Abstract
-
Purpose
This pilot study aimed to develop a virtual reality (VR) nursing simulation for pediatric pneumonia care tailored to the Korean clinical context and evaluate its educational effectiveness for nursing students.
-
Methods
A single-group pretest–posttest design was employed with 20 senior nursing students from April to May 2025. The intervention was a 70-minute VR nursing simulation of pediatric pneumonia care, structured with pre-briefing, a main scenario, and debriefing. Data were collected on participants’ knowledge of pediatric pneumonia, clinical judgment (Nursing Clinical Judgment Scale), and simulation effectiveness (Simulation Effectiveness Tool-Modified) before and after the intervention. Usability (User Experience Questionnaire) and qualitative feedback were collected after the intervention. Pre-post comparisons were performed using paired t-tests.
-
Results
The program did not yield a statistically significant change in knowledge scores (p=.893). However, there were significant improvements in the mean scores for clinical judgment (p<.001) and simulation effectiveness (p=.013). A qualitative analysis revealed that, while the participants found the experience immersive and realistic, they also reported operational difficulties, indicating the need for usability improvements.
-
Conclusion
The VR nursing simulation is a promising pedagogical tool for enhancing nursing students’ clinical judgment and perceived learning effectiveness in a Korean pediatric context. The findings suggest that, while the intervention was effective in improving practical reasoning, future iterations should focus on reinforcing knowledge acquisition and optimizing user experience to maximize the educational impact.
-
Key words: Nursing student; Pediatrics; Simulation training; Virtual reality
INTRODUCTION
Simulations replicate clinical environments to facilitate critical thinking [
1] and bridge the gap between theory and practice in nursing education [
2]. This provides an immersive and safe setting for students to develop diverse competencies [
3]. With recent advancements in virtual reality (VR) simulations [
4], the importance of this educational methodology has been highlighted by the limitations of clinical practicums during the COVID-19 (coronavirus disease 2019) pandemic [
5]. Nursing simulation offers a safe learning environment for students to practice without risk to actual patients [
6], thereby enhancing their clinical competence by learning from their mistakes [
7]. Integrating repetitive task performance with structured debriefing improves clinical outcomes [
8], establishing simulations as a core component of nursing education.
While nursing simulation education in South Korea is continuously advancing, one area requiring further development is the reliance on scenarios developed in foreign countries. This practice can create discrepancies due to cultural and systemic differences that may not fully align with the Korean clinical context. For example, North American-oriented scenarios privilege communication that respects the child’s autonomy and directly delivers explanations to the child; conversely, Korean clinical practice places emphasis on family-centered communication competencies that reassure highly anxious caregivers, typically parents, and effectively address their questions [
9]. Furthermore, the drug names and medical devices depicted in overseas scenarios, such as certain infusion-pump models, are often absent in domestic practice. Consequently, they hinder learners’ immersion and conflict with the guidelines articulated in reports issued by the Korean Health Insurance Review and Assessment Service [
10]. Most existing scenarios are centered on adult patients, leading to a scarcity of content for pediatric populations that require specialized skills. Moreover, most commercially available scenarios are adult-focused, resulting in a critical shortage of pediatric content that reflects Korean clinical guidelines, medication names, and caregiver–child communication styles [
11,
12]. Furthermore, conventional simulations are often constrained by the high costs of equipment and personnel, limiting their accessibility and scalability for many institutions [
3,
13].
With the recent advancements in VR technology, simulations have emerged as a cost effective solution, unconstrained by time or place [
5]. As the technology matures, it is increasingly integrated into medical education to enhance learning outcomes [
14]. Particularly in pediatric nursing, VR simulations offer a means of safely experiencing several care situations that are otherwise difficult to practice [
5,
15]. Despite these benefits, there is a dearth of pediatric VR nursing simulation programs specifically designed for the Korean healthcare context.
To comprehensively evaluate the effectiveness of such a newly developed educational program, appropriate outcome variables must be selected based on prior research. Simulation-based education is recognized as a key strategy for bridging the gap between theoretical knowledge and practical application in complex clinical situations [
16]. To evaluate the educational effectiveness of the program, we selected key outcome variables grounded in prior research. Pneumonia was chosen as the clinical scenario because it is a prevalent and high-acuity disease in pediatrics and requires the application of integrated nursing competencies, such as assessment, intervention, and communication. Accordingly, the knowledge of pediatric pneumonia was selected as the primary variable. This is because simulation-based education is a well-established strategy for bridging the gap between theoretical knowledge and practical application, and its effectiveness in enhancing nursing students’ knowledge has been demonstrated in numerous studies [
16]. Furthermore, because the ultimate goal of the simulation is to enhance practical capabilities, clinical judgment was chosen as the core outcome variable. Several studies have demonstrated that VR and simulation-based learning are effective tools for improving nursing students’ clinical reasoning, decision-making, and problem-solving skills [
17]. Finally, simulation effectiveness was included to gauge the pedagogical quality and learners’ acceptance of the intervention. Perceived effectiveness and satisfaction from the learners’ perspective are critical factors that influence learning motivation and the successful integration of new educational technologies [
18].
Therefore, this study aimed to develop a pediatric VR nursing simulation program, specifically a scenario involving a child with pneumonia, suitable for the Korean clinical environment. Therefore, we conducted a pilot study to verify the program’s educational effectiveness. By developing a pediatric VR simulation program for the Korean nursing education setting, we hoped to overcome the limitations of current simulation practices and leverage the advantages of VR technology to effectively enhance students’ pediatric nursing competencies.
The primary objective of this study was to develop a VR nursing simulation program for pediatric patients with pneumonia tailored to the Korean clinical context and evaluate its impact on nursing students’ learning outcomes. This study sought to investigate the program’s applicability and initial effects, thereby providing guidance for future enhancements and establishing foundational data to validate the educational utility of VR simulations. The specific aims were as follows: (1) to evaluate the effectiveness of the VR simulation program by comparing pre- and post-intervention scores on the knowledge of nursing care for pediatric pneumonia, clinical judgment, and simulation effectiveness; (2) to describe the learners’ general characteristics and assess the simulation usability upon the completion of the program; and (3) to analyze the qualitative feedback from learners who participated in the VR nursing simulation program for pediatric pneumonia.
METHODS
Ethical statements: This study was approved by the Institutional Review Board (IRB) of Gang-neung-Wonju National University (GWNUIRB- 2025-6). Informed consent was obtained from all participants.
1. Study Design
This pilot study adopted a single-group pretest–posttest design to evaluate the effects of a VR nursing simulation program for pediatric patients with pneumonia. The primary outcomes were the nursing students’ knowledge of pediatric pneumonia care, clinical judgment, and perceived simulation effectiveness. This manuscript was prepared in accordance with the guidelines of the Transparent Reporting of Evaluations with Non-randomized Designs [
19].
The participants were senior students enrolled in a Bachelor of Science in Nursing program at Department of Nursing, Gangneung-Wonju National University in Wonju-si, Gangwon-do, who voluntarily consented to participate. The inclusion criteria required that the students (1) were currently enrolled in or had previously completed the pediatric nursing theory course and (2) had prior clinical practicum experience. Students were excluded if they had previous experience with VR simulation education in pediatric nursing or clinical experience in caring for a child with pneumonia.
Recruitment was conducted via an announcement on the nursing department’s online bulletin board, and participants were selected on a first-come-first-serve basis. On the first day of the study, all the participants received a detailed explanation of the research purpose and procedures before they provided written informed consent.
Initially, 21 students applied to participate in the study, and the final sample comprised the first 20 applicants. Following the recommendations of Whitehead et al. [
20] for pilot studies, which base the sample size on standardized effect sizes, a target sample size of 20 participants was deemed appropriate for assessing the intervention’s feasibility and preliminary efficacy. All 20 enrolled participants completed the study, resulting in a 0% attrition rate.
1) General characteristics
Data on the participants’ general characteristics were collected using a self-administered questionnaire. The variables included sex, age, academic year, and grade point average (GPA). This demographic information was used to describe the characteristics of the sample and control for potential confounding variables during the interpretation of the results.
2) Pediatric nursing knowledge regarding pneumonia
To assess the core pediatric nursing knowledge regarding pneumonia (PNKP), we developed a 10-item instrument. The items were constructed based on the primary learning objectives of the VR simulation scenario and reviewed for content validity by two pediatric nursing professors on the research team. The instrument items were calibrated to an appropriate level for the undergraduate nursing curriculum. Each item was scored as 1 point for a correct answer and 0 points for an incorrect answer, yielding a total score ranging from 0 to 10. A higher score indicated a greater level of knowledge on the topic.
3) Clinical judgment
Clinical judgment was measured using the Nursing Clinical Judgment Scale (NCJS), an instrument developed by Kwon and Park [
21]. This scale consists of 23 items rated on a 5-point Likert scale, with responses ranging from 1 (“not at all”) to 5 (“very much so”). It assesses six subdomains of clinical judgment: integrative data analysis, intervention evaluation and reflection, evidence for intervention, interprofessional consultation, patient-centered nursing, and collaboration with peer nurses. A higher total score indicates a higher level of clinical judgment. The scale demonstrated high reliability at the time of development with a Cronbach’s α of 0.92. In the present study, the instrument exhibited excellent internal consistency with a Cronbach’s α of 0.88.
4) Simulation effectiveness
The effectiveness of the simulation was measured using the Simulation Effectiveness Tool-Modified (SET-M), developed by Leighton et al. [
22]. This instrument comprises 19 items designed to assess the effectiveness of simulation-based learning across three subscales: pre-briefing, learning, and debriefing. Responses are recorded on a 3-point Likert scale (1=disagree, 2=slightly agree, 3=strongly agree). The original instrument demonstrated high reliability (Cronbach’s α=0.94), and it maintained high internal consistency in the present study (Cronbach’s α=0.89).
5) Simulation usability
The usability of the simulation was assessed using the User Experience Questionnaire (UEQ) developed by Laugwitz et al. [
23]. The UEQ is a 26-item instrument that evaluates a user’s subjective experience across six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Each item is rated on a 7-point semantic differential scale across a spectrum between two bipolar adjectives. Higher scores indicate a more positive user experience. The UEQ is a well-established instrument, with prior studies consistently reporting high reliability across its subscales (Cronbach’s α>0.70) [
24]. In the present study, the scale demonstrated high reliability with a Cronbach’s α of 0.90.
1) Development process
The VR nursing simulation program was developed to address the limitations of conventional education, such as difficulty reflecting on complex clinical situations and a lack of opportunities for integrated learning. The program was commissioned by the researcher and developed through close collaboration between pediatric nursing experts and Newbase (
https://medicrew.me/#/home), a professional VR content development company. A view of the VR simulation environment is shown in
Figure 1. The development was based on a patented system that utilizes a standardized “Simulation scenario template for child’s care” (Korean Patent Publication No. 10-2377113, 2022). This system enabled the creation of VR content by defining initial elements such as target learners, learning objectives, and required competencies. This structured approach ensured that the educational content was systematically designed to align with specific learning goals and learners’ educational levels.
2) Scenario content and progression
The developed scenario focused on the nursing care of a 5-year-old male patient, Kim*Byeol, diagnosed with pneumonia who presents with a fever and dry cough. The primary learning objectives were to assess the signs and symptoms of pediatric pneumonia, prioritize and perform appropriate nursing interventions, and identify the essential nursing skills for solving the clinical problems. The simulation was composed of four main stages, with the patient’s condition and lab results (e.g., C-reactive protein 10.95 mg/dL, total calcium 11.1 mg/dL) changing dynamically based on the learner’s actions and predetermined triggers. The scenario began with the initial state in which the learner performed an initial assessment of a relatively stable patient. At this stage, the patient’s vital signs were recorded as body temperature 37.8°C and peripheral oxygen saturation 96%, and the learner was expected to conduct a physical assessment, including auscultation of chest sounds. In the secondary state, the patient’s condition deteriorated, triggering urgent intervention. The vital signs changed to show a rising body temperature of 38.2°C and a drop in oxygen saturation to 90%. This required the learner to prioritize and perform the necessary interventions, such as applying oxygen, administering a prescribed antipyretic (Acetaphen injections), and using a nebulizer with Ventolin and Mucomyst. Following initial stabilization, the third state required the learner to administer the prescribed intravenous antibiotics, specifically Bactacin injections, and perform an antibiotic skin test while continuing to monitor the patient’s vital signs. In the final state, the patient was stable, and the focus shifted to recovery and education. The learner reevaluated the patient’s condition and provided comprehensive discharge education to his parent. This included instructions on medication adherence, the importance of fluid intake, and the application of chest physiotherapy at home. Throughout the simulation, the learner had to engage in therapeutic communication with the patient’s anxious mother, and the scenario featured a branching logic in which timely and appropriate interventions led to a positive outcome.
5. Data Collection Procedure
Data were collected over approximately 7 weeks from April 18 to May 29, 2025, in a classroom at the university. To ensure an unbiased research environment, the intervention was administered by the corresponding author, who had no conflicts of interest (e.g., academic or evaluative role) with the student participants. Twenty senior nursing students were recruited via an online bulletin board announcement, and written informed consent was obtained from all participants after they received a full explanation of the study.
The procedure was structured into three distinct phases (
Table 1). First, in the pretest phase, the participants completed a 15-minute online questionnaire to gather data on general characteristics and establish baseline measures for knowledge, clinical judgment, and simulation effectiveness. Second, in the intervention phase, each participant individually engaged in a 70-minute VR simulation session, which consisted of pre-briefing (20 minutes), the main simulation (30 minutes), and debriefing (20 minutes). To mitigate potential motion sickness, a VR simulation component was administered in two segments. Finally, in the 15-minute posttest phase conducted immediately after the session, the participants anonymously completed a questionnaire to reassess the primary variables and measure user experience. All data were systematically managed to ensure research integrity.
1) Quantitative analysis
All quantitative data were analyzed using IBM SPSS ver. 26.0 (IBM Corp.). The participants’ general characteristics were analyzed using descriptive statistics, including frequencies, percentages, means, and standard deviations. The primary variables—knowledge of nursing care for pediatric pneumonia, clinical judgment, behavioral scores, simulation effectiveness, and simulation usability—were treated as continuous variables. The pre- and post-intervention scores were described using means and standard deviations. The Shapiro-Wilk test was used to assess the normality of the data distribution for the primary outcome variables. The results confirmed that the knowledge, clinical judgment, and simulation effectiveness scores were all normally distributed (p>.05). Therefore, a paired t-test was used to analyze the differences between pre- and post-intervention scores. This test was appropriate for evaluating the mean changes within a single group when the data were normally distributed, with a statistical significance of p<.05.
2) Qualitative analysis
The free-text feedback from participants was first translated into English and subsequently analyzed in a three-stage process: text preprocessing, sentiment and emotion analysis, and visualization and summary. In the preprocessing stage, the text was cleansed by removing unnecessary elements such as special characters and redundancies. The sentences were refined by identifying the root forms of the keywords (lemmatization). Subsequently, a validated natural language processing tool was used to automatically classify each sentence by its sentiment polarity (positive, neutral, or negative) and primary emotions (e.g., joy, sadness, and fear). Polarity was categorized on a 3-point scale, whereas emotional intensity was calculated as a score ranging from 0 to 1 and recorded in a data table. The results of this analysis were aggregated into tables and visualized using bar graphs, histograms, and word clouds to summarize the findings.
7. Ethical Considerations
Beyond formal approval, additional measures were taken to safeguard ethical standards. Participants were recruited voluntarily via an online bulletin board, with explicit assurance of their right to withdraw at any time without penalty. To protect confidentiality, all data were anonymized immediately using unique codes, and identifying information was stored separately in a secure location.
To minimize potential coercion, no faculty members involved in academic evaluation participated in recruitment or intervention. The corresponding author, a doctoral researcher with no prior relationship with the participants, conducted all study procedures. Participant safety was prioritized, with students informed about possible VR-related side effects and advised to stop immediately if discomfort occurred. As a token of appreciation, participants received a 50,000 Korean won mobile gift certificate for stationery.
RESULTS
1. Baseline Characteristics and Pretest Results
The participants comprised 13 females (65.0%) and seven males (35.0%). Their mean age was 23.20±1.32 years. All the participants were senior students, and the most frequent GPA range was 4.0–4.5 (45.0%). At baseline, the mean scores for PNKP, NCJS, and SET-M were 6.60±1.40, 4.15±0.36, and 2.47±0.31, respectively. The Shapiro-Wilk test indicated that the primary outcome variables (PNKP, NCJS, and SET-M) were normally distributed (
p>.05), justifying the use of parametric tests for pre-post comparisons. The baseline characteristics of the participants are presented in
Table 2.
The pre-post analysis indicated no significant change in the participants’ PNKP scores (t=–0.14, p=.893). Conversely, a statistically significant increase was observed in the overall mean NCJS score (t=4.23, p<.001). Specifically, significant improvements were observed in the NCJS subdomains of comprehensive data analysis (t=4.34, p<.001), evidence for intervention (t=2.54, p=.020), and patient-centered nursing (t=3.87, p=.001).
The overall mean score for the SET-M also showed a significant increase (t=2.75,
p=.013). Among the SET-M subdomains, the scores for pre-briefing (t=2.57,
p=.019) and debriefing (t=2.10,
p=.049) increased significantly. The change in the scenario subdomain was not statistically significant (t=1.86,
p=.079). The results of the difference analysis of the primary variables are summarized in
Table 3.
The analysis of the UEQ revealed varied results across its dimensions. Under the attractiveness dimension, the scores for the items “pleasant” and “understandable” were relatively low. Under the perspicuity dimension, the “predictable” item was rated particularly low. By contrast, the “good” item under the efficiency dimension received a comparatively high score. However, under the dependability dimension, the “safe” item was rated relatively low. Furthermore, the “innovative” item (1.75±0.97) under the novelty dimension was rated very close to the “conservative” item (1), indicating that participants perceived the VR program as similar to existing methods instead of viewing it as innovative. Similarly, the “boring” (1) versus “interesting” (7) item under the attractiveness dimension received a score of 1.55±0.89, leaning toward “boring,” which suggested a need for improving engagement. Conversely, under the efficiency dimension, the “good” (1) versus “bad” (7) item obtained a score of 1.75±0.85, in favor of “good.” Additionally, the “difficult” (1) versus “easy” (7) item received a score of 5.15±1.04, leaning toward “easy,” which indicated positive evaluations of efficiency. A detailed breakdown of the UEQ results is presented in
Table 4.
1) Distribution of text sentiment polarity
The analysis of sentiment polarity in the participant responses showed that positive sentiments were the most frequent, accounting for approximately 35 instances (
Figure 2A). This was followed by neutral (approximately 25 instances) and negative (approximately 20 instances) sentiments, suggesting that the participants generally held a positive perception of the VR simulation experience. Positive comments were primarily focused on the realism of the educational content, such as “It was good because it was similar to the actual clinical environment”; however, negative comments tended to relate to usability issues, such as “Movement was difficult, and operation was not easy.”
2) Distribution of primary emotions
The analysis of the primary emotions expressed in the text revealed that “neutral” was the most predominant emotion, with approximately 30 instances (
Figure 2B). “Sadness,” “fear,” and “joy” were present in high proportions, with approximately 15 instances each. By contrast, “surprise” (approximately five instances), “anger” (approximately two instances), and “disgust” (approximately one instance) were minimal. The prevalence of neutral responses indicated that participants often provided objective, factual feedback, such as “I was able to understand the tutorial.” Simultaneously, the results revealed complex emotional states. Feelings of pressure related to learning performance (sadness and fear), as in “I was disappointed that I couldn’t learn properly,” were mixed with positive feelings from a new experience (joy), such as “It was good to experience a new type of simulation.”
3) Distribution of emotion intensity scores
A histogram analysis of the emotion intensity scores revealed a bimodal distribution, with distinct peaks in the 0.3–0.4 range (approximately 6–7 instances) and the 0.9–1.0 range (approximately 9–10 instances) (
Figure 2C). This suggested that participants’ experiences were divided into two distinct types. Neutral experiences with low emotional arousal, such as “I learned the controls without much difficulty,” corresponded to lower intensity scores. By contrast, experiences that elicited a strong emotional reaction, such as “I was flustered when the alarm sounded on the patient monitor,” were associated with high intensity scores. This implied that the intensity of emotion varied according to the participants’ level of immersion and perceived difficulty of the tasks.
4) Word cloud analysis
The word cloud analysis, which visualized key terms from the text data, showed that “patient,” “simulation,” “nursing,” and “VR” were the most prominent words (
Figure 2D). This clearly reflected this study’s central theme of patient care simulation using VR technology. Furthermore, words such as “difficult,” “learn,” “operate,” and “realistic” appeared with high frequency. This finding indicated that the participants’ core perceptions involved the opportunity for a realistic learning experience and the challenges of operating the system. This trend was consistent with the results of the sentiment and emotion analyses.
DISCUSSION
This study demonstrated that, although VR simulation is a promising tool for enhancing nursing students’ clinical judgment and perceived effectiveness, it involves the challenge of translating immersive experiences into measurable knowledge gains. The improvement in practical decision-making skills suggested that VR can effectively supplement clinical training. However, the lack of knowledge gain underscores the need for robust instructional design to ensure cognitive consolidation.
This lack of knowledge gain was likely multifactorial. The intervention—a single session with brief pre-briefing and debriefing periods—might have provided an insufficient dose of content exposure and structured reflections for knowledge acquisition. Educational theory posits that, for experiential learning to consolidate into long-term memory, robust conceptual framing is required before the experience and structured metacognitive reflection afterward [
25]. This aligns with findings from other VR-based education studies, suggesting that multiple exposures are often necessary to achieve significant knowledge improvement [
26]. Furthermore, this finding should be interpreted with caution owing to the nature of the measurement instrument. As the 10-item knowledge test was developed by the researcher and its psychometric properties, such as validity and reliability, were not formally established, its capacity to accurately detect changes in knowledge may be limited. Therefore, to ensure that VR-based learning enhances both practical skills and knowledge, we recommend a three-stage design: comprehensive e-learning to build a conceptual foundation, VR simulation for application, and a final stage of debriefing paired with quizzes to foster consolidation and integration.
In contrast to the findings on knowledge, the VR simulation had a positive influence on learners’ NCJS. The improvement in NCJS scores after the VR session can be interpreted as an enhancement of students’ critical thinking and clinical reasoning abilities gained through the experience of practicing decision-making in virtual patient care scenarios. As VR provided a realistic clinical environment in which students could attempt various judgments without risk, they had several opportunities to consider the rationale for their judgments and practice decision-making in complex situations.
However, conflicting findings have been reported regarding the improvements in clinical judgment. According to Ha et al. [
27], VR education did not show a statistically significant advantage over traditional teaching methods in enhancing critical thinking. This discrepancy may be attributable to factors such as the sensitivity of the assessment instrument, learners’ baseline competency levels, and the specific design of the scenarios. Although the single-group design of this study, which lacked a control group, limited the ability to determine its absolute effectiveness, the magnitude of the pre-post improvement suggested that the VR learning experience was beneficial for developing students’ clinical judgment. Particularly, when accompanied by sufficient feedback and debriefing, VR simulations can demonstrate high potential as a tool for developing clinical judgment.
The significant increase in simulation effectiveness scores indicated an overall improvement in student satisfaction with VR learning. As the SET-M is an instrument that assesses learners’ perceptions of how effective a simulation experience is in achieving learning objectives, the increase in scores signified that students perceived the VR scenario as a “beneficial experience that was helpful for achieving practice objectives” [
22]. The participants most likely experienced an improvement in their nursing performance capabilities through a more immersive VR experience. This finding is consistent with those of previous studies. For example, Choi et al. [
28] reported that VR-based education facilitates learning, with students stating a preference for it and a desire to use it in diverse scenarios.
Thus, introducing a VR simulation led to significant improvements in learning satisfaction and positive perceptions of the learning experience. This can be attributed to VR’s high degree of presence and interactivity, stimulating learning motivation and enhancing a sense of achievement. Taken together, our quantitative results suggested that VR nursing simulation can positively affect multiple learning domains, providing evidence for its use as an innovative pedagogical strategy to enhance learning outcomes in future nursing education.
The data on the experience of using the VR simulation, collected through qualitative analysis, were also largely positive. The results for the UEQ indicated that students generally rated the VR learning system favorably across dimensions such as attractiveness, perspicuity, efficiency, and novelty. This signified that the students perceived VR-based learning as an engaging experience that provided a novel stimulus for their studies. Nursing students who experienced the VR simulation provided positive feedback, with comments such as “It felt like learning in a completely new way” and “I was so immersed it felt like I was caring for a real patient.”
Such positive user experiences are critical factors in enhancing learning effectiveness as they increase learners’ immersion and motivation. Prior studies have also reported that students participating in nursing education using fully immersive VR demonstrated high levels of immersion by learning in realistic situations and showed a generally favorable attitude toward the learning experience. In their qualitative analysis of nursing students’ experiences with a VR simulation, Saab et al. [
29] concluded that “VR learning authentically reproduces real nursing care and is enjoyable, immersive, memorable, and inclusive.” This corroborates our findings that the participants gained a quasi-realistic clinical experience through VR learning and that their overall user experience was favorable.
Furthermore, the sentiment analysis results identified numerous positive affective expressions. For instance, words such as “fun,” “interesting,” and “vivid” were frequently mentioned, demonstrating the enjoyment and satisfaction derived from the VR learning experience. This affective immersion is linked to a sense of learning efficacy, which can be considered a desirable educational outcome.
Despite the positive feedback, the qualitative data revealed areas for improvement, primarily related to usability issues such as operational difficulties and a lack of haptic feedback. These findings highlight key considerations for designing future VR educational programs. Technical aspects such as cybersickness must be mitigated through content optimization to maximize learning effectiveness [
30]. From a pedagogical standpoint, providing a thorough orientation and adaptation period is crucial for reducing learners’ cognitive load and facilitating immersion [
31]. Finally, the instructional design should be iterative, incorporating user feedback to enhance interface intuitiveness and interactivity, such as by incorporating conversational scenarios or multimodal interactions [
32].
Nevertheless, this study has a significant strength in that it illuminated the effects of VR nursing education from multiple perspectives. By objectively measuring changes in learning outcomes through quantitative metrics while integrating qualitative data such as user experience and sentiment analysis, the study provided deep insights into why and how the learning experience was formed. Thus, this study is crucial for its use of a mixed-methods design to elucidate the pedagogical value of VR at multiple levels within a specific clinical context (pediatric pneumonia nursing). The findings provide empirical foundational data for the future development and application of VR educational programs.
However, several limitations must be carefully considered when interpreting and applying these results. First, the single-group pretest–posttest design, which lacked a control group, made it difficult to definitively attribute the observed positive changes solely to the independent effect of the VR program. Second, the instrument used to measure knowledge change was researcher-developed. While the items were reviewed by content experts, formal psychometric testing, such as calculating a content validity index from external experts or assessing internal consistency reliability, was not performed. The lack of established validity and reliability for the knowledge assessment tool is a clear limitation of this study and must be considered when interpreting the results. Therefore, future studies should use fully validated instruments. These limitations highlight the need for future research that incorporates randomized controlled trial designs, utilizes standardized measurement instruments, and conducts longitudinal studies to track the persistence of learning outcomes.
CONCLUSION
This pilot study was conducted to develop a VR nursing simulation program for pediatric patients with pneumonia tailored to the Korean clinical context and validate its effectiveness. The findings indicated that the program yielded positive effects, significantly enhancing nursing students’ clinical judgment and simulation effectiveness. Although the short-term intervention did not lead to a significant improvement in nursing knowledge, it highlighted the necessity of reinforcing future versions of the program with repetitive learning and systematic debriefing. Furthermore, while learners reported high overall satisfaction and immersion, some noted operational challenges, confirming the importance of enhancing the user experience.
Therefore, the significance of this study lies in its empirical confirmation of the pedagogical potential of the VR simulation in Korean pediatric nursing education. It also proposes specific directions for improvement such as reinforcing learning content through repetition, enhancing debriefing, and optimizing the user interface to ensure effective program design and application. Future refinement of the program based on these findings and follow-up studies with more rigorous research designs can help VR simulation become a key pedagogical strategy in nursing education.
ARTICLE INFORMATION
Figure 1.(A–D) View of the actual virtual reality simulation environment.
Figure 2.Sentiment analysis results. (A) Polarity distribution. (B) Primary emotion distribution. (C) Emotion intensity score distribution. (D) Main keywords word cloud.
Table 1.
|
Contents of the VR simulation learning module |
Time (min) |
|
Learning objectives and expected outcomes |
• To assess and interpret pediatric patients’ pneumonia and related signs and symptoms. |
- |
|
• To identify and implement appropriate nursing interventions based on the patient's clinical signs and symptoms. |
|
• To select and prepare essential basic nursing skills required for solving nursing problems. |
|
• To enhance practical clinical competence in the diagnosis, assessment, and management of pediatric respiratory diseases. |
|
• To develop the ability to understand and appropriately respond to the complex symptoms and diagnostic features of pneumonia. |
|
• To cultivate rapid and accurate decision-making skills in response to real-time changes in a patient’s condition. |
|
Phase |
|
|
|
Pre-test |
• Collection of general characteristics: gender, age, year in school, GPA (via self-administered questionnaire): pneumonia knowledge test (10 researcher-developed multiple-choice items based on scenario learning goals); clinical judgment (Nursing Clinical Judgment Scale; 23 items, 5-point Likert; Cronbach’s α=0.88); simulation effectiveness (Simulation Effectiveness Tool-Modified; 19 items, 3-point Likert; Cronbach’s α=0.89) |
15 |
|
PreBriefing stage |
• Orientation on learning objectives (knowledge of pediatric pneumonia, assessment skills, medication administration, communication, patient safety); introduction of patient case (5-year-old male with fever and dry cough); description of simulation setting and equipment: virtual simulator, monitor, PPE, thermometer, IV lines, infusion pump, oxygen kit, medications (e.g., Bactacin, NS, Ventolin, Mucomyst, Acetaphen); explanation of learner roles, time allocation, and debriefing process |
20 |
|
Tutorial |
• Technical orientation to VR environment and device use |
30 |
|
VR simulation practice stage |
• Simulation scenario based on the case of a 5-year-old male (Kim Hanbyeol) diagnosed with pneumonia; patient with persistent fever and dry cough, transferred to ER after initial outpatient visit; simulation includes clinical assessment, nursing decision-making, and intervention; reference data include patient history, radiologic images, and lab results |
30 |
|
Debriefing stage |
• Video-assisted debriefing in small groups (3–8 learners); structured discussion: recall (reaction), analysis, summary, self-reflection, and group discussion; guided questions: what happened, what went well/poorly, what to improve, what was learned; encourages reflection on nursing knowledge, skills, and attitudes; focus on prioritization, judgment, and clinical reasoning |
20 |
|
Post-test |
• Reassessment of pneumonia knowledge (10-item multiple-choice); clinical judgment (NCJS; 23 items, 5-point Likert; Cronbach's α=0.88); simulation effectiveness (SET-M; 19 items, 3-point Likert; Cronbach’s α=0.89); simulation usability (User Experience Questionnaire; 26 items, 7-point semantic differential scale; Cronbach’s α=0.90) |
15 |
Table 2.Baseline characteristics and pre-test scores of primary variables
|
Variable |
No. (%) |
M±SD |
Min |
Max |
Median |
Skew |
Kurt |
Shapiro-Wilk (p) |
|
Sex |
|
|
|
|
|
|
|
|
|
Male |
7 (35.0) |
|
|
|
|
|
|
|
|
Female |
13 (65.0) |
|
|
|
|
|
|
|
|
Age (yr) |
|
23.20±1.32 |
22 |
26 |
23 |
0.75 |
0.38 |
0.94 (<.001) |
|
22–23 |
11 (55.0) |
|
|
|
|
|
|
|
|
24–26 |
9 (45.0) |
|
|
|
|
|
|
|
|
Grade: 4th |
20 (100.0) |
|
|
|
|
|
|
|
|
GPA |
|
|
|
|
|
|
|
|
|
3.0–3.5 |
5 (25.0) |
|
|
|
|
|
|
|
|
3.5–4.0 |
6 (30.0) |
|
|
|
|
|
|
|
|
4.0–4.5 |
9 (45.0) |
|
|
|
|
|
|
|
|
PNKP |
|
6.60±1.40 |
4 |
9 |
7 |
0.03 |
0.79 |
0.95 (.31) |
|
NCJS |
|
4.15±0.36 |
3.48 |
4.83 |
4.13 |
0.21 |
0.48 |
0.97 (.81) |
|
SET-M |
|
2.47±0.31 |
1.95 |
3.00 |
2.42 |
0.34 |
0.82 |
0.95 (.38) |
Table 3.Pre-post comparison of primary variables
|
Variable |
Pre-test |
Post-test |
t (p) |
|
PNKP |
6.60±1.40 |
6.60±1.64 |
–0.14 (.893) |
|
NCJS |
4.15±0.36 |
4.40±0.44 |
4.23 (<.001) |
|
Comprehensive data analysis |
4.21±0.53 |
4.50±0.44 |
4.34 (<.001) |
|
Intervention evaluation and reflection |
4.23±0.46 |
4.32±0.44 |
0.75 (.460) |
|
Intervention evidence |
4.08±0.46 |
4.34±0.46 |
2.54 (.020) |
|
Interprofessional consultation |
4.17±0.63 |
4.40±0.74 |
1.73 (.100) |
|
Patient-centered nursing |
4.00±0.60 |
4.38±0.52 |
3.87 (.001) |
|
Peer nurse collaboration |
4.27±0.59 |
4.40±0.74 |
0.95 (.352) |
|
SET-M |
2.47±0.31 |
2.67±0.31 |
2.75 (.013) |
|
Prebriefing |
2.40±0.45 |
2.70±0.47 |
2.57 (.019) |
|
Scenario |
2.42±0.37 |
2.61±0.41 |
1.86 (.078) |
|
Debriefing |
2.63±0.36 |
2.81±0.29 |
2.10 (.049) |
Table 4.Result of user experience questionnaire
|
Dimension |
Item no. |
Left (1) |
M±SD |
Right (7) |
|
Attractiveness |
1 |
Annoying |
2.65±1.53 |
Pleasant |
|
2 |
Incomprehensible |
2.50±1.53 |
Understandable |
|
3 |
Novel |
2.10±1.86 |
Conventional |
|
4 |
Easy to use |
4.85±1.31 |
Complex to use |
|
5 |
Beneficial |
2.15±1.42 |
Not beneficial |
|
6 |
Boring |
1.55±0.89 |
Interesting |
|
Perspicuity |
7 |
Not fun |
1.55±0.83 |
Fun |
|
8 |
Unpredictable |
4.65±1.14 |
Predictable |
|
9 |
Fast |
2.95±1.00 |
Slow |
|
10 |
Creative |
2.05±1.00 |
Conventional |
|
Efficiency |
11 |
Difficult |
5.15±1.04 |
Easy |
|
12 |
Good |
1.75±0.85 |
Bad |
|
13 |
Complex |
5.30±1.03 |
Simple |
|
14 |
Unpleasant |
2.10±1.17 |
Attractive |
|
Dependability |
15 |
Conventional |
2.70±1.17 |
Modern |
|
16 |
Not enjoyable |
1.95±1.00 |
Enjoyable |
|
17 |
Safe |
2.65±1.27 |
Unsafe |
|
18 |
Stimulating |
2.65±0.93 |
Dull |
|
Stimulation |
19 |
Meets expectations |
2.40±1.47 |
Does not meet expectations |
|
20 |
Inefficient |
2.40±1.14 |
Efficient |
|
21 |
Clear |
2.65±1.14 |
Unclear |
|
22 |
Not practical |
2.50±1.28 |
Practical |
|
Novelty |
23 |
Concise |
4.00±1.26 |
Confusing |
|
24 |
Fascinating |
2.30±1.17 |
Difficult to understand |
|
25 |
Familiar |
3.15±1.04 |
Absolute |
|
26 |
Conservative |
1.75±0.97 |
Innovative |
REFERENCES
- 1. Malya RM, Mahande MJ, Urstad KH, Rogathi JJ, Bø B. Perception of simulation-based education among nursing and midwifery students in Tanzania: a qualitative study. Adv Simul (Lond). 2025;10(1):8. https://doi.org/10.1186/s41077-025-00339-1
- 2. Tong LK, Li YY, Au ML, Ng WI, Wang SC, Liu Y, et al. The effects of simulation-based education on undergraduate nursing students’ competences: a multicenter randomized controlled trial. BMC Nurs. 2024;23(1):400. https://doi.org/10.1186/s12912-024-02069-7
- 3. Alharbi A, Nurfianti A, Mullen RF, McClure JD, Miller WH. The effectiveness of simulation-based learning (SBL) on students’ knowledge and skills in nursing programs: a systematic review. BMC Med Educ. 2024;24(1):1099. https://doi.org/10.1186/s12909-024-06080-z
- 4. Sihnas MA, Sujendran S, Sathaananthan T. The effectiveness of simulation-based learning in nursing education: a review. Int J Sci Res Publ. 2024;14(1):550-555. https://doi.org/10.29322/IJSRP.14.01.2024.p14565
- 5. Yeo JY, Nam H, Park JI, Han SY. Multidisciplinary design-based multimodal virtual reality simulation in nursing education: mixed methods study. JMIR Med Educ. 2024;10:e53106. https://doi.org/10.2196/53106
- 6. Elendu C, Amaechi DC, Okatta AU, Amaechi EC, Elendu TC, Ezeh CP, et al. The impact of simulation-based training in medical education: a review. Medicine (Baltimore). 2024;103(27):e38813. https://doi.org/10.1097/MD.0000000000038813
- 7. Shahzeydi A, Dianati M, Kalhor F. Clinical simulation in nursing students’ safe medication administration: a systematic review. Iran J Nurs Midwifery Res. 2024;29(5):522-529. https://doi.org/10.4103/ijnmr.ijnmr_323_23
- 8. El Hussein MT, Hirst SP. High-fidelity simulation’s impact on clinical reasoning and patient safety: a scoping review. J Nurs Regul. 2023;13(4):54-65. https://doi.org/10.1016/s2155-8256(23)00028-5
- 9. Cheon J, Kim H, Kim DH. Healthcare providers’ communication experience in the pediatric intensive care unit: a phenomenological study. BMC Health Serv Res. 2024;24(1):956. https://doi.org/10.1186/s12913-024-11394-1
- 10. Health Insurance Review & Assessment Service. Research on improvement of reimbursement for expansion of pediatric emergency medical care: final report. Health Insurance Review & Assessment Service; 2020.
- 11. Hong CM, Whang SA. Simulation practice in pediatric nursing: a scoping review. Heal Nurs. 2021;33(2):35-45. https://doi.org/10.29402/HN33.2.4
- 12. Kim E, Song S, Kim S. Development of pediatric simulation-based education: a systematic review. BMC Nurs. 2023;22(1):291. https://doi.org/10.1186/s12912-023-01458-8
- 13. Ayaz O, Ismail FW. Healthcare simulation: a key to the future of medical education: a review. Adv Med Educ Pract. 2022;13:301-308. https://doi.org/10.2147/AMEP.S353777
- 14. Sung H, Kim M, Park J, Shin N, Han Y. Effectiveness of virtual reality in healthcare education: a systematic review and meta-analysis. Sustainability. 2024;16(19):8520. https://doi.org/10.3390/su16198520
- 15. Del Castillo Miranda JC, Oweidat M, Abady E, Shehadeh MH, Aziz MM, Onyejesi CD, et al. Enhancing pediatric emergency care in low-resource settings through simulation-based training: a narrative review. Sage Open Pediatr. 2025;12:30502225251319883. https://doi.org/10.1177/30502225251319883
- 16. Al-Ghareeb AZ, Cooper SJ. Barriers and enablers to the use of high-fidelity patient simulation manikins in nurse education: an integrative review. Nurse Educ Today. 2016;36:281-286. https://doi.org/10.1016/j.nedt.2015.08.005
- 17. Foronda CL, Fernandez-Burgos M, Nadeau C, Kelley CN, Henry MN. Virtual simulation in nursing education: a systematic review spanning 1996 to 2018. Simul Healthc. 2020;15(1):46-54. https://doi.org/10.1097/SIH.0000000000000411
- 18. Jallad ST. Effectiveness of simulation-based education on educational practices of communication skills, satisfaction, and self-confidence among undergraduate nursing students. Creat Nurs. 2025;31(2):135-143. https://doi.org/10.1177/10784535241301115
- 19. Des Jarlais DC, Lyles C, Crepaz N. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. Am J Public Health. 2004;94(3):361-366. https://doi.org/10.2105/ajph.94.3.361
- 20. Whitehead AL, Julious SA, Cooper CL, Campbell MJ. Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res. 2016;25(3):1057-1073. https://doi.org/10.1177/0962280215588241
- 21. Kwon SN, Park H. Development of Nursing Clinical Judgment Scale. J Korean Acad Nurs. 2023;53(6):652-665. https://doi.org/10.4040/jkan.23042
- 22. Leighton K, Ravert P, Mudra V, Macintosh C. Updating the simulation effectiveness tool: item modifications and reevaluation of psychometric properties. Nurs Educ Perspect. 2015;36(5):317-323. https://doi.org/10.5480/15-1671
- 23. Laugwitz B, Held T, Schrepp M. Construction and evaluation of a user experience questionnaire In: Holzinger A, editors. HCI and usability for education and work. Springer; 2008. p. 63-76. https://doi.org/10.1007/978-3-540-89350-9_6
- 24. Schrepp M. User Experience Questionnaire Handbook. UEQ Team; 2017.
- 25. Merkebu J, Veen M, Hosseini S, Varpio L. The case for metacognitive reflection: a theory integrative review with implications for medical education. Adv Health Sci Educ Theory Pract. 2024;29(4):1481-1500. https://doi.org/10.1007/s10459-023-10310-2
- 26. Hamilton D, McKechnie J, Edgerton E, Wilson C. Immersive virtual reality as a pedagogical tool in education: a systematic literature review of quantitative learning outcomes and experimental design. J Comput Educ. 2021;8(1):1-32. https://doi.org/10.1007/s40692-020-00169-2
- 27. Ha YO, Kwon SJ, Kim J, Song JH. Effects of nursing skills practice using VR (virtual reality) on competency and confidence in nursing skills, learning self-efficacy, and satisfaction of nursing students. J Ind Converg. 2022;20(4):47-55. https://doi.org/10.22678/JIC.2022.20.4.047
- 28. Choi J, Thompson CE, Choi J, Waddill CB, Choi S. Effectiveness of immersive virtual reality in nursing education: systematic review. Nurse Educ. 2022;47(3):E57-E61. https://doi.org/10.1097/NNE.0000000000001117
- 29. Saab MM, Landers M, Murphy D, O’Mahony B, Cooke E, O’Driscoll M, et al. Nursing students’ views of using virtual reality in healthcare: a qualitative study. J Clin Nurs. 2022;31(9-10):1228-1242. https://doi.org/10.1111/jocn.15978
- 30. Biswas N, Mukherjee A, Bhattacharya S. “Are you feeling sick?”: a systematic literature review of cybersickness in virtual reality. Acm Comput Surv. 2024;56(11):1-38. https://doi.org/10.1145/3670008
- 31. Poupard M, Larrue F, Sauzéon H, Tricot A. A systematic review of immersive technologies for education: learning performance, cognitive load and intrinsic motivation. Br J Educ Technol. 2025;56(1):5-41. https://doi.org/10.1111/bjet.13503
- 32. Kiourexidou M, Kanavos A, Klouvidaki M, Antonopoulos N. Exploring the role of user experience and interface design communication in augmented reality for education. Technol Interact. 2024;8(6):43. https://doi.org/10.3390/mti8060043
