Integrating augmented reality virtual patients into healthcare training: A scoping review of learning design and technical requirements

Farshid Chahartangi; Nahid Zarifsanaiey; Manoosh Mehrabi; Bahareh Zeynalzadeh Ghoochani

doi:10.1371/journal.pone.0324740

Abstract

Augmented reality (AR) enables users to view the real world with enhanced digital information, making it a transformative tool in education. Virtual patients (VPs) technology is also defined as “a specific type of computer-based application that simulates real-world clinical scenario. AR and VPs offer interactive and immersive learning experiences, with AR enhancing the understanding of complex concepts and VPs providing hands-on practice in clinical scenario. This scoping review aims to identify an integrated learning design framework and the technical requirements for augmented reality-based virtual patient Simulation in healthcare professions education. This study employed a scoping review methodology that adhered to the PRISMA-ScR checklist and the Joanna Briggs Institute (JBI) guidelines, conducted between September and October 2024. The review covered six reputable databases: MEDLINE (PubMed), Science Direct (Elsevier), Web of Science (Clarivate), Cochrane library, ERIC, Scopus. A comprehensive search yielded 924 potential studies. Articles were selected via a two-stage screening process, involving title/abstract and full-text reviews based on predefined inclusion criteria. Disagreements were resolved through consultation, resulting in 27 studies being included. Eligible studies focused on augmented reality (AR)-based virtual patient (VP) technology in healthcare education, encompassing observational, quasi-experimental, and descriptive designs. Exclusions comprised grey literature, irrelevant studies, non-full-text articles, and non-AR/VP-focused research (e.g., standalone virtual reality). Various design approaches were employed, including situated learning, experiential learning, and the ADDEI model. The technical foundations of these studies were diverse, with Unity, UTTIME and PalpSim being commonly used software platforms. It is recommended that future studies thoroughly investigate each of these design framework and the technical requirements of VPAR, examining them in greater detail from various cultural, economic, social, and emotional perspectives. Tackling these problems will be a crucial stride towards enhancing and optimizing education for healthcare professions education.

Citation: Chahartangi F, Zarifsanaiey N, Mehrabi M, Ghoochani BZ (2025) Integrating augmented reality virtual patients into healthcare training: A scoping review of learning design and technical requirements. PLoS One 20(7): e0324740. https://doi.org/10.1371/journal.pone.0324740

Editor: Ziyu Qi, University of Marburg: Philipps-Universitat Marburg, GERMANY

Received: March 5, 2025; Accepted: April 30, 2025; Published: July 16, 2025

Copyright: © 2025 Chahartangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Recent advancements in digital technology have necessitated a re-evaluation of traditional teaching methodologies, particularly within healthcare education. The integration of innovative strategies that promote active student engagement has shown significant potential to enhance learning outcomes and prepare future healthcare professionals for evolving clinical demands [1] Among these strategies, the adoption of advanced digital tools—such as augmented reality (AR), mixed reality (MR), extended reality (XR), and virtual patient (VP) simulations—has gained considerable traction in educational settings worldwide [2].

Augmented reality, in particular, has emerged as a transformative tool in healthcare education. By overlaying digital information onto the physical world, AR creates immersive learning environments that enhance student interaction with educational content and simplify complex concepts [2]. Research indicates that AR positively influences learning outcomes, offering benefits such as improved knowledge retention, increased motivation, and enhanced performance accuracy [2]. Similarly, XR/AR simulators have demonstrated multiple advantages in healthcare training, including reduced errors, shortened simulation times, and support for cognitive-psychomotor tasks [3]. Despite these benefits, challenges such as technical complexities, educator resistance, and the need for streamlined implementation remain significant barriers to widespread adoption [3].

A prominent application of AR in healthcare education is the use of virtual patients (VPs). Defined by the Association of American Medical Colleges (AAMC) as “computer-based programs that simulate real-life clinical scenarios,” VPs enable learners to assume the role of healthcare providers, practice clinical skills, and make diagnostic and treatment decisions in a controlled environment [4]. Over the past decade, the use of VPs has grown significantly, with research highlighting their effectiveness in improving diagnostic accuracy, clinical reasoning, and decision-making skills [5]. Virtual patient simulations also promote self-directed learning, active engagement, and timely feedback, making them a valuable tool for flexible and independent learning [6]. These simulations often incorporate 3D clinical settings, human physiology engines, and interactive avatars, further enhancing their realism and educational value [7,8].

While VP simulations provide a safe and controlled environment for practicing clinical skills, they often lack the dynamic and contextual elements inherent in real-world clinical encounters. Augmented reality, on the other hand, has the potential to bridge this gap by overlaying digital information onto the physical world, creating a more immersive and interactive learning experience [4]. The integration of VP simulations with AR technologies offers a promising approach to healthcare education, combining the strengths of both tools to enhance learners’ knowledge, skills, and confidence [9,10]. Studies suggest that this combination positively impacts learning performance, motivation, and engagement, while also supporting personalized learning and active knowledge acquisition [11–13].

Despite the growing interest in AR and VP technologies, the integration of virtual patient simulations with augmented reality remains underexplored. Most existing research has focused on the use of these technologies in isolation, with limited empirical evidence on their combined effectiveness in improving specific clinical outcomes, such as diagnostic accuracy, decision-making, and patient communication skills [13,14]. Furthermore, the optimal design features and educational approaches for integrating VP simulations with AR have yet to be fully defined, highlighting a critical gap in the literature.

Recognizing the potential of these technologies, our research team conducted a scoping review to synthesize existing evidence on the integration of virtual patient technologies and AR in healthcare education. This review aims to address gaps in comprehensive evaluations and provide an overview of the technical requirements, design considerations, and assessment methods for these immersive learning tools. By doing so, we seek to establish a conceptual framework for augmented reality-based virtual patient (VPAR) systems in healthcare education, guiding their effective development and implementation.

The primary objective of this scoping review is to conceptualize a comprehensive framework for the integration of augmented reality-based virtual patient (VPAR) systems in healthcare professions education.

Specific goals

Identify an Integrated Learning Design Framework for the effective integration of VPAR systems into healthcare training curricula, including key pedagogical strategies, interaction mechanisms, and learning environment considerations.
Determine the Technical Requirements Framework for the development and implementation of VPAR systems, encompassing hardware, software, and supporting technologies necessary for seamless integration and optimal learning outcomes.

Materials and methods

Study design and framework

This scoping review was conducted from September to October 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines and the Joanna Briggs Institute (JBI) methodology for scoping reviews. The review was structured using the Population, Concept, and Context (PCC) framework to ensure a systematic and comprehensive approach [15].

Eligibility criteria.

Inclusion Criteria:

Study Focus: Only studies integrating both augmented reality (AR) and virtual patient (VP) technologies in healthcare professions education were included. Studies utilizing only one technology (AR or VP alone) were excluded unless they explicitly combined both.

Study Designs: Observational (e.g., cohort studies), quasi-experimental (e.g., randomized controlled trials, pre-post studies), and descriptive (e.g., case studies, qualitative studies).

Language: English-language publications.

Timeframe: No date restrictions were applied; all available literature meeting the criteria was considered.

Exclusion Criteria:

Grey literature, non-full-text articles, studies not directly addressing AR/VP integration, and publications from dubious databases.
Studies focused solely on virtual reality (VR) without AR or VP components.

The PCC framework.

Population: Healthcare profession students (medical, nursing, allied health) engaged in AR-based VP educational interventions.

Concept: Integration of AR and VP as educational tools, including:

Design, implementation, and evaluation of AR-based VP simulations.

Impact on learning outcomes (e.g., knowledge acquisition, clinical reasoning).

Context: Healthcare education settings (undergraduate, postgraduate, continuing professional development).

Population (participants): Healthcare profession students (e.g., medical, nursing, and…).

Information sources and search strategy

A comprehensive search strategy was developed and executed across seven bibliographic databases: MEDLINE (PubMed), Science Direct (Elsevier), Web of Science (Clarivate), Cochrane Library (Embase and ClinicalTrials.gov), Scopus, and ERIC. The search strategy was designed in three stages:

Preliminary Search: Two authors independently conducted a pilot search in MEDLINE (PubMed) to identify relevant keywords and refine the search strategy.
Refinement: Search terms and strategies were compared, discussed, and refined until consensus was reached.
Application: The final search strategy was applied to all seven databases.

The search strategy included Boolean operators and key terms such as “augmented reality,” “virtual patient,” and “healthcare education.” The full search strategy is detailed in Table 1.

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Table 1. Full search strategy in PubMed, conducted in October 2024.

https://doi.org/10.1371/journal.pone.0324740.t001

Study selection process

All identified records were imported into EndNote 21 for duplicate removal. Two independent reviewers screened titles and abstracts against the inclusion criteria. Full-text articles of potentially relevant studies were retrieved and assessed using the Rayyan system. Disagreements were resolved through discussion or consultation with a third reviewer. The study selection process is summarized in a PRISMA-ScR flow diagram (Fig 1).

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Fig 1. PRISMA-SCR flow diagram of the search and study selection process for virtual patient and augmented reality integration in health care professions education.

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Data extraction

A standardized data extraction form was developed and piloted. Two reviewers independently extracted data from the included studies, focusing on:

Design methodologies of AR-based VP simulations.
Evaluation techniques used to assess educational outcomes.
Technical requirements for implementing AR and VP technologies.
Outcomes related to knowledge acquisition, skill development, and learner engagement.

Discrepancies in data extraction were resolved through discussion or consultation with a third reviewer. The extracted data were organized into a structured table (Table 2).

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Table 2. Summary of study characteristics and extracted data.

https://doi.org/10.1371/journal.pone.0324740.t002

Data collection process

During the initial screening phase, two independent reviewers meticulously evaluated each study included in the scoping review on integrating augmented reality virtual patients (AR-VPs) into healthcare training. Each reviewer independently assessed the studies using predefined inclusion criteria, focusing on methodological rigor, relevance to the research questions, and alignment with the review’s objectives. When discrepancies arose between the reviewers’ assessments, they engaged in comprehensive discussions to reconcile differences, ultimately reaching a consensus through collaborative dialogue. This consensus-based approach ensured a systematic and objective evaluation, minimizing potential individual biases and enhancing the reliability of the study selection process.

To formally appraise the quality of the included studies, the reviewers employed the JBI (Joanna Briggs Institute) checklist, which provides a validated framework for assessing methodological quality in various research contexts. The detailed appraisal involved a systematic scoring process using this checklist, which evaluated multiple dimensions, including research design quality, methodological rigor, statistical significance, and potential limitations. Reviewers thoroughly documented their rationale for each scoring decision, ensuring a transparent and traceable assessment methodology.

Quality assessment

To ensure a comprehensive evaluation of the studies included in this scoping review on AR-based virtual patients in healthcare training, two independent reviewers conducted a thorough quality assessment using the JBI checklist. Each reviewer systematically appraised the studies against predefined criteria, focusing on several key areas: methodological quality, relevance to learning design and technical requirements, and alignment with the objectives of the review.

To address any initial disagreements in assessments, the reviewers engaged in iterative discussions, ultimately reaching a consensus. This collaborative approach guaranteed an unbiased and standardized selection process.

A structured scoring system was utilized to evaluate critical aspects of each study, including:

Research Design Validity: Assessing the robustness of the study’s design.
Technical Implementation Rigor: Evaluating the thoroughness of the technical execution.
Educational Outcomes Measurement: Analyzing how effectively educational outcomes were measured.
Reported Limitations: Reviewing the transparency and acknowledgment of limitations within each study.

The reviewers meticulously documented their scoring rationales to enhance transparency, creating an auditable trail for methodological decisions. This rigorous approach prioritized studies that effectively addressed the integration of AR virtual patients in healthcare education while upholding high scientific standards.

Collating and summarizing data

Following the quality assessment phase, the data were systematically categorized and analyzed. A summary table was created to outline the characteristics and findings of the included articles, and a comprehensive list of studies was compiled. An overview of the studies was conducted by systematically analyzing their geographic distribution, publication years, outcomes, and content to identify the benefits, effects, and challenges associated with the study’s objectives.

Results

Included reviews

A comprehensive search strategy was implemented across multiple databases from September 25 to October 15, 2024, yielding a total of 924 records. The distribution of records across databases was as follows: MEDLINE (PubMed) (n = 23), Science Direct (Elsevier) (n = 24), Web of Science (Clarivate) (n = 324), Cochrane Library (Embase and ClinicalTrials.gov) (n = 481), Scopus (n = 31), and ERIC (n = 41). After removing 385 duplicate records, 539 entries underwent title and abstract screening, resulting in the exclusion of 422 records. The remaining 117 records were retrieved for full-text evaluation. Following full-text analysis, 79 articles were excluded due to the following reasons:

No focus on the integration of virtual patients (VPs) and augmented reality (AR) (n = 46).
No focus on healthcare professions education (n = 21).
Insufficient information to determine eligibility (n = 12).

Ultimately, 27 studies were deemed relevant and included in this review. The study selection process is summarized in the PRISMA-ScR flow diagram (Fig 1).

A detailed overview of the studies incorporated in this review as offered in Table 2.

Characteristics of included studies

Geographical distribution.

The 27 included studies originated from diverse geographical regions, with the majority (55.56%, n = 15) from the Americas, followed by Europe (37.04%, n = 10), and Asia (7.41%, n = 2). Notably, Africa was not represented in the reviewed studies. Within the Americas, the United States contributed the most studies (n = 12), followed by Brazil, Mexico, and Canada (n = 1 each). In Europe, the United Kingdom led with 2 studies (n = 2), followed by Italy (n = 2), and France, Russia, Serbia, Norway, Germany, and Sweden (n = 1 each). The limited representation from Asia included studies from China and Thailand (n = 1 each) (Fig 2).

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Fig 2. Geographical distribution chart of studies.

https://doi.org/10.1371/journal.pone.0324740.g002

Participant demographics.

The participants in the reviewed studies were predominantly medical students (40.74%, n = 11), followed by nursing students (18.52%, n = 5), radiology students (11.11%, n = 3), and preclinical and other students (11.11%, n = 3). Other groups included medical residents (7.41%, n = 2), dental students (3.70%, n = 1), pharmacy students (3.70%, n = 1), and medical Informatics (3.70%, n = 1) (Fig 3).

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Fig 3. Diagram of participants (students) in studies.

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Medical SPECIALTIES AND EDUCATIONAL APPLICATIONS

The included studies (n = 27) showcased the application of augmented reality-based virtual patient (AR-VP) technology across various medical specialties. Two reviewers independently extracted and classified the data according to the primary educational focus of each study, resolving discrepancies through discussion and consulting a third reviewer when necessary. The classifications were derived inductively from study objectives and outcomes, without utilizing qualitative analysis software.

The major themes and applications identified included:

Advanced Clinical Procedures: Examples include mpMRI-guided prostate biopsies, emergency cardiac arrest management, and treatment planning for colorectal cancer metastases.
Anatomical and Physiological Education: Focus areas encompass female breast anatomy, lower limb structures, skeletal system, and congenital heart abnormalities, alongside the use of CT scans for pulmonary lesion assessment and heart physiology.
Diagnostic and Therapeutic Training: This includes diagnostic skills such as stroke diagnosis and critical patient assessment, as well as therapeutic skills like brain tumor management, prescription writing, and patient communication. Stroke diagnosis was classified under “therapeutic skills” when studies emphasized treatment decision-making.
Practical Clinical Skills: Hands-on training involved vital sign monitoring, chest screw placement, and heart rate interpretation.

These studies underscore the importance of comprehensive training across medical specialties and the critical role of immersive technologies in developing essential clinical skills.

Overview of medical contexts.

The reviewed studies covered a wide range of medical specialties and educational contexts, including:
Advanced Procedures: mpMRI-guided prostate biopsies, emergency cardiac arrest responses, and colorectal cancer management.
Anatomical Education: Female breast, lower limb, and skeletal anatomy.
Diagnostic Training: Heart anatomy, physiology, pulmonary lesions, and radiological techniques (e.g., CT scans).
Therapeutic Skills: Stroke diagnosis, brain tumor management, and patient communication.
Practical Skills: Heart rate monitoring, chest screw placement, prescription writing, and vital signs management.

Distribution of study designs

The chart illustrates the distribution of study designs among the 27 included studies, categorized into four main types: sample-experimental, descriptive-analytical, experimental, and quasi-experimental (Fig 4).

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Fig 4. Chart of study types.

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As we can see, the chart illustrates the distribution of study designs among the 27 included studies, categorized into four types: sample-experimental, descriptive-analytical, experimental, and quasi-experimental. Sample-experimental studies, representing 48% (n = 13) of the studies, focus on evaluating the initial effectiveness and feasibility of AR and VP technologies in educational settings. Descriptive-analytical studies, accounting for 22% (n = 6), analyze user interactions with these technologies to understand their advantages and challenges. Experimental studies, also at 22% (n = 6), employ controlled experiments to assess the direct impact of AR and VP on learning outcomes. Quasi-experimental studies, the smallest group at 8% (n = 2), explore initial impacts in specific educational contexts, providing preliminary insights for further research.

Data analysis

Specific Objective 1: Identification of an Integrated Learning Design Framework for the use of virtual patients and augmented reality in healthcare professions education (Table 3).

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Table 3. Components of integrated learning design framework.

https://doi.org/10.1371/journal.pone.0324740.t003

The study identified five key components of an integrated learning design framework for VP & AR technologies in healthcare education. These components include VP/AR design models, educational strategies, interaction and feedback, learning environments, and learning outcomes evaluation.

VP/AR Design Models:

Simulation fidelity: High fidelity (n = 22), medium fidelity (n = 3), and low fidelity (n = 2).
Scenario complexity: Complex scenarios (n = 24) and simple scenarios (n = 3).
Content types: Multimedia content, including photos, videos, 3D images, avatars, and hyperlinks.
Teaching methods: Student-centered, instructor-centered, and blended approaches.

Educational Strategies:

Problem-based learning: Engaging students with real-world problems (n = 8).
Prototype-based learning: Focusing on innovative methods and technologies (n = 7).
Situational learning: Utilizing real-world settings for practical experience (n = 27).
Game-based learning: Incorporating clinical cases and interactive activities (n = 12).

Interaction and Feedback:

Interaction types: Commanding, conversing, manipulating, exploring, and responding.
Feedback types: Diagnostic, formative, and summative feedback to enhance learning outcomes.
Learning Environments:
Hospitals: Clinical settings for hands-on training (n = 8).
Universities: Academic and clinical departments (n = 17).
Private institutions: Technology-focused training centers (n = 2).

Learning Outcomes Evaluation:

Knowledge: Retention and understanding of medical concepts (n = 5).
Skills: Practical application and repetition (n = 12).
Attitudes: Confidence, motivation, and communication (n = 2).
Assessment methods: Objective measurements (n = 9) and subjective assessments (n = 11).

Overall, the framework emphasizes high-fidelity simulations, multimedia content delivery, and interactive experiences, highlighting innovative approaches to enhance the quality of education and learning outcomes in healthcare.

Specific Objective 2: Determine the Technical Requirements Framework for the development and implementation of VPAR systems, encompassing hardware, software, and supporting technologies necessary for seamless integration and optimal learning outcomes.

Table 4 presents an overview of the essential technical requirements necessary for the effective implementation of Virtual Patient (VP) and Augmented Reality (AR) technologies in educational settings.

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Table 4. Components of technical requirements.

https://doi.org/10.1371/journal.pone.0324740.t004

The technical requirements for implementing Virtual Patient (VP) and Augmented Reality (AR) technologies in healthcare education were categorized into hardware, software, and technology types.

Hardware:

Handheld displays: Transparent LCD displays for AR overlays (n = 7).
Projection screens: Matte mirrors for direct projection (n = 5).
Combined systems: Indoor projectors with reflective materials (n = 15).

Software:

Web-based systems: UT TIME, AR ticor, and Microsoft Teams Remote Assist.
3D imaging and simulation: CHARM Simulation, Magic Leap One, and Unity.
Specialized tools: PalpSim for tactile feedback and Immersive Touch for surgical training.

Technology Types:

Web-based systems: Virtual patient platforms (n = 27).
Image-based systems: Marker-based (n = 7) and marker-less (n = 20) technologies.

Most studies emphasized web-based systems and image-based, marker-less technologies for virtual patients. Handheld displays and projection screens were frequently highlighted in hardware, while software components featured advanced interfaces like HoloLens and tools for 3D imaging and game development, such as Microsoft Teams Remote Assist, 3D VSP, Magic Leap One, and Unity. These platforms support simulations across healthcare fields, showcasing innovative tools designed to enhance education through immersive experiences.

Overall, the figure presented below outlines a comprehensive conceptual framework for VPAR systems. This framework was developed after integrating data and insights from the analysis of relevant literature. It provides a structured overview of the key components and their interrelationships within the VPAR ecosystem. Understanding this framework is crucial for the effective design, development, and implementation of immersive learning technologies in healthcare education.

The framework encompasses several interconnected elements, including the characteristics of VPAR design models, educational strategies, interaction and feedback mechanisms, learning environments, and the evaluation of learning outcomes. These components work together to create an engaging and effective learning experience for healthcare students and professionals. (Fig 5).

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Fig 5. Conceptual framework of virtual patient augmented reality (VPAR).

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Discussion

This scoping review provides a comprehensive overview of the Integrated Learning Design Framework and Technical Requirements for Augmented Reality (AR)-based Virtual Patient (VP) systems in healthcare professions education. The findings highlight the importance of design models, educational strategies, interaction mechanisms, learning environments, and evaluation methods in creating effective and immersive learning experiences. By drawing on established educational models such as the ADDIE model for instructional design and the Technology Acceptance Model (TAM), this review ensures a robust foundation for evaluating VP/AR technologies [41,42].

A central theme identified across the reviewed literature is the prevalence of student-centered learning approaches, often facilitated by instructor guidance. Our analysis reveals a consistent application of pedagogical strategies like problem-based learning, prototype-based learning, situational learning, and game-based learning to enhance learner engagement and knowledge acquisition [12,43,44]. These strategies are commonly embedded within simulations of varying fidelity—high, medium, and low—leveraging a rich array of multimedia content, including audio, images, videos, 3D models, and avatars, to create realistic and interactive learning scenarios [12,21,27,28]. In terms of interaction, VP/AR systems support a diverse set of methods such as commanding, conversing, manipulating, exploring, and responding, all of which contribute to active participation and the development of essential skills [45–47]. The provision of feedback, categorized as diagnostic, formative, and summative, is consistently highlighted, with formative feedback being particularly emphasized for its role in guiding learning and improving outcomes [12,17,21].

VP/AR systems are implemented in diverse learning environments, including hospitals, universities, and private institutions, each offering unique opportunities for hands-on training and skill development [16,18,25]. Evaluation methods include objective measurements (e.g., tests, surveys) and subjective assessments (e.g., interviews, observations). Kirkpatrick’s evaluation model, which assesses reaction, learning, behavior, and results, is commonly used to measure the effectiveness of VP/AR technologies [16,18,19]. The hardware for VP/AR systems ranges from handheld displays (e.g., computers, monitors) to projection displays (e.g., HoloLens, mobile cameras) [10,24,25,35], often combined with advanced software platforms such as Unity3D, ARKit2, and CHARM Simulation [21,23,27]. These technologies are evaluated using models like the Technology Acceptance Model (TAM) and the System Usability Scale (SUS), focusing on usability objectives such as effectiveness, efficiency, safety, and user satisfaction [48].

Our study both corroborates and extends existing research on VP/AR technologies in healthcare education. For example, the emphasis on location-based AR and image-based AR for achieving high-fidelity simulations, as highlighted by Moro et al. (2017) and Herbert et al. (2021) [20,47], aligns with our findings regarding the importance of realistic and contextually relevant learning experiences. However, while studies such as Aebersold et al. (2018) and Barsom et al. (2016) have demonstrated the educational benefits of AR in specific domains like anatomy learning and clinical simulations [29,49], they often present a fragmented view of the integrated potential of VP and AR. In contrast to these more focused studies, our scoping review provides a more holistic framework that synthesizes design principles, assessment strategies, and educational theories to optimize the comprehensive application of VP/AR systems in healthcare education. This broader perspective allows for a more nuanced understanding of how these technologies can be effectively leveraged across different learning contexts and for various educational objectives.

Furthermore, while previous research has touched upon the various components of VP/AR systems, this review offers a more integrated perspective by explicitly linking the technical requirements with established learning design frameworks. For instance, our analysis highlights how specific interaction mechanisms supported by AR technology can directly facilitate active learning strategies advocated by models like problem-based learning. This explicit mapping between technical capabilities and pedagogical approaches represents a significant contribution beyond studies that primarily focus on either the technical aspects or the educational outcomes in isolation.

Despite the considerable promise of VP/AR technologies, our review has identified several limitations within the current body of research. The prevalence of small sample sizes across many studies raises concerns about the generalizability of their findings. Additionally, the high costs associated with specialized AR hardware, such as the HoloLens, present significant barriers to widespread adoption [20,50]. Concerns regarding the reliability and validity of certain assessment tools underscore the need for the development of more robust and standardized evaluation methods. The novelty effect, which may temporarily inflate learner satisfaction and performance, necessitates longitudinal studies to accurately assess the long-term retention of knowledge and skills acquired through VP/AR interventions [51]. To address these limitations, future research should prioritize conducting larger, multi-institutional studies to enhance the generalizability of findings. Exploring more cost-effective hardware solutions is crucial for promoting broader accessibility. The development and validation of comprehensive assessment tools are essential for accurately measuring the impact of VP/AR on learning outcomes. Furthermore, investigating the long-term retention of knowledge and skills and the seamless integration of VP/AR technologies into broader medical curricula are vital steps towards realizing their full educational potential.

In comparison to existing reviews in this area, our scoping review provides a more granular and integrated analysis of the technical requirements and learning design frameworks underpinning VP/AR systems. While other reviews may focus on specific applications or outcomes, our work offers a broader synthesis of the key components and considerations for designing and implementing effective VP/AR-based learning experiences. This comprehensive approach allows educators and researchers to gain a deeper understanding of the interdependencies between technical features, pedagogical strategies, and assessment methods.

In summary, this scoping review reaffirms the transformative potential of VP/AR technologies in healthcare professions education. By effectively integrating advanced technologies, established educational models, and comprehensive evaluation methods, VP/AR systems have the capacity to significantly enhance learning experiences and improve outcomes for medical students and professionals. However, our analysis underscores a critical need for more balanced evaluation methods that incorporate both formative and summative feedback, as well as continued research into immersive AR technologies that offer fully integrated and dynamic learning environments. Future research should also focus on addressing the identified limitations, particularly regarding generalizability, cost-effectiveness, assessment rigor, and the long-term impact of these technologies.

The limitations of this review, inherent to its scope and the current evidence base, suggest that our identification of the fundamental components of virtual patient and augmented reality technologies in healthcare professions education may not be exhaustive. Scoping reviews inherently involve certain limitations, as they aim to map the breadth of evidence rather than conduct detailed quality assessments of individual studies. Our search strategy, utilizing keywords such as simulation, virtual patient, augmented reality, mixed reality, extended reality, and healthcare professions education, may not have captured all relevant literature. The selection of components was based on existing studies and resources from both technological and educational perspectives, which may not comprehensively encompass all aspects. Additionally, many included studies provided overarching insights rather than in-depth specifics, potentially leading to incomplete information. However, we have prioritized transparency by providing clear decision-making criteria for our evidence compilation, believing that our findings offer a valuable overview of the current state of virtual patient and augmented reality technologies in healthcare education and lay the groundwork for future research.

Conclusion

This scoping review examined virtual patient (VP) integrated with augmented reality (AR) in healthcare professions education. The review found VP/AR simulations to be promising for creating interactive and realistic learning experiences. Effective design requires aligning content with learning goals, balancing realism with usability, incorporating interactive features, and providing meaningful feedback. While limitations like small sample sizes and high hardware costs exist, future research can address these by conducting larger studies, exploring cost-effective hardware, and developing robust assessments. Ultimately, this review contributes to understanding VP/AR in healthcare professions education and paves the way for optimizing their effectiveness for wider adoption.

Supporting information

S1 File. PRISMA ScR fillable checklist.

https://doi.org/10.1371/journal.pone.0324740.s001

(DOCX)

S2 File. Data obtained based on search strategy in 6 databases: MEDLINE (PubMed) (S1 Table), Science Direct (Elsevier) (S2 Table), Cochrane library (S3 Table) ERIC (S4 Table), Web of Science (Clarivate) (Supporting Information table 50, Scopus(S5 Table).

https://doi.org/10.1371/journal.pone.0324740.s002

(DOCX)

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Figures

Abstract

Introduction

Specific goals

Materials and methods

Study design and framework

Eligibility criteria.

The PCC framework.

Information sources and search strategy

Study selection process

Data extraction

Data collection process

Quality assessment

Collating and summarizing data

Results

Included reviews

Characteristics of included studies

Geographical distribution.

Participant demographics.

Medical SPECIALTIES AND EDUCATIONAL APPLICATIONS

Overview of medical contexts.

Distribution of study designs

Data analysis

Discussion

Conclusion

Supporting information

S1 File. PRISMA ScR fillable checklist.

S2 File. Data obtained based on search strategy in 6 databases: MEDLINE (PubMed) (S1 Table), Science Direct (Elsevier) (S2 Table), Cochrane library (S3 Table) ERIC (S4 Table), Web of Science (Clarivate) (Supporting Information table 50, Scopus(S5 Table).

References