Present and Future Trends of Virtual Simulation in Education: A Bibliometric Analysis

Literature Review

The evolution of virtual simulations in education reflects a dynamic interplay between technological advancements, pedagogical innovation, and an enhanced understanding of how individuals learn. Emerging in the late 1990s and early 2000s, virtual simulations were initially experimental, grounded in the concept that digital environments could provide safe, repeatable, and controlled settings for learners to practice complex tasks without real-world risks (Steinicke, 2016). Early research, such as that of Akpan (2001) and Bell and Waag (1998), established the theoretical foundation by suggesting that simulations could enhance experiential learning through the replication of real-world scenarios—such as using virtual cadavers for anatomy training in medicine or flight simulators for pilot instruction. As technology progressed, the focus between the 2000s and 2010s shifted toward empirically validating these tools, with studies by (Motola et al., 2013; Rutten et al., 2012) demonstrating that virtual simulations significantly improve engagement, motivation, and skill development across fields from healthcare to STEM education. This research provided robust evidence that simulations could foster inquiry-based learning and enhance critical thinking, problem-solving, and decision-making skills, proving their efficacy beyond traditional methods. The mid-2010s marked a period of rapid technological integration, with advancements in virtual reality (VR) and augmented reality (AR) creating fully immersive environments that deepened learning through emotional and sensory engagement, as highlighted by Riva et al. (2016) and Martín-Gutiérrez et al. (2017). Concurrently, the adoption of artificial intelligence (AI) and machine learning allowed simulations to become more adaptive and personalized, dynamically responding to individual learner needs and offering tailored feedback, as explored by Luan et al. (2020). Recent studies, such as those by Adefila et al. (2020), further illustrated the potential of simulations to facilitate collaborative learning, enabling multiple learners to engage in shared virtual spaces that enhance teamwork and communication skills. Today, the focus is on advancing these capabilities with AI-driven features, such as the intelligent avatars researched by Shah (2023), which serve as personal tutors, providing real-time, interactive support within simulations.

The relationship between virtual simulation technologies can be conceptualized as a “pyramid” structure, as illustrated in Figure 1. High-speed Internet forms the foundation for system sharing. As one ascends the pyramid, the technical levels of virtual simulation, VR, and AR are progressively enhanced, accompanied by an increasing complexity of both software and hardware components. Virtual simulation refers to the use of computer technology, along with specialized software and hardware, to replicate real-world systems (Potkonjak et al., 2016). It typically involves processes such as constructing data models, collecting data, simulating systems, and analyzing results. VR emphasizes the creation of immersive virtual environments, allowing users to experience these environments through devices such as head-mounted displays (Jensen & Konradsen, 2018). VR technology employs tools such as three-dimensional models, VR devices, interactive interfaces, and computer programs to immerse users in a simulated “real” environment, enabling them to freely explore and interact with the surroundings during experiments. AR, on the other hand, integrates virtual information seamlessly with the real world. AR employs a range of technological methods to simulate and overlay computer-generated virtual information onto the real environment (Dargan et al., 2023). These two types of information complement each other, thereby enhancing the user’s perception of the real world.OPEN IN VIEWER

Virtual simulation in education is a rapidly evolving field that harnesses immersive technologies, including VR, AR, and mixed reality, to create dynamic, interactive learning environments that replicate complex real-world scenarios. These technologies offer learners experiential, hands-on opportunities that traditional classrooms often lack, making them particularly effective in fields such as medical training, engineering, environmental science, and business education. Research by Papanastasiou et al. (2019) indicates that these simulations enhance procedural skills, spatial awareness, and decision-making abilities by engaging students in deep, active learning processes. The integration of AI within these simulations amplifies their effectiveness by enabling real-time adaptation to individual learner needs, fostering personalized learning paths that respond to each student’s performance and pace (Frank, 2024). This adaptability aligns with modern pedagogical approaches, including constructivist, problem-based, and inquiry-based models, which emphasize active engagement, critical thinking, and reflection—key components for achieving deeper cognitive involvement and improving knowledge retention (Bakar, 2021). Additionally, the field is focusing on the development of dynamic assessment and feedback mechanisms that provide continuous, context-sensitive feedback, allowing students to correct errors immediately and refine their strategies, thereby promoting self-regulated learning (Munshi et al., 2023). Innovations such as customizable interfaces, audio descriptions, and mobile-friendly applications are also contributing to reducing the digital divide, ensuring more equitable distribution of high-quality educational resources. However, as Alnagrat et al. (2022) point out, the cost and scalability of virtual simulations remain significant challenges, prompting ongoing research into their long-term value and effective integration into curricula. This also includes professional development for educators, who must adapt to new teaching methods while balancing digital engagement with traditional instruction.

Numerous studies have explored various aspects of virtual simulation, including its educational characteristics, applications in nursing education, clinical virtual simulations, and virtual simulation experiments. For example, Shin et al. (2019) conducted an integrative review of studies from databases such as PubMed, Medline, and CINAHL to identify both general and virtual-specific characteristics of virtual simulation in nursing education. Similarly, Foronda et al. (2020) reviewed 80 studies, indicating that 86% of the research supported virtual simulation as an effective pedagogical tool, and emphasized the importance of curricular integration and determining best practices for its implementation. Furthermore, Padilha et al. (2020) highlighted the growing use of clinical virtual simulation as a method for enhancing nurses’ clinical reasoning skills, utilizing a quantitative approach. Virtual simulation experiments, such as the one developed by Hou et al. (2023) for flexible nanochannel membrane materials, provide accessible, interactive platforms that enhance chemistry education by offering safe, cost-effective, and flexible experimental opportunities for both on- and off-campus students, ultimately improving their skills and engagement in scientific research. Despite these contributions, no study to date has specifically addressed the bibliometric analysis of virtual simulation in education. Notably, VOSviewer software is increasingly used as a tool to assist in mapping research activity within this field. Accordingly, this study conducts a bibliometric analysis through quantitative techniques to identify major academic contributions, highlight current and emerging trends, and address gaps in the implementation of virtual simulation in education.

Present Study

This research aims to perform an in-depth analysis of scholarship on virtual simulation in education using a systematic bibliometric approach. The study consisted of two phases: identification of relevant scientific contributions and bibliometric analysis. By employing two innovative bibliometric techniques, the study aims to address a previously identified gap and highlight key areas for current and future investigation (Li et al., 2023). The specific objectives of this study, guided by the bibliometric results, are to:

(1) Identify current topics in virtual simulation in education literature through co-citation analysis.

The content of this piece is organized as follows. The first portion outlines the importance of virtual simulation in education, as well as the research’s motivations and aims. The second part explains the approach utilized for bibliometric evaluation and data collecting. The third portion offers the results and comments. The fourth section discusses the implications and future trends. Finally, the fifth section presents the conclusions, limitations, and future research avenues.

Bibliometric Approach

Bibliometrics is a sophisticated quantitative tool that applies statistical and mathematical methods to the analysis of academic literature, uncovering complex patterns, relationships, frequencies, and trends within scientific publications (Zupic & Čater, 2015). Through the detailed examination of citation counts, authorship patterns, and keyword frequencies, bibliometrics offers a comprehensive understanding of research impact, productivity, and the evolving landscape of research topics. Borgman and Furner (2002) suggest that this approach not only provides valuable insights into the dynamics of scientific knowledge and academic communication but also aids in identifying emerging trends and influential works, thereby shaping the future trajectory of scholarly research.

Co-citation is a bibliometric methodology that examines the relationships between scientific publications by analyzing how frequently two or more articles are cited together. The presence of a co-citation relationship between publications signifies an association and mutual influence between them. This method is essential for identifying leading authors, institutions, and journals within a specific field by revealing the connections between scholarly works (Fellnhofer, 2018). In the context of education, co-citation analysis allows researchers to identify key elements or aspects of virtual simulation, understand how they are interrelated, and determine which studies are particularly influential within the field. Furthermore, Co-citation analysis is a valuable methodological approach for identifying research fronts within a discipline. In the context of virtual simulation in education, co-citation analysis enables the identification of clusters of scholarly activity, providing insights into the foundational works driving current research directions and the emerging areas of interest.

Co-word analysis is a methodology used to explore the co-occurrence of keywords within a body of literature, making it an effective tool for identifying emerging research trends. By examining the patterns of keywords that frequently appear together in recent publications, researchers can gain valuable insights into the evolving landscape of a specific topic (Wider et al., 2023). In the context of education, applying co-word analysis to studies on virtual simulation provides a comprehensive understanding of the current state of research and helps forecast potential future directions. This approach enables researchers to remain at the forefront of developments by identifying emerging topics and areas of interest that are likely to influence the future trajectory of the field (Kumar & Srivastava, 2023). Thus, co-word analysis not only helps to map the existing knowledge structure but also serves as a strategic guide for future research efforts, ensuring that scholars contribute to the continuous advancement of virtual simulation in education.

Research Design and Data Collection Procedure

To ensure comprehensive coverage, this search encompassed all available WoS core collection databases and was conducted up to September 2, 2024, with the aim of capturing a broad range of publications from the database’s inception to the specified date. The selection of the WoS database for this study highlights its reputation for extensive coverage and high-quality resources, making it an exemplary source for bibliometric analyses. This approach guarantees a holistic overview of global research trends, as the database provides comprehensive insights into significant research outputs (Ezugwu et al., 2021). For the search process, the analysis focused on the topic field within the database, employing a string of keywords related to virtual simulation in education. The search terms combined the phrases: (“virtual simulat*”) AND (“educat*” OR “teach*”). This search strategy was designed to capture a wide range of relevant studies centered on virtual simulation technology in education.

In terms of inclusion and exclusion criteria, the study focused on articles and conference proceedings published in English, thereby narrowing the scope to research accessible to an English-speaking academic audience. The bibliometric analysis included a critical evaluation phase, during which 982 papers were identified as meeting the predefined criteria. The data were collected in CSV and TXT formats and subsequently analyzed using Microsoft Excel for further verification. Duplicate entries and keywords were removed following the cross-checking method proposed by Zupic and Čater (2015). These selected papers then underwent further analysis using VOSviewer 1.6.20, a software tool renowned for its ability to visualize and analyze complex bibliometric data. Tools like VOSviewer allow researchers to navigate and interpret the intricate landscape of academic publications, offering insights into the thematic and intellectual structures that underpin the field of virtual simulation in education. In this study, both the co-citation method and the keyword co-occurrence method employ the full counting approach. These methods are implemented with a citation threshold of 12, an attraction parameter set to 1, a repulsion parameter set to −1, and a layout optimized for network visualization.

Descriptive Analysis

The WoS database returned a total of 10,278 citations for the selected 982 papers, excluding self-citations (9121). The average citation count per paper was 10.47, with an H-index of 48. Figure 2 depicts the publication trends and citation counts from 2000 to September 2, 2024. Given that the first publication in this field appeared in 2000, it is interesting that significant contributions to this field were not recognized until 2002. Nonetheless, there was a significant exponential increase in the number of publications between 2007 and 2023. The observed significant increase indicates a growing interest in research on the application of virtual simulation in education. These findings suggest a potential rise in research on virtual simulation in education in the future.OPEN IN VIEWER

Co-Citation Analysis

Among the 982 publications examined, a co-citation analysis was conducted based on their cited references. Using a citation threshold of 12, 61 cited sources were selected for further investigation. Table 1 lists the top ten co-cited references, sorted by overall link strength. Specifically, the work of Foronda et al. (2020) received 74 citations, Padilha et al. (2019) received 51 citations, and Committee (2016b) received 39 citations.

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Table 1.

The Highest Ten Papers in Terms of Co-Citation

No	Papers	Citation	Overall Link Strength
1	Foronda, C. L., Fernandez-Burgos, M., Nadeau, C., Kelley, C. N., & Henry, M. N. (2020). Virtual simulation in nursing education: a systematic review spanning 1996 to 2018. Simulation in Healthcare, 15(1), 46–54	74	362
2	Padilha, J. M., Machado, P. P., Ribeiro, A., Ramos, J., & Costa, P. (2019). Clinical virtual simulation in nursing education: randomized controlled trial. Journal of medical Internet research, 21(3), e11529	51	256
3	INACSL Standards Committee. (2016). INACSL standards of best practice: SimulationSM simulation design. Clinical simulation in nursing, 12, S5–S12	39	209
4	Cobbett, S., & Snelgrove-Clarke, E. (2016). Virtual versus face-to-face clinical simulation in relation to student knowledge, anxiety, and self-confidence in maternal-newborn nursing: A randomized controlled trial. Nurse Education Today, 45, 179–184	34	207
5	Sapiano, A. B., Sammut, R., & Trapani, J. (2018). The effectiveness of virtual simulation in improving student nurses’ knowledge and performance during patient deterioration: A pre and post test design. Nurse education today, 62, 128–133	30	190
6	Hayden, J. K., Smiley, R. A., Alexander, M., Kardong-Edgren, S., & Jeffries, P. R. (2014). The NCSBN national simulation study: A longitudinal, randomized, controlled study replacing clinical hours with simulation in prelicensure nursing education. Journal of Nursing Regulation, 5(2), S3-S40	37	182
7	Gu, Y., Zou, Z., & Chen, X. (2017). The effects of vSIM for Nursing™ as a teaching strategy on fundamentals of nursing education in undergraduates. Clinical Simulation in Nursing, 13(4), 194–197	20	153
8	Shin, H., Rim, D., Kim, H., Park, S., & Shon, S. (2019). Educational characteristics of virtual simulation in nursing: An integrative review. Clinical Simulation in Nursing, 37, 18–28	27	132
9	Lioce, L., Lopreiato, J., Downing, D., Chang, T. P., Robertson, J. M., Anderson, M., … & Terminology and Concepts Working Group. (2020). Healthcare simulation dictionary. Rockville, MD: Agency for Healthcare Research and Quality, 2020, 20–0019	24	131
10	INACSL Standards Committee. (2016). INACSL standards of best practice: SimulationSM debriefing. Clinical simulation in nursing, 12, S21–S25	24	130

The study of co-citations reveals five unique clusters, each with a distinctive subject. Figure 3 displays the network model based on the listed publications. These clusters group papers that share thematic similarities, with each cluster visually represented by networks of the same color (Solih et al., 2024). Within each cluster, points of varying sizes are depicted, with larger points indicating a greater collaborative contribution of the corresponding article. Table 2 displays a summary of the co-citation analysis, including clustering amount and color, tags, number of papers, and representative papers. The classification and explanation for each cluster are listed below.OPEN IN VIEWER

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Table 2.

Co-Citation Clusters Related to Virtual Simulation in Education

Cluster	Cluster Tag	Number of Papers	Representative Papers
1 (Red)	The role of virtual simulation in nursing education	17	Chen et al. (2020); Padilha et al. (2019); Padilha et al. (2018); Coyne et al. (2021); Gu et al. (2017); Sapiano et al. (2018); Cobbett and Snelgrove-Clarke (2016); Kim et al. (2021); Foronda et al. (2018); Kim et al. (2021); Jeffries (2005); Jeffries et al. (2015); Wright et al. (2018)
2 (Green)	The impact of virtual simulation in health professions education	13	Cant and Cooper (2014); Kononowicz et al. (2019); Cook et al. (2010); Barry Issenberg et al. (2005); Cook et al. (2011); Seymour et al. (2002); Caylor et al. (2015); Foronda et al. (2014); Farra et al. (2013); Perry et al. (2015); McGaghie et al. (2010); Cook and Triola (2009)
3 (Blue)	Virtual simulation is a modern learning method	13	Cant et al. (2019); Cant & Cooper (2017); Committee (2016a); Eppich & Cheng (2015); Kolb (2014); Lioce et al. (2020); Page et al. (2021); Rudolph et al. (2014); Shin et al. (2015); Watts et al. (2021)
4 (Yellow)	Transformative potential of virtual simulations as a teaching tool	10	Braun & Clarke (2006); Duff et al. (2016); Verkuyl et al. (2016, 2017b, 2017a, 2018a, 2018b, 2018c, 2019); Zigmont et al. (2011)
5 (Purple)	Virtual simulation promotes talent cultivation	8	Alexander et al. (2015); Foronda et al. (2020); Aebersold (2018); Fogg et al. (2020); Foronda et al. (2020); Haerling (2018); Sullivan et al. (2019); Hayden et al. (2014)

Source: Author interpretation from VOSviewer.

Cluster 1 (Red): With 17 publications, this cluster focuses on the topic of “The role of virtual simulation in nursing education.” Virtual simulation is revolutionizing nursing education by providing a flexible and effective method for developing clinical skills, enhancing knowledge retention, and improving student self-confidence. Research consistently demonstrates its value: Chen et al. (2020) found that virtual simulations significantly improve learning outcomes by allowing students to practice hands-on skills in a controlled, risk-free environment, fostering critical thinking and decision-making. This finding aligns with studies by Padilha et al. (2019, 2018) and Coyne et al. (2021), who emphasize that virtual simulations immerse students in realistic, interactive scenarios that closely replicate real patient care, thereby deepening engagement and enhancing the application of theoretical knowledge in practical settings. Further support comes from Gu et al. (2017) and Sapiano et al. (2018), who found that tools like vSIM for Nursing enable students to master foundational skills at their own pace, accommodating individual learning styles and promoting self-directed learning. Moreover, Cobbett and Snelgrove-Clarke (2016) highlight that virtual simulations can reduce anxiety and improve self-confidence compared to traditional face-to-face methods—crucial factors in high-pressure situations such as managing patient deterioration. During the COVID-19 pandemic, as noted by Kim et al. (2021), virtual simulations were essential in maintaining educational continuity, offering a solution for clinical training despite social distancing measures and lockdowns. However, the transition to virtual learning also revealed challenges, including technological barriers such as unreliable internet access and limited device availability, as well as concerns about the lack of physical patient interaction, which some students felt might not fully prepare them for real-world practice (Foronda et al., 2018; Kim et al., 2021). To address these challenges, the integration of virtual and traditional methods is recommended by the NLN Jeffries Simulation Theory (Jeffries, 2005; Jeffries et al., 2015), which advocates aligning simulation types with specific learning objectives to optimize their effectiveness. This blended approach leverages the strengths of virtual simulations—such as the ability to repeat complex scenarios and provide self-paced learning—while preserving the essential tactile and interpersonal experiences offered by traditional methods. Wright et al. (2018) underscore the importance of a robust technological infrastructure and adequate faculty support to enhance the effectiveness of virtual simulations. High-quality, realistic scenarios, combined with comprehensive faculty training, can help bridge the perceived gap between virtual and real-world training environments.

Cluster 2 (Green): Comprising 13 publications, this cluster revolves around the topic of “The impact of virtual simulation in health professions education.” These works explore how various virtual simulation technologies—such as virtual patient platforms, 3D VR environments, and web-based simulations—enhance learning outcomes, improve clinical skills, foster interprofessional collaboration, and prepare healthcare professionals for real-world practice. Virtual simulation is transforming health professions education by providing highly immersive and targeted training experiences that closely align with the demands of modern healthcare. Cant and Cooper (2014) highlight the impact of web-based simulations in nursing, offering a scalable and accessible complement to traditional methods, while Kononowicz et al. (2019) and Cook et al. (2010) emphasize the effectiveness of virtual patients in enhancing diagnostic accuracy, clinical reasoning, and decision-making skills. High-fidelity simulations, as examined by Barry Issenberg et al. (2005), offer opportunities for repeated practice and immediate feedback, essential for refining both procedural and cognitive competencies. Systematic reviews by Cook et al. (2011) further demonstrate that these simulations lead to significant improvements across cognitive, psychomotor, and affective domains, fostering better knowledge retention, clinical performance, and professional behaviors. The findings of Seymour et al. (2002) underscore this impact by showing that VR-based surgical training significantly enhances operating room performance, directly translating simulated learning into clinical expertise. Beyond individual skill development, virtual simulations are critical for cultivating interprofessional collaboration and communication, essential in healthcare environments where teamwork and multidisciplinary approaches are paramount. Caylor et al. (2015) provide evidence that integrating virtual simulations with TeamSTEPPS training improves team dynamics and communication among healthcare professionals, while Foronda et al. (2014) illustrate how virtual clinical simulations strengthen nursing students’ communication skills, which are vital for patient interactions and coordinated care. The ability of these simulations to replicate complex clinical settings allows learners to refine both technical and non-technical skills, deepening their understanding of teamwork and collaborative problem-solving. Additionally, the flexibility of virtual simulations enables them to be tailored to the unique learning requirements of various health professions. Farra et al. (2013) demonstrate how 3D VR simulations enhance disaster response training by developing situational awareness and rapid decision-making, while Perry et al. (2015) highlight their application in dental education to improve procedural accuracy and clinical judgment. McGaghie et al. (2010) further reveal that virtual platforms like Second Life can effectively foster critical thinking and clinical judgment in nursing students. This adaptability ensures that simulations remain relevant across diverse educational contexts, supporting a shift toward experiential, learner-centered education that moves beyond passive knowledge acquisition to active, applied learning in a risk-free environment. As argued by Cook and Triola (2009), the future of medical education lies in leveraging these advanced simulation technologies to create comprehensive, effective, and engaging learning experiences that build critical thinking, adaptability, and clinical competence.

Cluster 3 (Blue): Including 13 publications, this cluster focuses on “Virtual simulation is a modern learning method.” This research focuses on how virtual simulation integrates theoretical foundations, empirical evidence, and best practice standards to create an effective, immersive learning environment. Virtual simulation in healthcare and nursing education represents an innovative pedagogical approach that combines these elements to enhance educational outcomes. Central to this approach is the development of a shared language and conceptual clarity, as highlighted by Cant et al. (2019), who emphasize the necessity of standardized terminology to avoid ambiguity and ensure consistent communication across diverse educational settings. Supporting this effort, the “Healthcare Simulation Dictionary” by Lioce et al. (2020) provides precise definitions for the varied terms used in simulation, fostering a unified understanding among educators, researchers, and practitioners. This clarity is essential for aligning simulation practices with Kolb (2014) experiential learning theory, which emphasizes learning through direct experience, engaging learners in active experimentation, reflection, and conceptualization—processes that closely mirror real-world clinical scenarios. The theoretical alignment with practice is further reinforced through the structured approaches outlined by the Committee (2016a), which provides comprehensive guidelines for simulation design, facilitation, and debriefing. These standards ensure that simulations are consistently learner-centered, goal-oriented, and contextually relevant, enabling meaningful engagement and skill development. The PEARLS framework developed by Eppich and Cheng (2015) complements these standards by integrating various debriefing methods to promote reflective learning and critical thinking, thereby bridging the gap between simulated scenarios and real-world practice. Additionally, Rudolph et al. (2014) stress the importance of psychological safety in simulation settings, advocating for pre-simulation briefings that create a supportive environment where learners feel free to explore, make mistakes, and learn without fear of judgment. Empirical studies corroborate the effectiveness of these approaches: Cant and Cooper (2017) provide a systematic review demonstrating that simulation-based learning significantly enhances clinical competencies and decision-making skills in nursing education, while Shin et al. (2015) offer meta-analytic evidence confirming its positive impact on clinical performance. The methodological rigor of this research is further supported by the PRISMA 2020 guidelines presented by Page et al. (2021), which promote transparency and consistency in reporting systematic reviews. Watts et al. (2021) build on this foundation by advocating for continuous updates to simulation design standards, ensuring their alignment with evolving educational goals, learner needs, and technological advancements.

Cluster 4 (Yellow): Comprising 10 publications, this cluster titled “Transformative potential of virtual simulations as a teaching tool.” The literature emphasizes the importance of both thoughtful simulation design and diverse debriefing strategies in creating meaningful, interactive, and impactful learning experiences. Virtual simulation in education represents a transformative shift from traditional, lecture-based methods to an experiential, interactive learning approach that is enhanced by structured debriefing processes. Central to this shift is the methodological framework developed by Braun and Clarke (2006), whose thematic analysis allows researchers to extract deep, qualitative insights into student experiences and engagement with virtual environments. Duff et al. (2016) demonstrate how virtual simulations improve diagnostic reasoning by providing opportunities for repeated, risk-free practice of clinical decision-making. Building on this, Verkuyl et al. (2016, 2017a, 2017b, 2018a, 2018b, 2018c, 2019) showcase the adaptability and effectiveness of virtual gaming simulations in developing specific nursing skills, underscoring the importance of user-centered design to ensure these tools are engaging, accessible, and aligned with learners’ needs. Their work extends beyond simulation design to explore the critical role of debriefing—a process that links the virtual experience with meaningful learning outcomes. Zigmont et al. (2011) propose the 3D model of debriefing (Defusing, Discovering, Deepening), offering a structured approach to reflection and knowledge consolidation. Verkuyl and colleagues further expand on this model by comparing various debriefing methods, including self-debriefing, virtual debriefing, and in-person debriefing, revealing that each approach offers unique benefits that can be tailored to different educational contexts and learner preferences. Their research suggests that combining self-reflection with group discussion optimizes learning outcomes by fostering both individual insight and collaborative knowledge-building.

Cluster 5 (Purple): Cluster 5 comprises 8 publications titled “Virtual simulation promotes talent cultivation.” This cluster is closely connected to others, illustrating how virtual simulation is revolutionizing education by offering innovative, scalable, and effective learning experiences that address key challenges in training competent professionals. The development and integration of virtual simulation have been supported by comprehensive frameworks, such as the “NCSBN Simulation Guidelines for Prelicensure Nursing Programs” (Alexander et al., 2015), which ensure consistency and quality across diverse educational settings. Over the past two decades, virtual simulation has evolved from a novel concept to a credible and mainstream educational approach, as demonstrated by Foronda et al. (2020), who highlight its growing acceptance and pedagogical strengths. By creating risk-free, immersive environments where students can repeatedly practice skills and receive immediate feedback, virtual simulation fosters experiential learning and bridges the gap between theoretical knowledge and practical application (Aebersold, 2018; Fogg et al., 2020). This approach accommodates diverse learning styles by offering multimodal engagement, thereby enhancing knowledge retention and application. Technological advancements, such as virtual and augmented reality, have further amplified the effectiveness and accessibility of virtual simulations, creating realistic, high-fidelity learning experiences that are not limited by the logistical constraints of traditional clinical placements (Foronda et al., 2020). Haerling (2018) also underscores the cost-effectiveness of virtual simulation, noting that although initial investments in technology may be substantial, the long-term benefits of scalability, repeatability, and reduced dependence on physical resources make it a sound economic choice. Comparative studies by Sullivan et al. (2019) and Hayden et al. (2014) reveal that virtual simulation can match or even surpass the effectiveness of traditional clinical training methods, supporting a blended approach in which simulation complements real-world practice. This hybrid model mitigates the variability and unpredictability of clinical placements, ensuring equitable learning opportunities for all students.

Co-Occurrence of Keyword

The dataset included at least 12 instances for each of the 62 determined keywords. The keyword “virtual simulation” was found to be the most frequently used, occurring 310 times, followed by “education” with 244 occurrences and “simulation” with 178 occurrences. Following the co-word analysis, the highest 15 keywords are listed in Table 3.

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Table 3.

The Highest 15 Keywords in Co-Word Analysis

No	Keyword	Occurrences	Overall Link Strength
1	Virtual simulation	310	735
2	Education	244	715
3	Simulation	178	589
4	Virtual reality	145	383
5	Nursing education	105	373
6	Skills	74	333
7	Students	79	316
8	Performance	59	252
9	Reality	59	182
10	Care	41	173
11	Nursing	38	160
12	Nursing students	40	159
13	Impact	35	144
14	Training	32	135
15	Surgery	34	133

Applying the same database, Figure 4 depicts the network structure of keyword co-occurrence, revealing four distinct clusters. The keywords within each cluster exhibit strong correlations, indicating that these terms represent concentrated areas of related research and potential future research hotspots. The four clusters are assigned the appropriate labels per the author’s inductive interpretation. Each cluster was carefully examined and expanded upon as follows:OPEN IN VIEWER

Cluster 1 (Red): Comprising 20 terms, this cluster is defined “Enhancing skills, confidence, and experiences of students.” Virtual simulation has emerged as a pivotal component in education, fundamentally transforming the development of essential skills, critical thinking, and decision-making abilities. By leveraging technologies such as VR, AR, and serious games, virtual simulations provide highly realistic and interactive scenarios that replicate the complexities of real-world environments. For example, a virtual simulation may place a nursing student in a high-fidelity scenario involving the management of a deteriorating patient in a critical care unit, requiring rapid assessment, prioritization of care, execution of advanced procedures, and effective communication with a virtual healthcare team (Goldsworthy et al., 2022). These scenarios are intentionally designed to challenge students and provide repeated practice in a safe, controlled environment, allowing them to build confidence and competence without compromising safety. For example, the opportunity to engage with diverse clinical cases, including rare or complex conditions not often encountered in traditional clinical placements, significantly enhances the learning experience and prepares students for a broader range of patient scenarios. Research by Lavoie et al. (2022) consistently demonstrates that virtual simulations improve learning outcomes by enhancing clinical judgment, accelerating decision-making processes, and increasing knowledge retention. Moreover, integrating serious games into these simulations introduces motivational elements such as scoring, progression levels, and real-time feedback, which foster engagement and reinforce learning through repeated practice and problem-solving (Ravyse et al., 2017). Gordon (2017) emphasizes that a crucial aspect of virtual simulation is the debriefing process, during which students reflect on their performance and receive constructive feedback from educators. These debriefing sessions encourage critical self-assessment and understanding, helping students identify areas for improvement and solidifying their learning. The COVID-19 pandemic has accelerated the adoption of virtual simulations, with studies such as Ravichandran and Mahapatra (2023) concluding that VR enhances student engagement, motivation, and practical skills acquisition through immersive simulations. Despite challenges such as high costs and technical limitations, many educational institutions have integrated virtual simulations into their curricula as a core component of a blended learning strategy.

Cluster 2 (Green): Comprising 17 terms, this cluster focuses on “Enriching medical and surgical training.” Virtual simulation is fundamentally transforming medical and surgical training by providing immersive, highly specific learning environments that enhance both technical and non-technical skills. Unlike traditional methods, which often limit trainees to observe or practice procedures in controlled clinical settings, these technologies offer dynamic, competency-based training where complex procedures can be rehearsed repeatedly in a risk-free environment. For instance, VR simulations allow trainees to engage in realistic exercises such as laparoscopic surgeries, endoscopic procedures, or robotic-assisted interventions, refining crucial skills like hand-eye coordination, precision, depth perception, and spatial orientation (Mariani et al., 2020). Meanwhile, AR overlays digital information onto the physical world, enhancing anatomical visualization and providing real-time guidance during practice by displaying correct surgical pathways or identifying critical structures (Tang et al., 2018). This dual approach enables trainees not only to master core technical skills but also to develop essential non-technical competencies, such as situational awareness, crisis management, teamwork, and communication, which are vital for effective decision-making under pressure in real surgical scenarios. Virtual simulation technologies are increasingly integrated into surgical curricula to provide a personalized, adaptive learning experience tailored to each trainee’s skill level and progress. Advanced computer-based simulations utilize machine learning algorithms to analyze performance data in real-time, dynamically adjusting task difficulty to ensure trainees progress from basic to advanced procedures steadily (Vaughan et al., 2016). This feedback mechanism helps identify specific areas of weakness, such as inconsistent suturing techniques or delayed response times during emergencies, allowing for targeted practice sessions that address these gaps. In specialized fields like neurosurgery, validated virtual simulators enable repeated practice of intricate procedures, such as aneurysm clipping or microvascular decompression, under diverse conditions and anatomical variations, without the risks associated with live surgery (Suri et al., 2016). These simulations replicate the tactile feedback and dynamic tissue responses experienced during real operations, further enhancing the realism and effectiveness of the training.

Cluster 3 (Blue): Cluster 3 consists of 13 terms titled “Developing and exploring interdisciplinary applied research.” This cluster highlights the potential of interdisciplinary applied research in virtual simulation for education as a transformative methodology, combining technological advancements with collaborative learning across diverse fields. The primary focus is on optimizing these simulations to enhance interprofessional education by fostering critical competencies such as communication, teamwork, and decision-making under pressure. For example, studies explore how scenario-based virtual simulations help nursing students coordinate care in complex situations, such as managing a multi-trauma patient in an emergency room or responding rapidly to a deteriorating patient in critical care (Rushton et al., 2020). These simulations are specifically designed to meet learning objectives that promote understanding of roles. Additionally, this research investigates the capacity of virtual simulations to enhance student safety by providing a controlled, risk-free environment for repetitive practice of high-stakes procedures. This includes evaluating various simulation technologies, such as VR and AR, to create immersive learning experiences that improve cognitive and psychomotor skills, enhance knowledge retention, and boost decision-making confidence. Further interdisciplinary studies focus on tailoring these simulations to address diverse learning styles and individual needs, offering adaptive learning experiences with personalized feedback. This adaptability proves particularly significant in engineering education, where customized learning pathways can bridge knowledge gaps and prepare students for professional practice (Alam & Mohanty, 2023). Moreover, virtual simulations promote interprofessional education by enabling students from different disciplines to collaborate in shared virtual spaces, thereby refining communication, teamwork, and conflict-resolution skills critical in today’s multidisciplinary environments. A meta-analysis by Zhang et al. (2023) of 14 empirical studies confirms that virtual simulations significantly improve collaborative outcomes, identifying key factors such as the number of professional disciplines involved and the duration of interventions. Smaller interdisciplinary teams and extended intervention periods were shown to yield the most favorable results.

Cluster 4 (Yellow): Comprising 12 terms, cluster 4 is defined “Innovating virtual simulation teaching technology.” It focusing on the integration of advanced virtual simulation tools—such as VR, AR, and interactive e-learning platforms—into education to address the specific challenges of developing competencies and enhancing learning outcomes. These technologies are reshaping traditional education models by providing highly immersive, realistic, and flexible learning environments tailored to the complex demands. Virtual simulations allow students to practice intricate procedures, such as performing root canal treatments, suturing wounds, or conducting laparoscopic surgeries, in risk-free, controlled settings that closely mimic real-world conditions. For example, VR platforms can simulate complex surgical procedures where students visualize anatomical structures in three dimensions, manipulate virtual instruments, and receive real-time feedback, enabling them to develop the fine motor skills and spatial awareness necessary for precision in clinical practice (Tomlinson et al., 2019). AR further enhances these experiences by overlaying digital information onto real-world environments, offering visual cues during procedures—such as indicating the depth and location of cavities in dental treatments or guiding the correct placement of implants (Ochandiano et al., 2022). These immersive technologies not only allow for repeated practice and skill mastery but also promote scenario-based and gamified learning approaches. For instance, through a mixed-methods case study involving 83 participants, the study by Baxter and Hainey (2024) demonstrates that immersive technologies significantly enhance student engagement and facilitate the visualization of course materials. Christopoulos and Mystakidis (2023) believe that the appeal of virtual simulation gamification often lies in its ability to make education and learning more attractive. Beyond improving educational outcomes, these technologies also contribute to sustainability by reducing dependence on traditional resources, like expensive materials and complex preparation. Additionally, virtual simulation tools democratize access to high-quality education through globally accessible, cloud-based platforms, ensuring that students from diverse backgrounds and regions receive equitable training opportunities.

Table 4 summarizes the co-word analysis of virtual simulation in education, including clustering amount and color, tags, total number of terms, and representative terms.

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Table 4.

Most Frequent Keywords in Each Cluster

Cluster	Cluster Tag	Number of Terms	Representative Terms
1 (Red)	Enhancing skills, confidence, and experiences of students	20	“assessment,” “clinical simulation,” “covid-19,” “debriefing,” “design,” “experience,” “experiences,” “health-care,” “learning,” “nursing education,” “nursing students,” “outcomes,” “perceptions,” “reality,” “satisfaction,” “self-confidence,” “serious games,” “simulation training,” “standards,” “virtual simulation”
2 (Green)	Enriching medical and surgical training	17	“augmented reality,” “competence,” “computer simulation,” “curriculum,” “model,” “neurosurgery,” “performance,” “skills,” “surgery,” “surgical education,” “surgical simulation,” “system,” “training,” “validation,” “validity,” “virtual reality simulation,” “virtual simulator”
3 (Blue)	Developing and exploring interdisciplinary applied research	13	“care,” “communication,” “education,” “interprofessional education,” “knowledge,” “nurses,” “nursing,” “patient,” “patient safety,” “patient simulation,” “simulation,” “students,” “virtual”
4 (Yellow)	Innovating virtual simulation teaching technology	12	“dental education,” “e-learning,” “environment,” “experimental teaching,” “higher education,” “impact,” “medical-education,” “science,” “simulations,” “technology,” “virtual reality,” “virtual simulation technology”