Introduction

Modern science has extensively studied the most vital scientific methods in education, particularly in academic institutions, aiming to achieve the highest levels of knowledge, technology, and skills in both theoretical and practical aspects ( 1 ). In this regard, the concept of the Metaverse has drawn significant attention from higher education institutions and scholars as an emerging technology ( 2 ). In academic literature, the Metaverse is conceptualized as a world where virtual and real elements influence each other, evolving socially, economically, and culturally to create value ( 3 ). It is a dynamic, immersive, and interconnected 3D virtual space where individuals can interact with one another and digital content in real-time. The Metaverse incorporates Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) to deliver a multifaceted learning experience ( 4 ). This technology aims to create unique, interconnected networks supported by innovative digital technologies, allowing users to immerse themselves in shared experiences. It is built on a complex 3D virtual world, and users—represented as avatars—can interact and socialize without boundaries ( 5 ). The Metaverse is utilized for various purposes, including social networking, online gaming, education, and training. It can be used to create virtual worlds that mirror the real world or design new and imaginative environments. According to scholars, one of the most important future applications of the Metaverse will be in education and learning ( 6 ). Figure 1 represents the Metaverse’s application in anatomy education.

Figure 1. Metaverse-based anatomy education ( 7 )

The growing demand for educational environments in the Metaverse has led to its emergence as a distinct form of learning that goes beyond traditional methods ( 7 , 8 ). In contrast, ongoing discussions focus on the benefits and challenges of the Metaverse in education and learning ( 9 ). Its application in medical education presents both opportunities and challenges. Within the Metaverse, students can explore scientific concepts, delve into historical events, and interact with cultural artifacts in impossible ways in the physical world. Notably, the practicality of Metaverse technology sets it apart from other virtual and augmented reality experiences ( 10 ).

Additionally, the Metaverse has the potential to offer students a more immersive, personalized, and interactive learning experience that surpasses what is achievable with traditional e-learning ( 11 ). However, challenges remain. Due to the high level of user freedom, platform administrators cannot fully anticipate all user actions, and the anonymity of the Metaverse increases the risk of cybercrimes ( 5 ). Another major challenge is cost. Creating interactive and immersive environments that accurately simulate real medical scenarios relies on advanced and often expensive technology ( 12 ). Furthermore, access to education remains a significant challenge, particularly in developing countries. Limited access to technological resources can negatively impact the quality of education ( 13 ).

However, the development of the Metaverse is still in its early stages ( 14 ). Despite the increasing hype surrounding the concept, its practical application in education remains limited ( 15 ). Much of the existing research on educational Metaverse is primarily theoretical, with insufficient empirical studies. A literature review on educational Metaverse ( 9 ) highlights a significant research gap regarding its application in education, especially given its constantly evolving nature. This gap is particularly evident in the diverse ways different audience and academic fields might utilize the Metaverse ( 16 ). Previous studies have explored various aspects of Metaverse adoption, but a comprehensive review integrating well-established adoption theories and models to understand better this domain has yet to be conducted ( 17 ). Despite these challenges, this study focuses on the strengths and potential of the Metaverse in medical education.

This study seeks to bridge this gap by systematically aggregating, synthesizing, and analyzing qualitative research on the applications of the Metaverse in education, with a particular focus on medical training. By examining existing studies, this research aims to provide a comprehensive and structured overview of how the Metaverse is utilized in medical education, identifying potential advantages. Furthermore, the findings of this study will serve as a valuable resource for researchers, educators, and policymakers by offering evidence-based insights into the effectiveness of Metaverse-based learning. Understanding the pedagogical implications of the Metaverse is crucial for developing strategic policies, designing robust educational frameworks, and ensuring that this technology is harnessed effectively to enhance medical training. Given the evolving nature of digital learning environments, a more integrated and analytical approach to studying the role of the Metaverse in education is essential. This research contributes to closing the gap in international studies and lays a foundation for future empirical investigations that can further refine and optimize the integration of Metaverse technologies in education.

Methods

This systematic review and qualitative meta-synthesis employed a meta-aggregation approach developed by the Joanna Briggs Institute (JBI). Conducted between September 2000 and March 2025, the study followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( 18 ).

Search Strategy

The review began with an initial, limited search in Medline and CINAHL, followed by a text-word analysis of the titles, abstracts, and index terms used to describe the articles. Subsequently, a more comprehensive search was conducted across multiple databases, including MEDLINE, CINAHL, Web of Science, Cochrane Library, Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, Scopus, PubMed, and Google Scholar, using all identified keywords and index terms. Finally, the reference lists of all selected reports and articles were examined to identify any additional relevant studies. Only studies published in English were included in this review, with a restriction on the publication date. The following is a list of keywords and operators used.

("Metaverse" AND "Medical Education") OR ("Virtual Reality" AND "Healthcare Training" AND "Education") OR ("Augmented Reality" AND "Medical Simulation" AND "Education") OR ("Extended Reality (XR)" AND "Clinical Learning" AND "Education") OR ("Immersive Technology" AND "Medical Training" AND "Education").

Inclusion and exclusion criteria

► The following were the inclusion criteria for the study:

a. Design: qualitative or a review study design

b. Language: English

c. Topic Relevance: Focus of the studies on using Metaverse in medical education

d. Time Frame: studies published between 2000 and 2025

► The exclusion criteria included:

a. Design: studies that did not use a qualitative or a review study approach

b. Study types: conference papers, editorials, letters, or general-comment articles

c. Language: studies published in neither English

d. Studies for which we could not get the full text or the data collection and analysis methods were not reported.

Figure 1 presents the PRISMA diagram to illustrate the filtering of articles in different sections (title, abstract, and full text).

Study Section

Three investigators (A-K, H-M, Z-T) independently screened all records, reviewing titles and abstracts to exclude studies that did not meet the inclusion criteria. Full-text articles were then assessed to identify those eligible for analysis. In cases of disagreement, a fourth researcher (F-D) was consulted to facilitate discussion and achieve consensus.

Assessment of Methodological Quality

Four reviewers evaluated the methodological validity of the retrieved qualitative research papers using the JBI Qualitative Critical Appraisal Checklist, which consists of 10 items designed to assess the appropriateness of the methodological approach, the application of methods, and the representation of the participants' voices in the studies. Each criterion was rated on a three-point scale: ‘yes,’ ‘no,’ or ‘unclear.’ Papers with fewer than six ‘yes’ ratings were excluded to ensure quality. Any disagreements between two reviewers were resolved through discussion, with a third reviewer consulted if necessary to reach a consensus. In Table 1, the characteristics of the selected studies are presented.

The selected studies shown in Table 1 present a diverse range of perspectives on integrating Metaverse technologies in education, spanning various countries, methodologies, and research focuses. The findings highlight key benefits such as enhanced immersive learning experiences, interactive engagement, and competency-based education. However, technological infrastructure limitations, ethical concerns, accessibility issues, and privacy risks are also frequently noted. Table 2 presents the quality assessment results of the articles.

Data Extraction and Synthesis

General characteristics of the included studies were extracted to better understand the literature, including author(s) name, regions, research objects, research methods, phenomena of interest, and main research results. The JBI meta-aggregation approach was used to extract and synthesize the data ( 45 ). The meta-aggregation approach is philosophically grounded in pragmatism and Husserlian transcendental phenomenology. Its alignment with pragmatism is evident in its objective of generating comprehensive statements, known as "lines of action," to guide decision-making in clinical practice and policy development ( 46 ). As a result, this approach avoids reinterpretation of original research findings and does not focus on theory generation. Instead, all findings or themes were presented exactly as they appeared in the original studies, without modification. To ensure thorough familiarity with the data, three reviewers (CW, DB, and HG) carefully re-read each included study. A three-step process was then implemented to synthesize the qualitative findings. First, all concluding findings from each included paper were extracted. Next, these findings were categorized based on thematic similarity, with at least two findings per category. Finally, the categorized findings underwent meta-synthesis to produce a comprehensive synthesized results. To assess the reliability of each finding, three reviewers independently evaluated the degree of congruence between the findings and the supporting data, assigning a credibility score based on the following criteria: unequivocal, credible, or unsupported. A finding was classified as unequivocal if its alignment with the supporting data was beyond reasonable doubt, credible if there was some ambiguity in the association and unsupported if the data failed to substantiate the finding. Only unequivocal and credible findings were included in the final synthesis, while unsupported findings were presented separately. However, in this study, no unsupported finding was identified.

Data Validation

In this study, the constant comparison method was employed as a key approach to ensure the credibility of qualitative data analysis. This iterative process continuously compared new data with previously collected information to identify the patterns, similarities, and differences. Initially, key concepts and categories were derived from the data, and as additional data were integrated, these categories were refined, merged, or expanded based on emerging insights. This approach ensured that the findings remained grounded in the data and allowed for the systematically developing themes that accurately reflected the studied phenomenon. By maintaining a dynamic and ongoing comparison throughout the research process, this method enhanced the reliability and depth of the qualitative analysis, ultimately contributing to the validity of the study conclusions​ ( 47 ).

Ethical Considerations

Throughout all stages of the present research, integrity was strictly upheld in citing sources and utilizing their findings.

Results

A total of 27 papers were included in this study. Figure 2 shows the literature screening process and results.

Figure 2. Literature screening process

Study Characteristics and Quality of Studies

The demographic characteristics of the reviewed studies varied significantly, reflecting a diverse range of methodologies, participant groups, and geographic distributions. Many studies employed systematic literature reviews, conceptual analyses, and theoretical frameworks, focusing on the broader implications of Metaverse-based education rather than primary data collection. However, some studies incorporated empirical research methods, engaging the participants from specific demographic groups, such as university students, educators, subject matter experts, and medical trainees.

Regarding geographical distribution, the studies originate from various regions, including China, Taiwan, India, Turkey, Iran, the United States, the United Kingdom, and European nations, showcasing a global perspective on Metaverse adoption in education. While some studies analyzed higher education institutions, others focused on specialized fields like emergency medicine, medical informatics, and digital learning ecosystems. The sample sizes also varied, with some studies involving large-scale systematic reviews of hundreds of articles, while others engaged smaller participant groups through structured interviews, phenomenological analyses, and mixed-method approaches. Additionally, age groups were predominantly within the higher education and professional training range (18-40 years old), reflecting the primary audience for emerging educational technologies. These demographic insights underscore the need for further cross-cultural, interdisciplinary, and inclusive research to assess the broader societal impact of Metaverse integration in education.

Meta-Aggregation

In total, seven main themes were identified. The first theme, emerging technologies in medical education, included 23 key concepts. The second theme, virtual learning environments, comprised 58 key concepts. The third theme, intelligent teaching methods, consisted of 32 key concepts. The fourth theme, personalized learning, was categorized under 10 key concepts. The fifth theme, expanding interactive spaces in medical education, included 29 key concepts. The sixth theme, intelligent assessment mechanisms for students, encompassed 12 key concepts. The seventh theme, developing clinical skills and medical decision-making, was classified under 9 key concepts. Finally, developing medical strategies and foresight included 9 key concepts. Table 3 presents the content analysis results of the selected articles.

The Table comprehensively categorizes key themes in medical education, focusing on emerging technologies, interactive learning environments, and intelligent teaching methodologies. The most frequently mentioned concepts include virtual reality (VR), augmented reality (AR), artificial intelligence (AI), metaverse-based simulations, and adaptive learning technologies. The Table also highlights the significance of personalized learning paths, data-driven assessment mechanisms, and interdisciplinary collaboration. This frequency of topics suggests a growing emphasis on technological integration in medical education to enhance practical skill development, decision-making, and real-time clinical simulations. The distribution of research references across different themes indicates a diverse scholarly interest in how these technologies are being adopted in real-world applications. Examining the Table reveals a strong focus on immersive and interactive learning strategies. Concepts like gamification, personalized avatars, and virtual patient interactions underscore the importance of making medical education engaging and student-centered. The application of AI in analyzing patient data for diagnosis and decision support represents a major innovation, indicating how machine learning and predictive analytics are transforming traditional medical training. Moreover, collaborative learning in the metaverse, where students engage in team-based emergency response simulations, suggests a shift toward multidisciplinary training, preparing medical students for real-world clinical environments. Automated assessments and real-time feedback mechanisms show an increasing reliance on data-driven education models that enhance learning efficiency.

Integrating blockchain for academic record management, cloud computing for resource sharing, and AI-powered learning data analysis signal a move toward intelligent and decentralized education systems. Automating administrative tasks—such as student enrollment, certification, and evaluation—indicates an effort to reduce bureaucratic inefficiencies and enhance digital learning ecosystems. The focus on metacognitive learning strategies, inquiry-based approaches, and scenario-based training aligns to foster critical thinking and lifelong learning skills in medical students. Finally, the exploration of multimodal learning environments and digital twins suggests a future where education is hyper-personalized, allowing students to navigate flexible, competency-based learning pathways in real and simulated medical settings. In Figure 3, the results are quantitatively visualized, presenting the frequency of key concepts categorized under each main theme. This visualization clarifies how frequently different concepts appear across the analyzed studies, highlighting the dominant themes and their relative significance in the research.

Figure 3. Visualization of the frequency of key concepts based on main themes

The visualization illustrates the frequency of key concepts mentioned in the context of medical education and interactive learning environments. The most frequently mentioned concept, "Emerging Technologies in Medical Education", indicates the growing emphasis on integrating advanced technologies like AI, VR, and AR into medical training.

Discussion

The growing influence of artificial intelligence (AI) in medical education has emerged as a pivotal tool for training a new generation of healthcare professionals and enhancing the overall quality of education and research ( 48 - 51 ). AI plays a significant role in transforming educational structures and methodologies. At the same time, the metaverse, as a groundbreaking and disruptive innovation, is poised to reshape the future of medical education. Its deep and revolutionary impact is expected to lead education toward digitalization, more advanced information delivery, and the modernization of teaching and learning processes ( 52 ). The present study aims to explore and analyze qualitative research focusing on the applications of the metaverse in medical education. Based on the findings, seven main themes have been identified and categorized as the core areas of application. In the following sections, these themes are analyzed in more detail, with a particular focus on the most prominent and influential concepts within each category.

Synthesized finding 1: The Use of Emerging Technologies in Medical Education

The emergence of immersive learning environments has had a profound impact on educational practices, significantly enhancing engagement, active participation, and accessibility to learning resources ( 53 ). In the field of medicine, Virtual Reality (VR) is widely used to simulate real-life clinical scenarios and provide interactive learning experiences. It enables learners to acquire and practice knowledge and skills within a safe and controlled environment ( 54 ). Augmented Reality (AR) is also applied in healthcare design and education to deliver interactive and informative experiences. This technology can integrate textual data, simulations, and interactive 3D models into the learning process, allowing users to engage with content in a more immersive and stimulating way ( 55 ). Mixed Reality (MR) offers another dimension by enabling users to interact with virtual objects in real-world contexts, creating more realistic and engaging experiences. MR can be used effectively in education, remote collaboration, and virtual prototyping ( 56 ). These advanced technologies play a vital role in advancing the medical profession. Immersive 3D computer-based systems assist medical professionals in analyzing various clinical conditions and comparing healthcare data. Incorporating simulation-based learning in the early stages of medical education enables institutions to provide learners with a deeper understanding of clinical procedures and decision-making processes ( 57 ). The findings related to this theme are consistent with the studies conducted by Chua ( 19 ), Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Chamola, et al. ( 22 ), Joshi & Pramod ( 23 ), Camilleri & Camilleri ( 24 ), Imannezhad, et al. ( 26 ), Ghaempanah, et al. ( 28 ), Zhang, et al. ( 6 ), Said ( 29 ), Cai, et al. ( 30 ), Gündüz & Sincar ( 31 ), Alfiras, et al. ( 32 ), Avcı & Akgül ( 33 ), Çalışkan & Maya ( 34 ), Dhillon & Tinmaz ( 35 ), Ahmad, et al. ( 36 ), Tekin, et al. ( 38 ), Roy, et al. ( 40 ), and Mahmoud ( 42 ).

Synthesized finding 2: Virtual Learning Environments

One of the most prominent applications of the metaverse in medical education is gamification of the learning process. The use of games in this context has demonstrated significant educational benefits, with virtual simulations proving highly effective in enhancing learning outcomes. Research also shows that educational games, mobile applications, and virtual patient simulations can boost the learners’ motivation and interest, while also providing a structured environment for practicing deliberate clinical reasoning skills ( 58 ). Another notable application of the metaverse is learning data analysis (LA). As a rapidly evolving field, LA focuses on the collection, measurement, analysis, and reporting of data related to learners and their educational environments, with the goal of optimizing both the learning experience and the learning ecosystem itself ( 59 ). learning data analysis can promote meaningful learning, serve as an additional resource for instruction, facilitate instructor feedback, and enable ongoing assessment of students’ performance and progress ( 60 ). The metaverse also supports the use of storytelling in medical education. Storytelling is a powerful tool for fostering a sense of community and connection, especially in postgraduate medical education. Moreover, it can serve as an effective approach to strengthening resilience among medical trainees. Digital storytelling allows for the translation and dissemination of healthcare knowledge to diverse audience, including caregivers, patients, policymakers, and healthcare professionals. This method also creates opportunities for interdisciplinary collaboration ( 61 ). By integrating storytelling into learning, students can share their experiences, receive peer feedback, and engage in a more collaborative and socially driven learning process ( 62 ). The findings of a study demonstrated that when properly designed to match user needs, metaverse-based educational environments did not present a barrier to learning but instead acted as a catalyst that enhanced comprehension and engagement with complex medical content ( 63 ). Another important application of the metaverse is its ability to facilitate blended learning environments. In such environments, a significant portion of learning takes place outside traditional classroom settings. The appeal of blended learning lies in its flexibility and ability to adapt to the diverse needs of learners ( 64 ). In one study, the comparative advantage of VR-based metaverse teaching over conventional simulation—namely in cost, scalability, and physical space requirements—suggests it is a viable alternative for broad adoption in medical institutions ( 65 ). This approach has been successfully applied in a variety of anatomical subjects such as histology, shoulder and knee anatomy, brainstem, musculoskeletal and cardiovascular systems, radiologic anatomy, and head, face and neck structures. In most cases, blended learning has yielded positive and satisfactory outcomes, indicating that combining in-person instruction with blended methods can significantly enhance active and effective learning across a range of educational domains. Overall, blended learning has shown a positive impact on the educational process ( 66 ). The findings related to this theme align with previous studies conducted by Chua ( 19 ), Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Chamola, et al. ( 22 ), Kryvenko & Chalyy ( 25 ), Braguez, et al. ( 26 ), Ghaempanah, et al. ( 28 ), Zhang, et al. ( 6 ), Said ( 29 ), Cai, et al. ( 30 ), Alfiras, et al. ( 32 ), Avcı & Akgül ( 33 ), Çalışkan & Maya ( 34 ), Ahmad, et al. ( 36 ), Tekin, et al. ( 38 ), Bernardes, et al. ( 39 ), Roy, et al. ( 40 ), Lin, et al. ( 41 ), Mahmoud ( 42 ), Fitria & Simbolon ( 43 ), and de la Asuncion, et al. ( 44 ).

Synthesized finding 3: Intelligent Teaching Methods

Another significant application of the metaverse in medical education is the adoption of Competency-Based Medical Education (CBME). The core objective of this approach is to ensure that learners develop comprehensive clinical competencies required for effective performance in real-world healthcare settings, rather than focusing solely on acquiring isolated theoretical knowledge or skills ( 67 ). CBME emphasizes continuous and accurate self-assessment of learners' progress and is designed to foster lifelong learning skills ( 68 ). As a learner-centered model, CBME offers greater flexibility in timing and simultaneously targets all three domains of learning: knowledge, skills, and attitudes. Implementing this model necessitates a restructuring of teaching and learning activities, as it prioritizes learning outcomes and prepares students for real clinical environments. Therefore, learners are expected to engage more actively in hands-on practice, clinical experience, and competency-based tasks to bridge the gap between theory and professional practice ( 69 ). Another prominent use of the metaverse in medical education is its support for simulation-based learning. Over the past decade, the integration of simulations into medical training and assessment has grown significantly ( 70 ). This technology provides learners with opportunities to develop and refine both technical and non-technical skills in a realistic, safe, and controlled environment, thereby enhancing their readiness for real-life clinical situations ( 71 ). Numerous studies have demonstrated that high-fidelity simulations significantly improve skill acquisition and retention compared to traditional teaching methods ( 70 ). A large-scale study in the United Arab Emirates examined the perceptions of 833 dental students regarding the use of the Metaverse versus traditional platforms like Zoom. The findings revealed that user mobility and accessibility were the key predictors of Metaverse adoption in educational contexts ( 72 ). These findings highlight the growing importance of simulation as a key component of modern medical education, contributing not only to higher-quality learning but also to reducing clinical errors. The findings related to this theme align with a wide range of prior studies, including those by Chua ( 19 ), Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Chamola, et al. ( 22 ), Kryvenko & Chalyy ( 25 ), Imannezhad, et al. ( 26 ), Braguez, et al. ( 27 ), Ghaempanah, et al. ( 28 ), Zhang, et al. ( 6 ), Said ( 29 ), Cai, et al. ( 30 ), Alfiras, et al. ( 32 ), Avcı & Akgül ( 33 ), Çalışkan & Maya ( 34 ), Ahmad, et al. ( 36 ), Tekin, et al. ( 38 ), Bernardes, et al. ( 39 ), Roy, et al. ( 40 ), Lin, et al. ( 41 ), Mahmoud ( 42 ), Fitria & Simbolon ( 43 ), and de la Asuncion, et al. ( 44 ).

Synthesized finding 4: Personalized Learning

One of the core and most valuable applications of the metaverse in medical education is personalized learning. This approach is rooted in the principle of adapting educational experiences to the individual needs, goals, preferences, and learning pace of each learner. Rather than designing a one-size-fits-all system that passively aligns with static user characteristics, personalized learning requires a flexible structure that responds dynamically to the learner’s evolving choices and objectives ( 73 ). The motivation for personalized learning stems from cognitive-social learning theories and social constructivism, where learning is viewed as a meaningful and active process of building knowledge based on prior experiences. Furthermore, based on the principles of intrinsic motivation—where learners make choices driven by interest—and self-determination theory, which emphasizes autonomy and competence, personalized learning fosters deeper, more meaningful, and transferable educational experiences. This is especially crucial in professional healthcare training and in promoting lifelong learning ( 74 ). In this context, a variety of educational models are employed to scaffold the learning process and strengthen the learner’s individual competencies. By personalizing the time, place, and pace of learning, learners gain increased flexibility and autonomy, allowing them to take an active role in shaping their educational pathways. The metaverse plays a crucial complementary role in this process by offering tools for tracking, managing, and analyzing the learning journey, while also enabling access to advanced informational resources ( 75 ). The findings associated with this theme align with prior research by Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Chamola, et al. ( 22 ), Camilleri & Camilleri ( 23 ), Kryvenko & Chalyy ( 25 ), Imannezhad, et al. ( 26 ), Ghaempanah, et al. ( 28 ), Zhang, et al. ( 6 ), Gündüz & Sincar ( 31 ), Alfiras, et al. ( 32 ), Dhillon & Tinmaz ( 35 ), Roy, et al. ( 40 ), and Lin, et al. ( 41 ).

Synthesized finding 5: Expanding Interactive Spaces in Medical Education

Interactivity and communication networking represent another vital and prominent application of the metaverse in medical education. In the 21st century, co-creation of knowledge and collective problem-solving for complex challenges have become top priorities in driving innovation and development ( 76 ). In this evolving context, traditional educational and collaborative structures are being replaced by Living Labs and Makerspaces—environments that value open dialogue, exchange of ideas, collaboration, and hands-on experimentation just as much as theoretical knowledge in shaping innovative solutions. Within such ecosystems, interdisciplinary and transdisciplinary approaches have become integral to technological innovation cycles, helping to bridge the gap between academic research, industry, and education. By enabling shared, interactive virtual environments, the metaverse fosters broad-based networking and collaboration among students, educators, professionals, and other stakeholders across various domains. In parallel, collaborative learning has emerged as a central feature of modern education. This approach involves students working together in small groups (typically two to five members) to achieve shared learning goals ( 77 ). A study utilizing the Second Life metaverse implemented a 6-week interuniversity radiology competition involving over 100 student teams. The gamified environment enabled collaborative learning through weekly tasks and tests on radiologic anatomy and semiology, with overall positive feedback from participants ( 78 ). In many constructivist-based models, such as Problem-Based Learning (PBL) and Inquiry-Based Learning, learners are encouraged to engage in problem-solving, project design, or group research, which promotes teamwork, critical thinking, and meaningful engagement with learning materials ( 79 ). Decades of empirical research have shown a positive correlation between collaborative learning and key educational outcomes, including academic achievement, motivation, effort, and persistence ( 80 , 81 ). Following the disruptions caused by the COVID-19 pandemic, a study in the United Arab Emirates explored the students’ perceptions of metaverse systems as a remote alternative for medical training. The research highlighted the growing acceptance and feasibility of using immersive platforms when traditional, face-to-face instruction was restricted ( 82 ). The metaverse offers a powerful platform to support these forms of learning in virtual yet highly interactive, flexible, and inclusive environments, allowing even geographically distant learners to collaborate in real time within a shared educational space. Findings related to this theme are consistent with previous studies by Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Chamola, et al. ( 22 ), Camilleri & Camilleri ( 24 ), Kryvenko & Chalyy ( 25 ), Braguez, et al. ( 27 ), Said ( 29 ), Alfiras, et al. ( 32 ), Avcı & Akgül ( 33 ), Çalışkan & Maya ( 34 ), Ahmad, et al. ( 36 ), Talan & Kalinkara ( 37 ), Tekin, et al. ( 38 ), Bernardes, et al. ( 39 ), Roy, et al. ( 40 ), Lin, et al. ( 41 ), Mahmoud ( 42 ), Fitria & Simbolon ( 43 ), and de la Asuncion, et al. ( 44 ).

Synthesized finding 6: Intelligent Assessment Mechanisms for Students

Assessment is arguably one of the most crucial and transformative domains in which Artificial Intelligence (AI) has demonstrated significant potential to reshape educational practices—particularly in medical education. Unlike traditional methods, AI-powered assessment moves beyond static testing formats and relies on the analysis of complex data, learner behaviors, and the processes behind learning itself. As such, AI has the potential not just to enhance but even to replace conventional assessment models, ushering in a fundamental shift in educational tools, strategies, and goal-setting frameworks ( 83 ). By leveraging AI-based tools, instructors and educators are able to allocate more time to core teaching and mentoring activities, as administrative and routine assessment tasks can be handled automatically. Moreover, AI technologies enable faster and more accurate feedback, facilitating the more effective implementation of formative assessments throughout the term. This, in turn, supports a more focused and responsive path toward achieving learning outcomes ( 84 ). In this context, Mirchi, et al. ( 85 ) introduced a practical model that combines AI with simulation-based medical education. They developed a virtual operational assistant capable of delivering automated, real-time feedback to medical students based on specific performance criteria. Using a formative learning framework, this system integrates virtual reality and artificial intelligence to classify learners according to their skill-based performance, providing them with targeted feedback to support continuous improvement. These advancements demonstrate that AI is not only a tool for more precise assessment but also a catalyst for active and effective learning. In the near future, it is expected that AI-driven educational systems will be widely adopted across medical training programs to build responsive, intelligent, and adaptive learning environments. The findings of this theme are in the same line with studies by Wu & Ho ( 20 ), Hwang & Chien ( 21 ), Joshi & Pramod ( 23 ), Kryvenko & Chalyy ( 25 ), Ghaempanah, et al. ( 28 ), Zhang, et al. ( 6 ), Alfiras, et al. ( 32 ), Ahmad, et al. ( 36 ), and de la Asuncion, et al. ( 44 ).

Synthesized finding 7: Developing Medical Strategies and Foresight

The use of artificial intelligence (AI) in decision-making stands as one of the most fundamental and impactful applications of this technology throughout its development. AI systems, particularly in medicine, can serve as supportive tools for human decision-makers, or, in some cases, fully replace human decision-making processes ( 86 ). In recent years, a new wave of AI systems with advanced predictive capabilities has emerged. These systems not only significantly improve the accuracy of predictions but also drastically reduce the cost associated with decision-making and data analysis ( 87 ). In a mixed-methods study involving nursing students, a metaverse-based career mentoring program significantly improved the mentees’ career decision-making self-efficacy, while also fostering realistic, open communication between mentors and mentees ( 88 ). Such advancements have created an ideal foundation for the broader integration of AI into complex medical decision-making. For example, in a study conducted by Loftus and colleagues, the researchers explored the applications of AI in clinical decision-making, with a particular focus on surgery. The findings indicated that integrating AI into surgical decision-making has the potential to fundamentally transform patient care. This includes several key improvements: Enhancing the accuracy and confidence in decisions to proceed with surgery, Improving the informed consent process for patients, Identifying and mitigating modifiable risk factors before surgery, Supporting optimized post-operative care planning, and facilitating more effective shared decision-making regarding the use of medical resources ( 89 ). In a study, immersive virtual and augmented environments, implemented through stereoscopic vision glasses, have been used to simulate clinical settings, enabling students to engage in realistic diagnostic and procedural training ( 90 ). In another study, over 490 virtual cases were completed by medical students, with results showing improved confidence and preparedness to manage real-world scenarios, especially when technical issues were minimized ( 91 ). In a study conducted in South Korea, a metaverse-based handoff simulation program using the ZEPETO platform was developed for senior nursing students in a pediatric intensive care context. The program successfully improved students’ handoff self-efficacy, demonstrating the value of immersive, patient-centered communication training in virtual settings ( 92 ). These insights clearly demonstrate that AI plays a transformative role not only in data analysis but also in elevating the quality of clinical decision-making. Looking ahead, the integration of AI into clinical reasoning education within medical training programs will likely contribute to the development of physicians who can make more precise, informed, and effective decisions by leveraging intelligent technologies. The findings of a study revealed that nurses scored significantly higher than students across all subdomains of metaverse experience, suggesting that prior clinical exposure and self-efficacy levels may mediate the effectiveness of immersive learning tools ( 93 ). The findings related to this theme align with previous research by Wu & Ho ( 20 ), Joshi & Pramod ( 23 ), Tekin, et al. ( 38 ), and Bernardes, et al. ( 39 ).

Limitations and Future Directions

Despite the rigorous design of this metasynthesis and adherence to the JBI metaaggregation methodology, several limitations should be acknowledged. Firstly, the inclusion criteria were restricted to studies published in English, which may have introduced a language bias and excluded potentially relevant findings published in other languages. Secondly, one notable limitation is the limited generalizability of qualitative studies, as their findings are often context-specific and derived from small, non-representative samples. This restricts the extent to which the results can be applied to broader populations or different educational settings. Using a quantitative approach, future researchers are encouraged to conduct a quasiexperimental intervention study to evaluate the effectiveness of metaverse-based education on clinical performance and decision-making skills of medical students compared to traditional instructional methods. From a qualitative perspective, a phenomenological study is recommended to explore the lived experiences of medical students in metaverse-based learning environments, focusing on their perceptions of interaction, cognitive engagement, and meaningful learning. Finally, employing a mixedmethods approach, it is suggested that researchers should design an exploratory study that first identifies the factors influencing metaverse technology acceptance from the students’ perspective (qualitative phase), and then tests the proposed acceptance model using structural equation modeling (SEM) in the quantitative phase. By creating fundamental transformations in various aspects of medical education, artificial intelligence has reshaped the face of this field.

Conclusions

Based on the findings of this qualitative meta-synthesis, it is evident that the integration of emerging technologies—particularly the metaverse and artificial intelligence—has initiated a fundamental transformation in medical education. These technologies enable the creation of immersive, interactive, competency-based, and personalized learning environments, which, compared to traditional methods, significantly enhance student engagement, conceptual understanding, clinical skills development, and decision-making abilities. The systematic review of the literature highlights that areas such as intelligent assessment, simulation-based training, interactive learning, and competency-based education present the greatest potential for applying metaverse technologies. However, effective utilization of these capabilities requires robust technological infrastructure, the development of educational policies aligned with digital innovation, and comprehensive training for educators and instructional designers. Given challenges such as high implementation costs, ethical considerations, and unequal access to technology, it is essential to adopt more precise strategic and policy approaches. Overall, the findings of this study offer a roadmap for researchers, policymakers, and academic institutions aiming to advance medical education in the digital era, and lay the groundwork for future empirical research on the effective deployment of the metaverse in health professions education.

Acknowledgment

The authors gratefully acknowledge colleagues who assisted in reviewing the qualitative findings of this meta synthesis article.

Conflict of Interest

The authors have no conflicts of interest to declare.

Declaration of AI

Artificial intelligence–based language tools were utilized to assist with translation, improve textual structure, and enhance overall clarity during the preparation and editing of this manuscript. All outputs produced by these tools were carefully evaluated, critically revised, and fully approved by the authors.

Authors’ Contributions

ZT was responsible for the literature search and article retrieval from the electronic databases. HM conducted the screening and selection of studies, including the critical appraisal of the included articles. AK performed the data analysis and synthesis, theme identification, and interpretation of the findings. FD wrote the first draft of the manuscript, including the introduction, tables/figures, and conclusion. All authors contributed to the critical revision of the manuscript for important intellectual content, approved the final version, and agree to be accountable for all aspects of the work.

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