Immersive Extended Reality (I-XR) in Medical and Nursing Education: A Systematic Review and Pedagogical Directives

1. Introduction

Simulation-based training is a pedagogical approach that employs simulated scenarios or environments to improve clinical skills, mainly among health field workers. The objective is to replicate real-world situations to provide a realistic and immersive experience without exposing patients to real risks (Becker & Hermosura, 2019). Simulation-based training complements conventional teaching methods, focusing on improving skill acquisition, decision-making, and teamwork (Ajemba et al., 2024). Such clinical experiences can help to create an opportunity for students to apply didactic content to clinical practice which allows students to bridge knowledge, skills, and competency (Hultquist & Bradshaw, 2016).

Halsted's "see one, do one, teach one" model, while groundbreaking in 1890, is now criticized for prioritizing rapid experience over deliberate skill development and patient safety (Ayub, 2022; Baskaran et al., 2023). This approach raises concerns about inadequate preparation, potentially leading to medical errors and a lack of emphasis on critical thinking and self-reflection (Rohrich, 2006). Modern medical education is shifting towards comprehensive training models that prioritize patient safety and incorporate simulation, deliberate practice, and feedback (Ayub, 2022; Baskaran et al., 2023).

Simulation-based medical education (SBME) (Saleem & Khan, 2023) offers a viable solution to bridge this gap. SBME enables deliberate practice and standardized training in a safe learning environment. It incorporates a wide array of techniques, ranging from basic mannequin-based simulations and standardized actor-patients to complex multimedia scenarios (Shah et al., 2022; Ziv, 2003). Through SBME, students can build confidence and competence, and ultimately enhance patient safety and the overall quality of care.

SBME has three primary objectives: (1) the execution of clinical skills, (2) supervised practice of that skill, and ultimately, (3) the independent and confident performance of clinical skills. This educational approach extends beyond technical proficiency to encompass the development of exteroceptive awareness in aspiring professionals. Exteroceptive awareness, the ability to perceive and interpret external stimuli, is crucial for stress management, confidence building, and overall well-being (Baskaran et al., 2023). Specifically, cultivating exteroceptive awareness helps regulate the sympathetic nervous system and manage stress responses in high-pressure situations, such as surgical procedures, thereby enabling healthcare professionals to maintain composure and perform optimally.

Specifically, significant positive effects for SBME (vs. non-simulated methods) have been shown for theory knowledge, analytic skills, learning interest and understanding, satisfaction, cooperative ability, problem-solving ability, teaching success, and situation awareness (Su & Zeng, 2023). Additionally, SBME proves effective for assessing teamwork and communication among healthcare providers (Dodson et al., 2023; Sezgin & Bektas, 2023). These benefits translate into improved patient outcomes and reduced healthcare costs (Le Cook et al., 2013; Issenberg et al., 2005), establishing it as a fundamental pillar of healthcare clinical training (Saleem & Khan, 2023) that provides a safe and effective environment for learners to develop crucial skills and decision-making abilities (Pottle, 2023).

1.1. Integration of I-XR in Healthcare Education

Recent technological advancements have further propelled SBME to include methods like virtual reality (VR), augmented reality (AR), and mixed reality (MR) (Tang et al., 2016). These methods are thought to help better equip healthcare students with practical experience and readiness for actual scenarios and procedures (Horowitz et al., 2022; Wu & Norvell, 2022). Such technologies are increasingly seen as having promise for teaching in the healthcare professions, even though the largest market is still in the entertainment and the gaming industry (Bankar et al., 2023; Wohlgenannt et al., 2022).

Extended reality (XR) is an umbrella term used to include VR, AR, and MR (Aebersold et al., 2020), but typically refers to computer-generated images in the wearer’s field of vision and includes the range of the user’s view of the world that can exist from fully visible to fully occluded (Zhang et al., 2023), and thus blurs the line between the digital world and the physical world (Chengoden et al., 2023). Finally, Immersive Extended Reality (I-XR) is characterized as a screen-based simulation that highlights the 3D nature of the simulated patients, graphics, sound, and navigation through the environment (McGrath et al., 2018). The use of I-XR for healthcare education is becoming increasingly more recognized as it allows an almost unlimited number of clinical scenarios to be simulated, with the ability to allow real-time feedback on student progress and patient status (Lu & Bowman, 2021), and can improve users’ motivation, engagement and enjoyment in educational learning across different domains by providing powerful experiential learning (Fokides et al., 2021; Tonteri et al., 2023). As more healthcare educators pioneer innovative methods, I-XR is emerging at the forefront of teaching and learning and is preparing healthcare students with practical skills, hands-on experience, and preparedness for real-world scenarios and procedures (Horowitz et al., 2022). Embracing these changes represents innovative steps towards cultivating a more knowledgeable, proficient, and self-assured healthcare workforce, benefiting patients, and enhancing healthcare education.

1.2. Review of I-XR Studies for Training Effectiveness in Healthcare Education

Whether I-XR methodologies specifically improve student learning outcomes compared to non-immersive (non-I-XR) methods is still up for debate. Historically, the majority of meta-analytic studies focused on medical students’ surgical skills (Zhang et al., 2023). Within the last five years, there has been a surge of systematic reviews and statistical meta-analyses seeking to explore whether I-XR (specifically VR, AR, MR) methodologies improve students’ learning outcomes across domain-specific skills, procedural outcomes, and even non-technical skills (e.g., empathy, self-efficacy, teamwork). Despite the number of reviews, the results are still inconclusive and often dependent on how “knowledge acquisition” and “skill performance” are operationalized. For instance, a meta-analysis of 11 studies on VR endoscopy training for medical students found that while VR improved procedure completion and overall performance ratings compared to traditional methods, it resulted in fewer independent completions and showed no difference in other behavioral outcomes (Khan et al., 2018). Another VR systematic review of nine studies revealed that only two showed improved knowledge acquisition in healthcare professionals compared to control groups (Abbas et al., 2023), suggesting that while VR can enhance knowledge, it may not be consistently superior to other methods. However, the same review found significant increases in skill performance in 19 out of 21 studies comparing VR to control methodologies (Abbas et al., 2023), indicating a stronger impact on practical skills. Other recent reviews (Dicheva et al., 2023; Huai et al., 2024; Jallad & Işık, 2022; Uslu-Sahan et al., 2023) show positive effects of I-XR on nursing students' knowledge, skill performance, skill acquisition, and clinical reasoning. These varied results suggest that the effectiveness of I-XR may depend on factors such as the specific technology used, the type of training, and the outcome being measured. Further research is needed to fully understand the conditions under which I-XR can optimize learning outcomes in healthcare education.

The results are also mixed as to whether one kind of immersive technology (AR, VR, MR) is more effective than another. For example, a systematic review of VR head-mounted displays in medical education (Kovoor et al., 2021) showed significant increases in learning surgical procedures in seven of the 11 studies reviewed compared to other immersive methodologies, but no differences when directly compared with AR. Yet, when assessing anatomy knowledge, only one of the six studies reviewed showed increased anatomy knowledge using VR. The authors concluded that VR technologies outperformed conventional methods for learning surgical skills, but they were not superior for learning anatomy knowledge. Yet, in another meta-analysis, VR methodologies proved to be more effective in improving nursing students’ performance skills compared to other mixed reality methodologies (Chen et al., 2023).

Despite the mixed evidence for I-XR technologies compared to non-immersive technologies in learning procedural skills and knowledge acquisition, the specific teaching and learning processes remain under-explored. Therefore, how these methods use or adhere to learning theories to understand the delivery of skills and knowledge could be another overlooked possibility for the mixed evidence.

For example, educational learning theories offer valuable frameworks for understanding how students acquire knowledge and develop skills. Researchers have identified thirteen key theoretical perspectives relevant to medical education, including cognitivism, constructivism, experiential learning, and reflective learning, among others (Kaufman & Mann, 2011; Mann, 2011). These theories guide a range of pedagogical approaches, from optimizing information presentation (Issa et al., 2013) to fostering reflective practice (Van Merriënboer & Sweller, 2010).

However, despite the potential benefits of integrating learning theories into I-XR research, several studies have identified a concerning trend. Many studies on I-XR in healthcare education either poorly described or completely omitted their pedagogical approaches (Kivuti-Bitoket al., 2022). In one study, less than 3% of papers on VR simulations incorporated a conceptual framework or theory in their design, and self-initiated training was the most common mode (Jiang & Fryer, 2024).

This lack of theoretical grounding raises concerns about the rigor and generalizability of I-XR research in healthcare education. Without a clear theoretical foundation, it becomes difficult to understand why certain interventions are effective and how they can be optimized for different learning contexts. This highlights a critical need for researchers to explicitly integrate learning theories into the design, implementation, and evaluation of I-XR training programs.

1.3. Summary

In sum, I-XR is transforming healthcare education by providing immersive and engaging learning experiences that enhance skill acquisition, knowledge retention, and preparedness for clinical practice. This technology aligns with the evolving needs of healthcare training, offering adaptable and flexible solutions for integrating cutting-edge techniques. As technology continues to advance, I-XR is poised to play an increasingly vital role in shaping the future of healthcare education. However, realizing the full potential of I-XR depends on equipping healthcare educators with the necessary skills and resources to integrate these technologies into high-quality patient care training effectively.

1.4. Purpose

This systematic review aimed to examine the effectiveness of I-XR training for healthcare students (e.g., medical and nursing). The review focused on comparing I-XR training methods to traditional, non-immersive approaches. Specifically, we sought to determine whether I-XR training leads to greater improvements in both skill competency and knowledge acquisition. Skill competency and knowledge acquisition were assessed across multiple domains, including performance skills, objective performance measures, clinical reasoning, internship grades, problem-solving skills, and skills knowledge.

This review offers several unique contributions to the field. First, it examines six distinct outcomes related to skill competency and knowledge acquisition. Second, it focuses on both graduate medical and nursing education across a wide range of techniques, providing a comprehensive overview of I-XR's potential in healthcare training. Third, recognizing that the effectiveness of I-XR may depend on the specific alternative being compared, the review analyzes results according to the type of non-immersive (comparison) group used (e.g., didactic instruction, manikin-based training, simulated patients). Fourth, unlike many reviews that focus on a single technology, this review encompasses the full breadth of I-XR techniques, including AR, VR, and MR. Finally, it assesses each study for its integration of pedagogical approaches and learning theory, providing insights into best practices for implementing I-XR in healthcare education.

2. Methods

2.1. Search Strategy

Consistent with Best Evidence in Medical Education (BEME) recommendations, the search was performed on the inclusive databases of PubMed and Google Scholar in January 2023 and again on April 1, 2024, and followed the Joanna Briggs Institute for scoping reviews. Search terms included “simulation in medical (or nursing) education” and “augmented reality (AR)” [or virtual reality (VR)” or “mixed reality (MR)”] and “skill outcome” (or “skill performance”). We selected articles based on the PRISMA method (see Figure 1). We have adhered to the PRISMA guidelines. This review was not pre-registered.

2.2. Inclusion and Exclusion Criteria

Only English articles were included. Only peer-reviewed empirical articles between the dates of January 1, 2016, and April 1, 2024, were included. XR papers surged in 2017, so this window captures the increase from 2016 to the present (Velev & Zlateva, 2017). No dissertations, letters to the editor, commentaries, or opinion pieces were included. Only articles with a control group were included, which could include a within-subject pre-posttest design, or a true, external comparison group. We also only included articles that focused on objective performance measures of skill competency and knowledge acquisition (i.e., performance skills, internship grades, performance measures, clinical reasoning or judgment, and problem-solving skills). Lastly, we included only articles that included graduate medical or nursing students. Studies were excluded if they were outside the indicated dates, did not include a control group or pre-post design, did not include medical or nursing graduate students (e.g., undergraduates), were not available in English, or were not in a peer-reviewed journal. Studies that solely included measures of confidence, self-esteem, or other non-technical skills were excluded from the process. For cases in which the studies included these measures but also included skill competency and/or knowledge acquisition measures, we reported only on the latter.

The initial search with parameters yielded 11,434 articles. Duplications were removed and titles and abstracts were evaluated for topic appropriateness. We also reviewed additional papers using the ancestral method (i.e., reviewing the references of included papers) that did not come up in our original search. After screening, 156 papers were left to review (see Figure 1). After reading these papers fully, 56 were included in the final data set (n = 100 removed for reasons indicated above) [Appendix A].

2.3. Data Extraction

We extracted the year, student sample size, field (e.g., medical, nursing), type of I-XR (AR/VR/MR), comparison group types, outcome measures, and any mention of learning theory. We also included a description of each study’s methods, results, and descriptive statistics for comparisons of interest (Table 1). At least three researchers reviewed each paper for the accuracy of these variables. Two coders coded the year, student sample, field, learning theory, and type of I-XR method. The agreement was > 95% on the first pass. The first and second authors coded the type of comparison groups and outcome measures, as well as made the final assessment for each. Initial agreement exceeded 85%. If codes were discrepant, the first author made the final decision.

2.4. Data Coding

Outcome measures (i.e. skill competency and knowledge acquisition) were initially operationalized with six categories based on past literature: “performance skills, including “direct observation of procedure” (DOPS); “performance measures” (Objective Structured Clinical Exams (OSCE), Global Operative Laparoscopic Assessment (GOALS), Global Rating Scale (GRS), Academic Achievement Test (AAT); “clinical judgment or clinical reasoning” (Laster Clinical Judgment Rubric (LCJR); “skills knowledge” (e.g., Mini-Clinical Evaluation Exercise (CEX), Mini-CEX, Neurological Physical Exam (NPE), multi-choice questions of skills (MCQ); “grade in internship”; and “problem-solving skills”. We then felt it necessary to subdivide “performance skills” into specific modifiers, regardless of the actual behavior (e.g., “performance skills errors,” “performance skills time,” “performance skills injury,” “performance skills dexterity”). Finally, we added an additional category: “performance skills other”, for cases in which the performance skill was not one of those noted above. If studies had more than one outcome measure, we evaluated each outcome separately. Therefore, the total number of measures exceeded the number of studies.

Comparison groups were grouped into seven categories, also based on previous literature. These groups included learning with: “print materials” (e.g., study guides, books, technical manuals), “teacher-led” (e.g., didactic/instructor-led), “electronic materials” (e.g., video tutorial, e-learning/computer materials), “practice” (e.g., BOX trainer, dissection, mannikin, simulated patient, case-based learning (CBL), or hands-on with instructor that was not immersive-XR). If a study had multiple comparison methods (e.g., electronic materials and practice), we coded it as “combined”. However, if a study included some students learning through print materials (comparison 1), and some learning via practice, the study was evaluated twice, in this case – once for each comparison group. If all students had additional training of some sort (including the I-XR group), we did not include that training as a comparison group. In those cases, the comparison group was coded as “did nothing additional”. Finally, some studies were pre-post studies with a comparison group. In those cases, the results were assessed concerning differences between the I-XR group and the comparison group(s) after the training, rather than any pre-post change.

We gave each study one of five final assessments (Table 1): (1) “Positive” (total support for I-XR methods on all outcomes for all comparisons), (2) “Negative” (total support for the control methods on all outcomes for all comparisons), (3) “No difference” (I-XR and control methods produced no differences on all variable and comparisons), (4) “Mixed Evidence Positive” or (5) “Mixed Evidence Negative”. “Mixed Evidence Positive” was used for cases in which the I-XR methods produced enhanced effects on more outcomes compared to the non-immersive (comparison) methods. Similarly, “Mixed Evidence Negative” was used for cases in which the comparison methods produced enhanced effects on more outcomes compared to the I-XR methods. Thus, “Mixed Evidence Positive” was given to any study that favored the I-XR methods, even if on some of the variables there were no differences with the comparison, or there were some differences that favored the comparison methods (as long as that number was fewer than those favoring the I-XR methods). A similar logic was used for Mixed Evidence Negative, except that the evidence favored the comparison methods. There was only one instance where “Mixed” without a qualifier was used. This was because there were two outcomes and one favored each.

2.5. Assessing Article Quality and Bias

There are several ways in which article quality and bias can be addressed, including the Critical Appraisal Skills Program (2024) instrument (Chen et al., 2017), the Mixed Methods Appraisal Tool (MMAT) (Hong et al., 2018), the Joanna Briggs Institute (JBI) tool (2014) , the Medical Education Research Study Quality Instrument (MERSQI) (Jaros & Dallaghan, 2024), the Newcastle-Ottawa Scale for Education (NOS-E) (Wells et al., 2014) [48], and the Quality Assessment of Diagnostic Accuracy Studies revised (QUADAS-2) (Whiting et al., 2011). In our exploration of the literature, we found that the most common assessments were the MERSQI, NOS-E, and QUADAS-2. Because both the MERSQI and the NOS-E evaluate different aspects of study design and quality, we chose to evaluate each study on both.

The MERSQI is used as a measure to assess the quality of educational studies across eight domains: 1) study design, 2) sampling, 3) response rate, 4) type of data, 5) validity of evidence for evaluation measures, 6) data analysis sophistication, 7) data analytic appropriateness, and 8) outcome. The maximum score for study design, validity of evidence, outcome, and type of data is “3”. For sampling design and response rate, the maximum score is “1.5”. For data analysis sophistication, the max score is “2”, and for data analysis appropriateness, the max score is “1”. Scores are summed, with the max score being 16. We also included the items from the NOS-E, which evaluates a study on sample representation, comparison groups, retention, and blinding conditions. Sample representation, selection of the comparison group, retention, and blinding conditions were all scored with a maximum of “1”, whereas the comparably of the comparison group was divided into randomized and non-randomized studies, each having a max score of “2”. Scores are summed, with the max score being eight. The max score for the total of both scales was thus “24”. The average MERSQI-2 plus NOS-E score across the 56 studies was 18.5, showing generally high quality of articles included (range: 15-22) (see Table 2).

To assess bias, we chose the QUADAS-2. The QUADAS-2 includes 11 items that assess risk of bias with “yes”/ ”no”/ ”unclear” marks. We included a total “yes” score that summed across the 11 statements, indicating bias. The average QUADAS-2 score across studies was 7.12 (range: 2-11), showing moderately high bias despite high study quality (see Table 2).

3. Results

3.1. Descriptive Findings

Of the 56 articles reviewed, 40 (71.43%) were VR studies, 14 were AR (25.0%), one study was an MR study, and one study was a VR/AR study because it was unclear whether the training was VR or AR based on the description of the apparatus (1.8%) (see Figure 3).

Most studies were published in 2020 (n = 13, 23.2%) (Figure 4).

80.4% (n = 45 studies) were performed on medical graduate students (n = 3641 individuals), compared to 19.6% of studies (n = 11) that were performed on nursing graduate students (n = 1085 individuals) (Figure 5).

The types of comparison techniques used across studies included: “print” (n = 13, 21.0%), “teacher” (n = 8, 12.9%), “electronic” (n = 10, 16.1%), “practice” (n = 15, 24.2%), “combined methods” (n = 6, 9.7%) , “pre-post” (n = 3, 4.8%), or “did nothing additional” (n = 7, 11.3%) (Figure 6).

“Performance skills” variables across studies included: “time”, n = 13, 11.3%; “errors”, n = 5, 4.4%; “dexterity’, n = 0, 0%; “injury”, n = 1, 0.9%; “other specific/DOPS”, n= 46, 40.0%) “skills knowledge’ (n = 28, 24.4%), “grade in internship” (n = 1, 0.9%), “performance measures” (n = 15, 13.0%), clinical judgment/reasoning skills” (n = 5, 4.4%), “problem-solving skills” (n = 1, 0.9%) (Figure 7). The average number of variables assessed in a study was two (range: 1- 12).

Table 2 also shows whether any learning theories were mentioned within the included studies. Approximately seventy-five percent of the studies did not mention any learning theory. Thirteen studies included one or more theories. The most commonly mentioned theory was cognitive load (n = 6). Simulation theory/NLN/Jeffries theory was mentioned three times, and self-regulated theory and Bloom’s theory were mentioned twice. Situated learning theory, directed self-regulated theory, simulation-based mastery learning, constructive alignment theory, deliberate practice, Kolb’s theory, and experiential learning theory were each mentioned once.

3.2. Overall Study Assessments of I-XR Skill Competency

Overall, we found that 42.5% of studies reported that VR was more effective than comparison (non-immersive) methodologies (Positive), 42.9% of AR studies were more effective than non-immersive methodologies (Positive), and the one MR (100%) was more effective than the comparison (Positive) (Figure 8). In addition, 25.0%, 21.4%, 0.0% of studies reported no difference between the VR, AR, MR group and the comparison groups, respectively (No difference). The one study classified as VR/AR also showed no change compared to the comparison (No difference). These percentages are compared to 35.0% (VR), 14.3% (AR) and 0.0% (MR) of studies showed mixed results (Mixed Evidence Positive or Mixed Evidence Negative). Finally, only two studies (one VR (2.5%) and one AR (7.14%)) favored the training comparisons (Negative).

4. Discussion

This review provides compelling evidence for the effectiveness of I-XR training in healthcare education. A substantial majority of studies (42.9%) demonstrated that I-XR methodologies led to universally improved outcomes compared to traditional training comparison methods. These percentages reflect improved outcomes on all measures, thus underscoring the findings' robustness. Only a small minority (3.6%) of studies universally favored comparison (non-immersive) approaches.

It is important, however, to acknowledge that 26.8% of I-XR studies showed mixed results (Mixed Positive, n = 10, 17.9%; Mixed Negative, n = 4, 7.1%; Mixed, n = 1, 1.8%). The effectiveness of I-XR is therefore not universal and may depend on various factors such as the specific technology used, the type of training, and the implementation context. Further research is needed to identify the conditions under which I-XR is most effective and to develop evidence-based guidelines for its optimal use.

A significant gap in the theoretical foundation of I-XR teaching and learning approaches was also revealed. Many studies lacked a clear articulation of the learning theories guiding their pedagogical strategies. This omission hinders the development of effective, evidence-based practices and may stem from a limited understanding of how best to teach and learn in these emerging digital environments. While I-XR offers exciting possibilities for simulation, interaction, and experiential learning, traditional pedagogical models designed for physical classrooms may not translate effectively to the virtual realm. For example, some studies have indicated challenges in maintaining student engagement, fostering collaboration, and ensuring equitable access in virtual learning environments (Zweifach & Triola, 2019).

These challenges underscore the need for further research to explore pedagogical approaches specifically for virtual learning environments. Future studies should prioritize the development of robust pedagogical frameworks specifically for I-XR, grounded in established learning theories. Additionally, more research is needed to determine the conditions under which I-XR training is most effective, considering factors such as the type of technology, the learning objectives, and the characteristics of the learners. By addressing these critical areas, the full potential of I-XR can be unlocked, optimizing its use in healthcare education to enhance the training of future healthcare professionals.

4.1. Limitations

While I-XR technology holds immense promise for revolutionizing healthcare education, a critical appraisal of the current landscape necessitates overcoming significant limitations. First, the existing body of research is hampered by methodological weaknesses, notably the prevalence of high bias in published studies. This bias, a well-documented phenomenon across academic research, may lead to an inflated perception of I-XR's positive effects, obscuring a clear and objective understanding of its true impact on learning outcomes and clinical performance.

Second, the inherent limitations of I-XR technology itself pose substantial barriers to widespread adoption. The acquisition and maintenance of I-XR equipment, software, and dedicated simulation spaces often entail significant financial investment, potentially creating disparities in access to this technology, particularly for institutions with limited resources.

Moreover, users may experience physiological side effects, such as cybersickness (including nausea, dizziness, and disorientation), eye strain, and headaches, which can hinder learning, diminish user engagement, and impede the seamless integration of I-XR into educational practices (Kolcun et al., 2023). Furthermore, the effective implementation of I-XR necessitates careful planning, technical expertise, and ongoing support. Challenges include the time and effort required for content creation, customization, and integration with existing curricula, as well as the need for dedicated technical support to troubleshoot issues and ensure smooth operation.

Addressing these limitations will require a multi-pronged approach. Future research should prioritize rigorous methodologies, including randomized controlled trials with well-defined comparison groups and objective outcome measures, to mitigate bias and provide a more robust evidence base for I-XR's efficacy. Technological advancements are urgently needed to reduce costs, enhance user comfort, and minimize physiological side effects, making I-XR more accessible and user-friendly.

Finally, fostering collaborative partnerships between educators, technology developers, and institutional stakeholders is crucial to addressing logistical challenges, developing best practices for implementation, and facilitating the seamless integration of I-XR into healthcare education. By acknowledging and proactively addressing these limitations, we can pave the way for the successful and impactful integration of I-XR, ultimately transforming healthcare education and improving patient care.

4.2. Future Directions

The future of healthcare education stands at the precipice of a transformative era fueled by the remarkable potential of I-XR technologies. These technologies, encompassing VR, AR, and MR, offer unprecedented opportunities to create engaging and effective learning experiences that bridge the gap between theoretical knowledge and practical application. However, successfully integrating I-XR into healthcare education requires a thoughtful and comprehensive approach beyond mere technological adoption. It demands a fundamental shift in pedagogical thinking, prioritizing the application of established learning theories, such as experiential learning and embodied cognition, to guide the design and implementation of I-XR curricula. Experiential learning emphasizes the importance of active engagement and hands-on experiences in the learning process, while embodied cognition highlights the interconnectedness of mind and body, suggesting that learning is enhanced when it involves physical interaction and sensory immersion (Macrine & Fugate, 2022). By grounding I-XR experiences in these theoretical frameworks, educators can create immersive and impactful learning environments that foster deep understanding, critical thinking, and skill acquisition.

Furthermore, the effective integration of I-XR necessitates a collaborative ecosystem where medical education leaders, faculty, and technology developers work in concert. This collaboration should focus on establishing a shared vision for I-XR integration that aligns with the goals of healthcare education, providing comprehensive faculty training to empower them to adapt their curricula and pedagogy, and encouraging active faculty participation in the development of future I-XR applications. To ensure consistency and quality, standardized methods for educating, assessing, and certifying I-XR instructors are essential.

While dedicated virtual simulation centers currently represent a significant step towards realizing the potential of I-XR in healthcare education, the future points towards increased accessibility and versatility. Imagine a future where I-XR transcends the confines of specialized centers, seamlessly integrating with mobile devices and wearable technology. This would empower healthcare professionals with a powerful toolkit readily available at their fingertips, enabling them to access course content, patient information, and real-time patient data during rounds using wearable devices or smartphones.

Problem-based learning, a cornerstone of medical education, could be further enhanced through I-XR, with virtual patients and dynamic case scenarios providing a safe and engaging environment for students to hone their clinical reasoning and decision-making skills. As I-XR technology becomes more affordable and accessible, collaborations will drive the development of innovative applications tailored to the diverse needs of individual learners and healthcare disciplines, fostering personalized learning experiences.

The future of I-XR also holds the promise of even richer and more immersive experiences. Haptic feedback devices will enable students to simulate procedures with unparalleled realism, developing muscle memory and refining their dexterity. Realistic visuals and soundscapes will transport students to diverse clinical settings, from bustling emergency rooms to serene operating theaters, fostering a deeper understanding of contexts and enhancing their ability to adapt to different environments. The key to unlocking the full potential of I-XR lies in integrating learning theory into I-XR experiences and creating immersive and impactful learning environments that optimize teaching, learning, and evaluation. This, coupled with ongoing innovation and collaboration, will ensure that healthcare education remains at the forefront of technological advancement and pedagogical excellence.