development of a more competent and confident nursing workforce to meet modern healthcare demands.
Author contributions
All three authors contributed equally to the research design, implementation, data collection, and analysis. Prior to conducting the study, they were professional colleagues who shared a common interest in exploring the use of virtual reality and artificial intelligence in healthcare education. The specific contributions of each author are outlined below:
• First Author: Neus Carlos Martinez is a Senior Lecturer and primarily wrote the manuscript and integrated feedback from the co-authors. She was the primary researcher and was actively involved in the research design and implementation and contributed significantly to the collection of results and data analysis. She has over 20-year experience in health care as a registered nurse and 8 years in academia and specialized in perioperative care.
• Second Author: Simone Morini was a Senior Lecturer at the time of the study and contributed to the writing process and provided essential revisions to the manuscript. He was actively involved in the research design and implementation and contributed significantly to the collection of results and data analysis. He has 6 years of experience as a registered nurse and has specialized in intensive care. He worked as an academic for 4 years.
• Third Author: Behnam Jafari-Salim is a digital learning specialist and played a critical role in the research implementation, data collection, and analysis, and provided essential revisions to the manuscript. He also played a crucial role in integrating the virtual platform into the simulated placement and digital tool troubleshooting. He has over five years of experience in academia, working collaboratively with staff to integrate engaging digital content into the curriculum and to introduce innovative learning technologies into teaching sessions.
All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
CRediT authorship contribution statement
Behnam Jafari Salim: Writing – review & editing, Software, Resources, Methodology, Formal analysis. Simone Morini: Writing – review & editing, Resources, Methodology, Investigation, Formal analysis, Data curation. Neus Carlos Martinez: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Formal analysis, Data curation.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Generative AI and AI-assisted technologies in the writing process
During the preparation of this work the author(s) used ChatGPT to correct spelling and grammatical mistakes. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
Declaration of Competing Interest
The authors declare no conflicts of interest. The study was conducted independently during our previous academic position and the conclusions drawn are based solely on the data and analysis conducted during that time. Current affiliation does not affect the integrity, objectivity, or impartiality of the findings presented in this article.
Acknowledgments
The authors would like to thank all the nursing students who took part in this study.
Appendix
|
Here are the main questions asked during the focus group session:
1. Introduction Questions: o Please introduce your first name and share one thing you enjoyed about your placement so far. 2. Experience with AI and VR: o Can you describe your experience using the AI-voice control scenario today and the AI- menu-based scenario yesterday? 3. Positive Experiences with AI: o Focusing on the AI-voice controlled conversation scenario today, what positive experiences did you have and how did it compare to the virtual reality session menu-based session yesterday? 4. Negative Experiences with AI: o Focusing on the AI-voice controlled conversation scenario today, what negative experiences did you have and how did it compare to the virtual reality session menu-based session yesterday? o Did you find any aspects of the VR AI-voice control or menu-based control scenario experience difficult or hard to manage? 5. Features and Functionalities: o Which features or functionalities of the AI and VR technology did you find most helpful? o What features or functionalities of the AI and VR technology did you find least helpful? 6. Challenges and Limitations: o What challenges did you face during the VR-AI scenario, and how did you overcome them? o What challenges did you face using the technology, and how did you manage it? 7. Impact on Nursing Assessment: o How do you think the virtual reality and AI technology you used help with your history taking assessment process? o Is there anything specific in history taking assessment that this technology helps you develop? 8. Most Important Aspect of the Learning Experience: o Out of everything we discussed today, what was most important to you in this learning approach? o How does this technology help you with your learning process? o Would you recommend integrating this technology in the nursing curriculum? Why? 9. Suggestions for Improvement: o What improvements would you suggest for the technology? |
Table 1
| Themes and Students’ quotes | ||
| Student | Quote | Theme 1: Confidence building |
| S2 | Q1 | “I think that it has given me more confidence.” |
| S1 | Q2 | “I think the AI section, the virtual section has actually given me a very strong sense of belief in myself that I will be able to carry out certain things if I’m asked to do them for the fact that I’ve actually seen it being done. That has actually increased my competency in most parts of the placement. It’s good.” |
| S3 | Q3 | "This AI, for me personally, it has given my confidence that in reality, if I'm asking patient questions, at least I know how to word them and reward the words in case they don't understand." |
| S5 | Q4 | “(…) you're getting that empowerment because sometimes you're in placement, they're asking you, you don't know, but we all know a lot. But it's just building our confidence.” |
| S8 | Q5 | “(…)really given me inside confidence, carry out most of these skills that I have read or seen. This one I'm giving the chance to practice on my own. Confidence has given me, the confidence.” |
| S13 | Q6 | “I think as you learn process to be with an integral part of the nursing programme. That means if a student starts the course, he's given the license rather than to work the later part of it so that you can start learning and building confidence.” |
| S10 | Q7 | "I think it's just developing a lazy way of learning. It's not really developing the confidence. It's just like, okay, I have to just answer these questions. It's not really engaging." |
| Theme 2: communication skills | ||
| S3 | Q8 | "I feel like it's just really helped me on my line of questioning because there's always two types, open and closed questions." |
| S4 | Q9 | "if you're asking a long question, too many questions at a time, they won't understand it. So you just have to just say, reframe it, summarise it. |
| S4 | Q10 | “Yeah. It's the same thing I wanted to say that it helps you… It just show that sometimes we might be asking patients some question we might be thinking they understand. So while we're asking this question, we need to break it down to the understanding so we ask again to get a better response.” |
| S15 | Q11 | "It helps in our communication skills and listening. Skills about what to do. That's a great thing." |
| S6 | Q12 | “It added to our observation skills, in this case that we were able to, as being taught, collect that as subjectively and objectively. After that, you analyse and be able to come up with a conclusion that a particular thing wrong with this person” |
| S15 | Q13 | "Sometimes it's not about what the question is but how you ask the question." |
| S7 | Q14 | “One of the negative experience, one thing I've noticed, apart from what I said, you have to re-word them. You have to reword it properly, appropriately." |
| S13 | Q15 | "My concern is why does he understand only certain vocabularies? When you speak to a patient, you expect that the patient understands English automatically, understands what you're saying. Why do I have to say certain words for the patient on the virtual to understand what I'm saying? You ask a simple question, when did you last have a back issue? And he's like, what's that answer again? Pardon? Pardon me. That's very simple in this language. It's very innovative, really creative, but I still think there are shortcomings here." |
| S4 | Q16 | “Also, in practice, when you are in placement, when you are interact with the patient, when you are doing your visual look, you may see something the person is trying to hide from you. But here you cannot find it. You cannot see.” |
| Theme 3: Information gathering within the assessment process | ||
| S15 | Q17 | “It helps with gathering information later. (…) really go on assessment, get all the information, and I can put the ears back together, and then I can do my assessment with confidence." |
| S4 | Q18 | “I would say it has helped in the area of assessment and being able to diagnose exactly what is going on with the patient because when you have the right question to ask, you need to gather the information on what the patient is suffering, how going to do about it, what are the possible intervention that we need to carry out.” |
| S15 | Q19 | “I think it's a good innovative, but it will take away the physical assessment of the patient because it doesn't pick uncertainties that happen during assessment.” |
| S8 | Q20 | “I would say the world is changing. Based on that fact, we have to be catching up every day as the world evolves because the time might come that maybe we are in a different location, the nurse is in a different location, and the patient's in a different location. With that, with the video from him talking and we're responding, there's a lot we can do to be able to diagnose them correctly. So I really think the changing time is going to be what is going to This is the future, actually.” |
| Theme 4: Innovative and technology acceptance | ||
| S13 | Q21 | "Yes, it is innovative, but my concern is why does he understand only certain vocabularies?” |
| S14 | Q22 | “I think it's a very good innovation. (…)"Yeah, I think this will help the students to be more interactive, because most times they feel shy, and they don't want to ask questions, so I think this will help them to be more open." |
| S15 | Q23 | "I think it's a good innovative, but it will take away the physical assessment of the patient." |
| S10 | Q24 | “It's innovative” |
| S5 | Q25 | "Initially, I was very apprehensive with this technology, but I think it has a very good part to play to enhance the future nurses." |
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"Like I said before, there are tools, there are certain things there which I don’t know about. But seeing them there [in the VR scenario with menu], I will bring that on to the patient in practice." |
| Theme 5: Knowledge enrichment through self-reflection | ||
| S15 | Q27 | “It does actually give a very good feedback because in summary, at the end of everything, you seem to now realize that there are certain questions I should have asked that I missed, and there are certain questions I asked that was not actually appropriate. I think it enlightens you. It furnishes your clinical knowledge, physical and mental.” |
| S8 | SQ28 | “So that will give us that power to be able to compare notes, so we can make an informed decision as to sadly how we can diagnose the patient.” |
| S1 | Q29 | “If I was in charge, the change I will make is, this will come first before placement.” |
| S5 | Q30 | “Implementation in curriculum, I think as you learn process to be with an integral part of the nursing programme." |
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