Advancing Civil Engineering Education: A Systematic Review of Opportunities, Trends, Challenges, and Future Research Directions in Computer-Altered Reality Technologies

Advancing Civil Engineering Education: A Systematic Review of Opportunities, Trends, Challenges, and Future Research Directions in Computer-Altered Reality Technologies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Advancing Civil Engineering Education: A Systematic Review of Opportunities, Trends, Challenges, and Future Research Directions in Computer-Altered Reality Technologies Fatma Hosny , Bharadwaj R. K. Mantha, Saleh Abu Dabous, Ghazi Al-Khateeb, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5996662/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing complexity of civil engineering demands innovative tools to bridge the gap between theory and practice. Computer-altered reality (CAR) technologies offer immersive environments that enhance learning outcomes. However, civil engineering education lags behind other disciplines in adopting these technologies. This study systematically reviewed 359 relevant studies from an initial pool of 1508 from 20214 to 2023 using a nine-step methodology involving keyword optimization, statistical analysis, and thematic mapping. The method employed was a systematic review following PRISMA guidelines. Key opportunities include improved visualization, increased engagement, and practical skill building, with 74% of studies reporting enhanced student performance. Trends reveal the growing integration of artificial intelligence (AI) and internet of things (IoT) into CAR platforms, enabling adaptive learning. For instance, AI-driven AR overlays improve site inspection accuracy by 36%, while IoT-linked virtual reality (VR) provides dynamic, contextual training. Comparatively, while disciplines like mechanical and aerospace engineering leverage CAR for design and manufacturing simulations, civil engineering applications are more focused on virtual construction sites and structural analysis, reflecting unique characteristics. Significant challenges persist, including high implementation costs (68%), insufficient educator training (54%), and limited infrastructure (41%). Ethical and psychological considerations remain largely unaddressed, with 95% of studies overlooking privacy, cybersecurity, and long-term psychological impacts, such as VR-induced discomfort. These gaps present critical areas for future research to ensure responsible CAR integration. Future directions include cost-effective CAR solutions, improved educator training, interdisciplinary collaborations, and a focus on ethical and cybersecurity concerns. Addressing the long-term psychological impacts of CAR technologies also remains imperative. Civil Engineering Computer-Altered Reality (CAR) Virtual Reality (VR) Augmented Reality (AR) Mixed Reality (MR) Civil Engineering Education Artificial Intelligence (AI) and Internet of Things (IoT) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 1 Introduction The primary purpose of pedagogy and education is to equip students with the knowledge, skills, and competencies needed to succeed as future professionals, leaders, and innovators. Traditionally, this has been achieved through lectures, textbook-based learning, and standardized testing. However, these conventional methods often fall short of meeting the dynamic needs of today’s students and the demands of modern industries. For instance, studies reveal that passive lecture-based learning leads to lower retention rates, with students retaining only about 5% of the information presented in lectures, compared to 75% from hands-on practice or experiential learning (Ho, 2023 ; Learning Pyramid In Demonstrating 7 Levels of Understanding , 2023). Moreover, despite billions of dollars invested globally in educational infrastructure and resources, student satisfaction and learning outcomes frequently lag behind expectations. In the U.S. alone, higher education spending exceeds $ 600 billion annually, yet a significant proportion of graduates report feeling unprepared for real-world challenges (Irwin et al., 2021). In the UK, recent surveys show that over 30% of students believe they are not receiving value for their tuition fees, citing outdated teaching methods as a major concern (Naves & Hewitt, 2022 ). These statistics underscore a growing dissatisfaction with traditional educational approaches, which often lack engagement, fail to promote critical thinking, and inadequately prepare students for complex, real-world problems. To address these challenges, educational institutions are increasingly adopting innovative pedagogical methods such as immersive technologies, project-based learning, and adaptive AI-driven platforms that aim to create a more interactive, personalized, and effective learning experience. These advancements not only enhance student engagement but also align more closely with industry demands, thereby fostering a new generation of adaptable and skilled professionals (Wang et al., 2024 ). Among these advancements, computer-altered reality (CAR) stands out as an up-and-coming technology capable of creating immersive and interactive learning environments. CAR refers to a set of technologies that alter the user’s perception of reality, either by immersing them thoroughly in a virtual environment or by overlaying digital elements onto the physical world. As educators seek to bridge the gap between traditional teaching methodologies and the dynamic demands of contemporary industries, CAR is emerging as a powerful tool for improving learning outcomes across both theoretical and practical domains (Eden et al., 2024 ). The benefits of CAR in education go beyond enhancing conceptual understanding, as these technologies also foster greater student engagement and motivation. One of the persistent challenges in education is maintaining student interest, particularly in subjects perceived as abstract or complex. CAR addresses this issue by transforming passive learning into an active, interactive experience. CAR technologies also support collaborative learning by allowing multiple users to interact within the same virtual or augmented environment. This capability is precious in disciplines such as engineering and design, where teamwork and problem-solving are essential skills. Students can collaborate on virtual projects, receive real-time feedback, and develop the critical skills necessary for professional success (Schuster et al., 2016 ). 1.1 Definitions of Computer-Altered Reality Technologies CAR encompasses a range of technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and other immersive approaches, offering the potential to revolutionize educational experiences by providing students with engaging, hands-on approaches that enhance both understanding and retention of complex concepts, as shown in Fig. 1 . VR refers to a fully immersive digital environment that replicates real-world or imagined scenarios through computer-generated simulations (Brey, 2009 ; Ogrizović et al., 2021 ). This immersion is typically achieved through headsets that block out the real world, enabling users to conduct virtual experiments, explore historical sites, or practice complex procedures in disciplines such as engineering and medicine (Radianti et al., 2020 ). In educational contexts, VR has gained significant attention for its ability to enrich learning experiences by offering students the chance to participate in virtual simulations, conduct experiments, and explore complex systems (DeLanzo, 2023 ; Ramadhanya, 2024 ). This technology is particularly effective in enhancing spatial understanding and problem-solving skills within a safe, controlled environment, making it a valuable tool in engineering education (FutureLearn, 2021 ). For example, history students can use VR to virtually explore historical landmarks and events, creating a dynamic and engaging way to learn about the past (Allison, 2008 ). Moreover, in medical education, for instance, students can practice surgeries or diagnostic procedures in a virtual environment (Buono et al., 2024 ), while engineering students can simulate construction projects to test the effects of different materials without facing real-world consequences such as loss of time and resources(Kassem et al., 2017 ). AR, on the other hand, overlays digital information in the real world, allowing users to interact with both digital models and their physical surroundings (Arena et al., 2022 ; Dong et al., 2013 ). AR is commonly implemented in mobile devices or wearable technology such as smart glasses, facilitating interactive learning experiences like viewing 3D models during science lessons or receiving real-time annotations during hands-on activities (Dong et al., 2013 ). AR offers substantial potential in educational disciplines, especially in disciplines like engineering, where students can visualize and manipulate digital designs or technical data in real-time (Alvarez-Marin & Velazquez-Iturbide, 2021 ). Moreover, AR can provide additional support or challenges based on student progress, ensuring that each learner’s needs are met (Mavroudi et al., 2018 ). This adaptability is especially useful in subjects where students may require varying levels of assistance. For instance, in mathematics, AR can project complex geometric shapes or graphs into the real world, allowing students to visualize and interact with them to understand abstract concepts like vectors or calculus better. Similarly, in language learning, AR can overlay translations and pronunciation guides onto real-world objects, helping students associate words with their meanings and improve their language skills through immersive interaction. MR, which merges both VR and AR, enables users to engage with digital and physical elements simultaneously (Rokhsaritalemi et al., 2020 ). This blending of the digital and physical worlds opens up opportunities for students to manipulate virtual models in real-world contexts, making MR valuable to many disciplines, such as medical training, design visualization, consumer experience, forensic reconstruction, and historical studies (Wang et al., 2023 ). In an educational discipline, MR facilitates interaction with digital objects within real-world contexts, offering students a richer, more collaborative, and hands-on learning experience (Vasilevski & Birt, 2020 ). For example, a study explored mixed reality environments within youth media practices to understand how metaverses are shaping interactive narrative experiences for young audiences (Prieto et al., 2022 ). The study focused on three primary aspects: first, it investigated the emergent design of metaverses, tracing their evolution from early concepts to their present technological and social developments. Second, it examined popular media platforms among young people, analyzing how these platforms’ interfaces engage users, particularly in the narrative aspects. Finally, it discussed the emerging metaverse models that resonate with young audiences, offering insights into how these virtual environments might evolve to align with youth preferences and media consumption trends. Other emerging technologies like haptic feedback devices, AI-driven adaptive learning systems, spatial computing, etc. are also transforming education. Haptic technologies enable tactile feedback in virtual environments, allowing students to “feel” textures or resistance, which is particularly useful in fields like design or physical therapy training. AI-powered learning systems customize educational content based on student progress, ensuring personalized learning trajectories and addressing individual weaknesses. Spatial computing, which integrates sensory data with AI and real-world inputs, provides an enhanced level of interaction and realism. For example, this technology can enable group projects in engineering, where students collaboratively build and test virtual prototypes within their physical classrooms. Finally, extended reality (XR) serves as an umbrella term that encompasses VR, AR, and MR, referring to all immersive technologies that either fully immerse or enhance the user’s experience through the combination of virtual and physical worlds (de Giorgio et al., 2023 ). XR technologies expand the possibilities for educational applications, creating new opportunities for interactive learning by integrating virtual simulations, augmented overlays, and real-time interactions. In engineering education, XR enables students to seamlessly transition between entirely digital experiences, augmented real-world interactions, and hybrid environments, providing flexibility and depth in learning complex, technical subjects. These technologies are increasingly integrated across various educational disciplines, particularly in engineering, where they aid in the comprehension of complex concepts and foster more interactive, engaging learning environments. Recognizing these trends ensures that the review is structured to address the critical technological advancements shaping the educational landscape, offering a comprehensive framework for further analysis and guiding future research into the incorporation of CAR technologies in education. 1.2 Overview of Existing Reviews Table 1 shows the summary of 13 selected existing review studies out of a total of 30 studies analyzed in the realm of CAR technologies within education. These studies are categorized into various disciplines and sub-disciplines, including education (general and overall), engineering education, and civil engineering education, to emphasize their specific contexts. The primary objective is to compare and contrast the findings from these studies to identify trends, strengths, and gaps that are particularly relevant to civil engineering education. From the summarized data, it is evident that while CAR technologies have been widely applied in general and engineering education, their targeted use in civil engineering education remains underexplored. The table highlights key observations, such as the effectiveness of CAR in enhancing conceptual understanding, promoting engagement, and offering immersive learning experiences. However, it also reveals gaps, including limited studies on long-term impacts and a lack of standardized assessment frameworks. These insights set the stage for adopting and adapting successful approaches to better address the unique needs of civil engineering education.) Recent studies have extensively explored the application of CAR technologies across various educational domains, including architecture (Casañas et al., 2021 ; Chu et al., 2019 ; Hajirasouli & Banihashemi, 2022 ), engineering (Dong et al., 2013 ; Espinoza et al., 2021 ; Kassem et al., 2017 ; Vergara et al., 2017 ), medicine (Buono et al., 2024 ; Sepasgozar, 2022 ), management (Pavón et al., 2020 ), and higher education (Di Natale et al., 2020 ; Onecha et al., 2023 ). Over the past decade, CAR technologies have seen a significant increase in use within civil engineering education. However, while the literature emphasizes the transformative potential of these technologies, it often lacks longitudinal studies examining their sustained impact on educational outcomes and professional readiness. For instance, a review by Nagaraj et al. ( 2023 ) evaluated 82 studies focused on XR technologies in manufacturing engineering education, covering the period from 1999 to 2020. While the review highlighted the effectiveness of XR in enhancing training and educational outcomes through immersive, hands-on experiences, it did not address critical aspects such as long-term knowledge retention or the scalability of these technologies in diverse educational settings. Similarly, a 2023 study observed that 45 out of 572 articles published between 2017 and 2022 highlights the growing importance of XR in architectural design education, underlining the transformative potential of these technologies in architectural education (Wang et al., 2023 ). However, this study did not explore the barriers to adoption, such as institutional costs and technical challenges, which remain significant limitations for widespread implementation. Specific applications of CAR technologies, such as VR and AR, have also been explored in more specialized educational contexts. For example, Bartels and Hahne ( 2023 ) emphasized the advantages of using VR to simulate construction processes, which enable students to better grasp the sequential and spatial aspects of projects. Nevertheless, their work did not consider the potential cognitive overload students might face or the accessibility of such simulations for institutions with limited resources. On the other hand, AR’s capability to provide real-time overlays of structural data on physical models (Schall et al., 2013 ) offers intuitive insights into complex structural behaviors. These applications provide students with an intuitive understanding of complex structural behaviors, bridging the gap between theoretical concepts and practical applications. Moreover, artificial intelligence (AI) has also emerged as a critical element in the evolution of education in engineering. While Núñez & Lantada ( 2020 ) highlighted AI’s role in simulating complex engineering problems, the scalability of such systems for large classrooms or diverse learning environments remains unaddressed. Similary, Wang et al. ( 2023 ) further explored AI’s role in education, demonstrating that AI-driven analysis tools can assist students in identifying mistakes and providing real-time solutions during classroom activities. The fusion of AI with AR and VR technologies offers a multifaceted approach to experiential learning, particularly in disciplines requiring hands-on problem-solving. Chen et al. ( 2022 ) developed an AI-integrated VR system that adapts to individual student’s learning pace, creating personalized problem-solving scenarios in structural engineering. Similarly, Devagiri et al. ( 2022 ) showcased an AI-powered AR system designed for real-time site inspections, enabling students to visualize structural anomalies and receive AI-driven feedback. This system effectively bridges the gap between theoretical knowledge and practical application, offering real-world insights into educational disciplines. Further validating these findings, Hwang and Chien ( 2022 ) conducted a meta-analysis revealing that courses integrating AI with AR or VR report a 30% increase in student engagement and a 25% improvement in knowledge retention compared to traditional methods. However, the analysis does not address the long-term efficacy of such integrations or the challenges of scaling these technologies for diverse student demographics. Lin et al. ( 2022 ) also noted that connectivity, processing power, and initial setup costs remain critical challenges for adopting AI-integrated CAR systems. Additionally, the need for continuous training of educators to effectively use these technologies highlights another research gap. In terms of future directions, ongoing research by Pan and Zhang ( 2021 ) focuses on AI’s predictive capabilities integrated with VR simulations. This research aims to enhance students’ problem-solving skills by presenting them with potential future scenarios in civil engineering projects. The convergence of AI, AR, and VR in civil engineering education is reshaping educational approaches with an emphasis on experiential and adaptive learning. This integration not only improves student engagement and retention but also prepares students for the evolving demands of the civil engineering profession. Table 1 Previous review work related to the CAR in engineering education Discipline # Reference Year No. of Studies Reviewed Time periods Review type Geographical Focus Technology Study Limitation XR Other VR AR MR Education 1 (Ouyang & Zhang, 2024 ) 2024 26 20 Systematic Review China ✓ Few design principles for AI-driven tools and insufficient research on integrating multimodal data 2 (Nagaraj et al., 2023 ) 2023 82 20 Critical Review USA, India ✓ Limited empirical research on AI’s long-term impact on STEM education and a lack of diverse case studies 3 (Obeidallah et al., 2023 ) 2023 23 4 Thematic Review Jordan ✓ ✓ Limited studies on XR’s long-term impact and its effectiveness across various disciplines 4 (Tang et al., 2022 ) 2022 128 10 Systematic Review China ✓ ✓ ✓ Lack of research on the long-term effectiveness and cost-efficiency of immersive technologies in medical education 5 (Di Natale et al., 2020 ) 2020 18 10 Systematic Review Italy ✓ Methodological flaws such as small sample sizes and non-randomized trials limit generalizability Engineering Education 6 (de Giorgio et al., 2023 ) 2023 78 23 Systematic Review Sweden, Italy ✓ ✓ ✓ Inconsistent quantitative data on XR’s effectiveness and lack of comprehensive evaluations in manufacturing education. 7 (Tan et al., 2022 ) 2022 82 10 Systematic Review China ✓ ✓ Few studies on long-term effectiveness and limited interdisciplinary applications of AR/VR 8 (Spitzer et al., 2022 ) 2022 82 10 Framework Review USA ✓ ✓ ✓ Lack of clear guidance in the literature for selecting appropriate XR technologies in educational settings 9 (Hajirasouli & Banihashemi, 2022 ) 2022 39 10 Review Australia ✓ Significant gap in developing pedagogies and teaching methods that effectively integrate AR technologies into the architecture and construction curriculum 10 (Diao & Shih, 2019 ) 2019 21 8 Systematic Review Taiwan, China ✓ Insufficient focus on long-term studies measuring the impact of AR on student learning outcomes. Civil Engineering Education 11 (Li et al., 2020 ) 2020 630 14 Bibliometric Review China, USA ✓ BIM education is predominantly limited to engineering management, with insufficient integration with computer and IT disciplines 12 (Wang et al., 2018 ) 2018 66 30 Critical Review Australia, China, Korea ✓ ✓ Few studies address the integration of VR with new education paradigms, and limited focus on improving depth perception and comfort with VR equipment 13 (Sampaio et al., 2010 ) 2010 25 5 Review Portugal ✓ Insufficient integration of VR with real-world projects and a lack of comparative studies on traditional versus VR methods 1.3 Research Gaps and Objectives Despite the growing interest in integrating technology into educational frameworks, the specific combination of emerging technologies and CAR in civil engineering education remains significantly underexplored. While current literature provides valuable insights into the broader applications of AR and VR, it often lacks a detailed examination of the combined benefits and challenges that arise when these technologies are paired with CAR tools. Additionally, there is a notable absence of comprehensive methodologies that integrate these technologies, coupled with a clear understanding of how they can transform traditional educational models within civil engineering. This gap is particularly evident in civil engineering education, where technology has the potential to address long-standing challenges, such as the visualization of complex structural concepts and the simulation of real-world scenarios. However, most studies focus either on isolated applications of AR or VR or their general implementation in education, leaving the synergistic use of CAR tools with other technologies, such as AI, the Internet of Things (IoT), and haptic feedback systems relatively unexamined. Therefore, the main aim of this review is to conduct a structured, systematic, and comprehensive analysis of multiple CAR technologies used in educational disciplines, with a specific focus on civil engineering education, to determine their opportunities, trends, challenges, and future research directions. This review seeks to bridge the gap between current technological capabilities and their potential to revolutionize the teaching and learning experience in this field. The main objectives of this study are to: Compare and contrast the adoption and implementation of CAR technologies in civil engineering education with other engineering disciplines and general education, identifying key similarities and differences. Examine unique challenges specific to civil engineering education and explore potential lessons that can be adapted from successful implementations in other disciplines. Evaluate the current state-of-the-art integration of CAR technologies in civil engineering education and their multifaceted impact, including student learning outcomes and teaching practices. Propose actionable research directions and strategies to enhance the accessibility, scalability, and real-world applicability of CAR technologies in civil engineering education. Objectives 1 and 2 establish a foundational understanding by situating civil engineering within the broader context of CAR adoption and identifying its unique challenges. Objective 3 delves deeper into assessing the current integration and its impacts within civil engineering education, synthesizing findings from Objectives 1 and 2. Objective 4 builds on this synthesis to propose future strategies and research directions, providing a logical conclusion and actionable insights for advancing the field. 2 Methodology Figure 2 . outlines the proposed structured nine-step methodology to achieve the objectives mentioned above. The first step involves analysis of the literature to identify different themes. This can be achieved through different ways, such as categorization, taxonomy, and clustering. These themes serve as the foundation of the structure of the review, which ensures that the review remains focused on key issues in the field. After the themes were identified, a review strategy was outlined. This involves selecting appropriate methodologies, identifying tools and databases to search, and setting a clear timeline for the review. A well-articulated review strategy ensures that the literature search is comprehensive and systematically conducted. The next step is to define specific keywords and keyword combinations based on the identified themes. These keywords will be used to search relevant databases, ensuring that the literature retrieved is focused on answering the critical research questions. A comprehensive search is conducted using the determined keywords across selected databases. This process helps to gather a broad but focused collection of literature relevant to the review’s objectives. After conducting the initial search, the preliminary results of the search are compiled and summarized. This step involves an initial review of the studies identified during the search process, with a focus on highlighting key themes and trends within the literature. These preliminary results serve as a foundation for further refinement and provide an early indication of the direction of the research. The results may highlight areas that require additional focus or refinement in the review strategy or keywords, ensuring the research remains on track and targeted. Moreover, statistical analysis is performed for quantitative insight. This statistical evaluation helps in understanding the impact of specific studies and authors in the field, as well as emerging trends. Citation analysis, network mapping, and other tools may be utilized to identify influential works and patterns within the literature. The retrieved studies are screened using pre-established inclusion and exclusion criteria. This step ensures that only high-quality, relevant studies are selected for further analysis, improving the severity of the review. After screening, the selection of studies is revised. This step involves evaluating whether any additional refinement of the included literature is necessary. If needed, the keywords and combinations are refined based on the insights gained from the statistical analysis and the initial findings of the literature review. This process ensures that the search captures a wider and more relevant pool of literature for subsequent searches. Once the final selection of studies is made, the next step is to perform a detailed review analysis. This involves synthesizing the literature, identifying gaps, and discussing the implications of the findings. This analysis helps to establish a comprehensive understanding of the current state of research and sets the stage for further detailed comparison. As a final step, the trends, challenges, and opportunities identified through the review are integrated into a broader discussion. This step synthesizes key findings into actionable insights and explores future research directions. By identifying opportunities for innovation, recognizing challenges faced by the field, and analyzing emerging trends, this stage ensures that the review provides a forward-looking perspective, offering valuable contributions to the academic community and practitioners. 2.1 Identify Different Themes In any review, identification of the main themes is a foundational step that helps frame the scope of the research and comprehensive coverage of all relevant aspects of the subject. Thematic identification provides researchers with a clear framework for analyzing key areas of focus, which is particularly important in multidisciplinary fields such as in the context of this study, where technology intersects with education. By identifying these core themes, researchers can structure their analysis more effectively, ensuring a thorough exploration of the literature. In this review, thematic identification aims to explore various themes related to the overall integration of CAR technologies in education, with a specific focus on engineering education, especially within the context of civil engineering. Following the rationale extensively discussed in Section 1.2, which elaborates on the increasing role of technology in enhancing education outcomes, three primary themes have been identified: VR, AR, and MR, which encompass the broader umbrella of CAR, as discussed earlier. These themes are prominent due to their growing influence in both the educational and industrial sectors. For instance, recent reports show that venture capital investment in CAR technologies has surged by 30–40% over the past year, and this trend is expected to continue (McKinsey, 2024 ). Additionally, the adoption of these identified themes in educational disciplines has been linked to improved student engagement, spatial understanding, and hands-on experience, especially in disciplines like civil engineering, where visualization and simulation are critical. 2.2 Outline Review Strategy A strong review strategy is essential to ensure that the literature retrieved is comprehensive, relevant, and of high quality. A systematic and well-defined search strategy allows researchers to gather studies from reliable sources, ensuring that all relevant publications on the topic are considered. This step involves not only selecting the most appropriate databases but also establishing search parameters that align with the objectives of the review. By employing advanced search techniques, such as Boolean operators and wildcards, researchers can refine their searches and focus on the most pertinent studies while efficiently excluding irrelevant papers. Once the key themes are identified, the next step is to define search characteristics and select the most suitable databases for conducting a thorough systematic search. Databases such as Scopus, Web of Science (WoS), IEEE Xplore, and others are critical in gathering a broad yet focused collection of studies. For this review, the Elton Bryson Stephens Company (EBSCO) database was chosen, which includes both WoS and the combined databases of Elsevier and Scopus (Chadegani et al., 2013 ). This selection was made to ensure comprehensive coverage of high-impact, peer-reviewed studies, as EBSCO provides access to a wide range of academic publications across various disciplines. By utilizing WoS and Scopus, which collectively capture over 90% of peer-reviewed journals globally, this review ensures thorough coverage of the most relevant and influential studies (Singh et al., 2021 ; Zhu & Liu, 2020 ). The combination of these databases was optimal for identifying studies related to CAR technologies in education and engineering. Table 2 outlines the detailed search criteria, and the rationale behind these selections is as follows. Initial searches revealed that the majority of relevant papers were published in English, leading to the decision to limit the search to English-language publications. This approach enhanced the relevance and accessibility of the results. Both articles and conference papers were included, as these formats typically provide the most current and relevant research on emerging technologies in educational and engineering disciplines. The search was further limited to the years 2014 to 2023, reflecting the period when immersive technologies, such as VR, AR, and MR, began gaining significant traction in educational and engineering disciplines. The rationale for selecting this 10-year window stems from the rapid technological advancements and increased adoption of immersive technologies in education during this timeframe. Prior to 2014, the development and application of these technologies were relatively nascent, with limited large-scale implementation in educational disciplines (Mohsen & Alangari, 2024 ). However, since 2014, immersive technologies have evolved considerably and become more accessible, driven by advancements in both hardware and software, making this period crucial for capturing the most relevant studies on their impact on education (Bermejo et al., 2023 ). Limiting the review to this period ensures the focus remains on the latest technological developments and research while excluding older studies that may no longer reflect the current state of immersive technology in education. To refine the search results and maintain relevance, Boolean operators such as “AND” and “OR” were employed. For example, the search strings “Virtual Reality” AND “Education” were used to target studies that intersected both disciplines, while OR was used to include related concepts, such as “immersive learning environments”. Additionally, wildcard symbols (e.g., *) were used to capture various forms of key terms. For example, “augmented realit*” was used to include variations such as “augmented realities” or “augmented reality-based systems”. To ensure the literature retrieved was highly relevant to the review’s objectives, searches were conducted across the title, abstract, and keywords fields. This approach ensured that the selected literature was directly aligned with the goals of the review. By employing the combined resources of EBSCO, including both WoS and Elsevier-Scopus, this review strategy ensured that the literature search was exhaustive and methodologically sound, capturing a wide range crucial for understanding the role of CAR technologies in education. This comprehensive strategy not only identifies existing trends but also highlights the most influential work in the field, facilitating an informed and well-rounded analysis. Table 2 Summary of search characteristics Criteria Option Search type Advanced search Languages English Document type Articles and conferences Timespan 2014–2023 Booleans used AND - OR Advanced search tool used Wildcard (*) Searches within TITLE-ABS-KEY (Article title, abstract, and keywords) 2.3 Determine Keywords and Combinations Selecting appropriate keywords and their combinations directly influences the scope and depth of the literature search, determining the inclusion of relevant studies while excluding irrelevant ones. By strategically combining keywords and using appropriate exclusion terms, researchers can refine their searches to target studies that align with the objectives of the review. In this review, the keyword selection process was designed to narrow the scope of the search systematically. The process began with broad research related to the use of CAR in education, followed by a more focused exploration of its application within engineering education and, finally, civil engineering education. This strategy ensured that the review captured a comprehensive range of literature, first addressing general educational applications before moving toward the specific domain of civil engineering. Table 3 outlines the final keyword combinations used throughout the review process. The first column of keywords contains different terms that are synonymous with and otherwise pertaining to CAR technologies. For example, “virtual reality (VR)” and “artificial reality” are synonymous, while “augmented reality (AR)” and “building information modeling (BIM)” refer to specific technologies within the broader category of CAR. Other terms like “extended reality (XR)” and “computer-altered reality” highlight the diverse vocabulary used in the field, catering to the variety of terminologies adopted in different studies. The second column of keywords focuses on the educational context and includes terms such as “education,” “school,” “university,” and action-oriented words like “train*,” “teach*,” and “learn*.” These terms aim to capture literature that investigates CAR applications in various learning environments, from K-12 to higher education and professional training. The third column refines the scope of engineering disciplines. Under “Engineering Education,” the focus is on studies broadly addressing CAR technologies in engineering contexts. Meanwhile, for “Civil Engineering Education,” the search narrows further to include terms such as “civil engineering,” “survey,” and “civil engineering teaching,” which are specific to the sub-discipline. During the initial phase, the search focused on the broader use of immersive technologies such as VR, AR, and MR in education overall. The terms “virtual reality,” “augmented reality,” and “mixed reality,” along with related phrases like “immersive simulation” and “extended reality,” were identified as the core search terms. These keywords were paired with education-related terms such as “teaching,” “learning,” and “student engagement” to capture a wide range of studies examining the use of immersive technologies in educational disciplines. However, throughout the search process, some adjustments were made to improve the relevance and scope of the results. For instance, the term “extended reality” was added, which provided a broader range of studies encompassing VR, AR, and MR technologies under a common term. As the review progressed, focusing more specifically on engineering education, additional keywords were incorporated. The second phase of the search added terms like “engineer*”, targeting how CAR technologies are being applied in technical and experiential learning disciplines, particularly within engineering disciplines. While “engineering education” initially seemed to provide relevant results, many studies pertained to general science, technology, engineering, and mathematics (STEM) education without a focus on immersive technologies (Owens & Hite, 2020 ). To address this, the search string was refined to include combinations like “immersive learning” AND “engineering”. This helped in narrowing down studies explicitly addressing the integration of CAR technologies in disciplines like mechanical, electrical, and civil engineering. This phase was crucial in identifying studies that address the challenges and opportunities of using CAR technologies in disciplines that require hands-on learning, such as engineering. In the final phase, the search was refined further to focus on civil engineering education. Keywords like “civil engineer*”, “civil*”, and “survey” were employed to locate studies discussing the application of immersive technologies within civil engineering disciplines. During this phase, it was noted that while “construction” yielded helpful studies related to engineering practices, it also returned studies focused on non-educational aspects of construction, such as project management in the field. To filter out these irrelevant results, the search was refined to include “construction education” or “civil engineering teaching”, which helped to limit results to educational disciplines. This phase aimed to understand how CAR technologies are transforming civil engineering education, focusing on improvements in teaching methodologies, student learning outcomes, and practical skills development. By using this phased approach, the search strategy ensured comprehensive coverage of CAR technologies from general to particular educational applications. The combination of these keywords facilitated the identification of relevant studies and minimized the inclusion of unrelated research. Table 3 Review strategy keywords combinations Keywords Topic CAR Technology Education Engineering/ Civil Engineering Virtual reality OR VR OR artificial reality OR extended reality OR XR OR computer-altered reality OR CAR OR augmented reality OR AR OR virtual environments OR building information modeling OR BIM OR visualization OR “immersive technolog*” AND Education* OR school OR university OR student OR train* OR E-learning OR teach* OR learn* OR immersive learning AND - Education Overall Engineer* Engineering Education Civil engineer* OR civil* OR survey OR construction education OR civil engineering teaching OR geotechnical OR “structural” OR transportation Civil Engineering Education 2.4 Conduct Search Once the search strategy was refined and relevant databases were selected, the search was conducted to collect relevant literature that was aligned with the objectives of the review. The focus during this phase was on ensuring that the gathered studies represented the broadest and most current range of research while remaining closely tied to the specific focus of CAR technologies in education overall, engineering education, and civil engineering education. The finalized keywords and combinations string after aggregating Table 2 and Table 3 were entered into the EBSCO database to conduct a search using the search characteristics as described in section 2.2. 2.5 Perform Statistical Analysis A statistical analysis was conducted to evaluate the scope, distribution, and impact of the retrieved literature. This step was designed to both summarize the dataset and provide deeper insights into the relationships, patterns, and trends within the themes identified. The statistical analysis was performed in two main phases: a preliminary analysis, which provided an overview of the dataset (e.g., number and distribution of the articles), and a bibliometric analysis, which offered a more in-depth examination (e.g., structure, influence, and key contributions). 2.5.1 Preliminary analysis The preliminary analysis was performed to capture the essential characteristics of the studies retrieved during the literature search, providing a foundational understanding of the dataset before proceeding further with an in-depth analysis. This analysis allowed for a thorough examination of the distribution and scope of research across different academic disciplines and educational themes. The analysis was conducted in four phases: (1) number of articles retrieved from the literature search, (2) distribution between conferences and journals, (3) distribution across academic disciplines, and (4) global distribution of studies. As mentioned in section 2.1, the literature search was conducted across multiple disciplines within the educational landscape, progressively narrowing the focus from general education topics to engineering education and, finally, civil engineering education. A total of 262,925 studies were obtained on the education overall topic, while engineering and civil education resulted in 25,424 and 1,646, respectively. 1) Number of Articles : Fig. 3 illustrates the trends in the publication of research articles across three disciplines: education overall, engineering education, and civil engineering education over the period from 2014 to 2023. The dotted line with asterisks represents the overall trends in education publications, which are plotted on the right y-axis. The light orange bars represent the publication trends in engineering education, plotted on the left y-axis, while the red bars depict publication trends in civil engineering education, also plotted on the left y-axis. During the review period, the number of publications showed variability, with a consistent upward trajectory in all three disciplines, particularly in recent years. From 2014 to 2018, the number of publications in education overall grew steadily, rising from around 12,000 to 21,000 publications annually. However, from 2019, there was a noticeable acceleration in publication rates, with the total number of studies more than 50,000 in 2023, indicating a significant growth in research activities related to education as a whole. This sharp increase can be attributed to several factors, including rapid technological advancements, a growing global focus on educational reforms, the rise of digital learning platforms, the impact of the COVID-19 pandemic, and the drive towards innovation, which accelerated the integration of technology into educational settings. A similar trend can be observed for engineering education as well, with the annual number of articles rising from approximately 1,200 in 2014 to around 4,000 by 2023. The growth in this category highlights the increasing emphasis on STEM education, the adoption of new teaching methodologies, and the use of immersive technologies like VR and AR in engineering disciplines. This trend also reflects the growing recognition of the need for innovation in engineering curricula to meet the demands of an evolving, technology-driven industry. Civil engineering education, although a smaller subset compared to the other two categories, also shows a consistent upward trend. Starting from a relatively low base of fewer than 100 publications in 2014, the number of articles rose to nearly 400 by 2023. Despite the smaller volume, this growth highlights the expanding role of specialized educational research in civil engineering, likely driven by the need for more hands-on, practical learning approaches in management and infrastructure-related disciplines. The rise in civil engineering education research aligns with the increased use of simulation technologies, management tools, and sustainability-focused practices that are now critical components of modern civil engineering education. 2) Distribution Between Conferences and Journals Figure 4 illustrates the distribution of publication types across the three disciplines. In education overall, the split between articles and conference papers is relatively balanced, with 52.96% being articles and 47.04% being conference papers. This suggests an equal reliance on journals and conferences for sharing research in this broad discipline. In contrast, engineering and civil engineering education show a higher shift toward conference papers compared to journal articles, with 66.50% and 57.07% versus 33.50% and 42.93%, respectively. This could be due to the applied nature of engineering disciplines, where conferences provide a valuable platform for engaging with industry experts and getting immediate feedback on practical developments. That is, they offer faster, more interactive ways to share and discuss cutting-edge developments with both academia and industry. 3) Distribution Across Academic Disciplines To further investigate the aggregated results, publications were initially distributed into different academic disciplines based on the preliminary research focus of the study by the search engines; however, these categories may be updated accordingly once the final categorization criteria are established. The five major academic disciplines chosen are namely natural sciences, life sciences and health, engineering and technology, social sciences and humanities, and other studies. These disciplines were selected based on their relevance to both the application of CAR technologies and their influence within the educational landscape. For instance, natural sciences and, engineering and technology are directly related to the use of advanced simulations, modeling, and virtual environments, which are highly applicable in educational contexts. Meanwhile, life sciences and health disciplines, although less represented in this dataset, have the potential for immersive training in areas like medical education and health sciences. Finally, social sciences and humanities and multidisciplinary studies were included to capture research that spans multiple academic disciplines and does not necessarily fit into one category. Figure 5 illustrates the overview of this distribution with the total number of research papers published within each of the respective categories as a percentage within the node. The higher the percentage, the bigger the node, and vice versa. The figure highlights several key trends in the distribution of research across educational disciplines, with a notable dominance of the engineering and technology disciplines. For instance, the engineering and technology disciplines clearly dominate the research landscape, with computer science being particularly prominent across all three disciplines: education overall, engineering education, and civil engineering education. In education overall, computer science contributes 32.49% of the total research, while in engineering education, it represents 31.76%, and even in the more specialized discipline of civil engineering education, it accounts for a significant portion of research (approximately 25%). This shows the central role of computer science in educational research, likely due to the increasing focus on digital technologies, coding, artificial intelligence, and the need for STEM skills in modern education frameworks. As expected, disciplines such as mathematics and physics are prominently featured within engineering education and civil engineering education. These subjects are foundational to engineering principles, explaining their substantial representation. However, chemistry appears to have a relatively low proportion of research in comparison, despite its relevance to several engineering subfields (e.g., materials science, chemical engineering). This might indicate that chemistry’s direct applications in civil engineering education are less frequently explored in the context of immersive learning technologies, unlike mathematics and physics, which are more frequently integrated due to their fundamental role in civil engineering concepts like structural analysis and fluid mechanics. Interestingly, a considerable portion of research within the three disciplines comes from the social sciences (approximately 5.72% in education overall, 8.57% in engineering education, and 10.02% in civil engineering education) and decision sciences (approximately 3.86% in education overall, 4.67% in engineering education, and 4.12% in civil engineering education). This trend reflects the increasing recognition within educational frameworks of the need to equip future engineers with increasingly essential soft and other skills such as communication, networking, and team work along with technical knowhow. Educational programs have been incorporating courses such as project management, collaboration, and stakeholder engagement to prepare students for the multifaceted challenges they will face in professional environments. These findings highlight the evolution of engineering curricula, which now frequently integrate leadership, team dynamics, and ethical decision-making as core components alongside traditional technical instruction. This shift in educational focus ensures that engineering students are not only proficient in technical skills but also capable of navigating the complex interpersonal and decision-making challenges inherent in real-world engineering projects, particularly in disciplines like civil engineering, where large-scale projects require coordinated, multidisciplinary efforts. 4) Global Distribution Figure 6 shows the distribution map of the total number of reviewed studies over the past ten years (from 2014 to 2023), highlighting global research output across education overall, engineering education, and civil engineering education. The map uses color gradients to represent the total studies in each country for education overall, with darker shades indicating higher numbers of studies. The bar chart in the bottom left correlates to the percentage of research output per country in engineering education, showing a clear quantitative comparison among the top ten contributing countries. The percentages, in addition, indicate the research output per country in civil engineering education. Three main observations stand out from the data. The first observation is that around 30% of the studies were conducted in the United States (US) and China, with 59,441 studies from the US and 49,596 from China emphasizing their leading positions in educational research. The significant research output from the US and China can be attributed to their emphasis on STEM education, technological advancements (particularly in VR and AR), industry-academia solid collaborations, and government policies that prioritize research and development (R&D) in educational technologies ( Communiqué on National Expenditures Science and Technology in 2020 , 2021; Freyman, 2024 ). These factors have driven the integration of CAR technologies into education disciplines, enhancing simulation-based learning and hands-on training. Interestingly, India stands out as a country that has made notable contributions despite being a developing nation. India contributed 23,278 studies to education overall, with 7.23% of research in engineering education and 0.56% in civil engineering education. This output is comparable to countries like the United Kingdom and Germany, which are known for their solid academic research ecosystems. This significant output is driven by government initiatives like Digital India and the National Education Policy (NEP) 2020, which promote technological integration and flexible learning models in education (Choudhary, 2023 ; Mahajan, 2022 ) The second observation is that, despite the US and China leading in the total number of publications in education overall, they do not hold the highest percentages in engineering education or civil engineering education. In engineering education, Spain ranks first with 11.36%, despite being ranked 10th in education overall, followed by Germany with 10.13%. Meanwhile, in civil engineering education, Italy tops the list with 0.90%, followed by Spain at 0.81%. This highlights that although the US and China dominate general education research, European countries, particularly Spain and Italy, are leading contributors to engineering-specific research, particularly in civil engineering. The third observation is the relatively low number of studies conducted in developing countries, which contribute around 9% of the total research output in education overall. This percentage is primarily made up of contributions from countries in Africa, South Asia (excluding India), and parts of South America. While these countries are making contributions to the field, they may be focusing on different educational priorities or areas of research. For many of these countries, the focus remains on improving basic educational access and addressing fundamental infrastructure needs, which limits the resources available for conducting research on niche topics such as technology integration into education. 2.5.2 Bibliometric analysis A bibliometric analysis was conducted to quantitatively evaluate the impact and relationships within the body of literature related to civil engineering education and the integration of CAR technologies. Bibliometrics involves applying statistical methods to publication and citation data, offering insights into the structure and development of research fields. This analysis was employed to analyze keyword co-occurrence, average yearly publications, and citation analysis. The analysis began by examining the keyword co-occurrence to identify key themes and trends in the literature (Palshikar, 2007 ). The aim is to identify these keywords using the co-occurrence analysis feature in VOSviewer© software, retrieving their frequency, degree of centrality, betweenness, and relative importance (van Eck & Waltman, 2010 ). Where frequency is the count of occurrences, degree of centrality measures the number of links among the keyword, and betweenness centrality reflects how often a keyword serves as a bridge or intermediary on the shortest route between two other keywords, typically calculated across every potential keyword pair in the network (van Eck & Waltman, 2013 ). In Fig. 7 , three main keyword clusters were identified, each representing distinct areas of research focus within the integration of CAR technologies in education. The red cluster predominantly focuses on AI, learning systems, and machine learning (ML), reflecting the growing importance of AI-driven technologies in education. Keywords such as convolutional neural networks, classification of information, automation, and decision-making suggest that a significant portion of the research explores how AI and ML systems are applied to enhance educational frameworks, improve decision support, and optimize learning algorithms. This cluster shows a strong connection between learning algorithms and optimization techniques, indicating that much of the research is dedicated to leveraging AI for educational advancements, including personalized and adaptive learning systems. These findings align with the increasing adoption of AI tools in various educational disciplines, particularly in creating intelligent tutoring systems and real-time feedback mechanisms for students. In contrast, the green cluster primarily centers around VR, e-learning, and engineering education, highlighting the role of immersive and online learning technologies. Keywords like curricula, teaching, students, and active learning suggest that the research in this cluster is focused on the practical applications of immersive technologies, especially in fields that benefit from hands-on learning, such as civil engineering and architectural design. The cluster reflects efforts to incorporate interactive learning environments, including VR simulations and e-learning platforms, to enhance student engagement and improve educational outcomes. This emphasis on practical training aligns with the field’s need for tools that can replicate real-world engineering challenges in a controlled, virtual setting. Lastly, the blue cluster is focused on human studies, with keywords such as controlled study, clinical article, and diagnostic accuracy, indicating that research is more concentrated on empirical studies and human interaction with technology. This cluster, though smaller, highlights the importance of human-centered research in evaluating the impact of immersive technologies on learners, particularly concerning user experience, learning efficiency, and cognitive outcomes. Comparing the three networks provides a clearer picture of how CAR technologies are being integrated across different educational domains. While AI, learning systems, and ML are consistently central across all disciplines, the application of VR and BIM is more specialized within engineering education and civil engineering education. This suggests that while CAR technologies are widely recognized for their educational potential, they are particularly impactful in fields requiring practical simulations and hands-on training, such as engineering and civil engineering. Additionally, civil engineering education stands out for its emphasis on real-world applications, as seen in the co-occurrence of terms like BIM, risk management, and sustainable development. This reflects the field’s focus on preparing students for industry challenges through advanced simulation technologies. In contrast, education overall focuses more broadly on technological integration, with AI and learning systems being dominant, reflecting the general shift towards digital learning environments in education. Figure 8 highlights the temporal distribution of keywords, showcasing how research interests have evolved over time across the three disciples. The color gradient, ranging from blue and green (older research, typically before 2018) to yellow (newer research, after 2021), illustrates the shifting focus in the application of CAR technologies and AI. In Fig. 8 a (education overall), it is evident that early research (blue tones) focused on topics like education, learning systems, optimization, ML, and algorithms, which were essential to meeting the demands of rapidly expanding digital education environments around 2014–2018. By contrast, more recent studies (green to yellow tones) increasingly center on AI, deep learning, convolution neural networks, and COVID-19, reflecting a growing interest in integrating AI to enhance personalized learning and decision-making processes. The engineering education network (Fig. 8 b) reveals a similar temporal pattern, where early research explored education, teaching, and engineering search for hands-on training (blue to green tones, before 2019). However, recent studies, seen in yellow, show a shift toward topics like ML, deep learning, and IoT, which are crucial for applying AI-driven solutions to engineering education. This suggests that while CAR technologies remain important, there is a growing interest in leveraging AI to optimize engineering education through adaptive learning and automated assessment tools. In civil engineering education (Fig. 8 c), the color gradient emphasizes a more noticeable shift. While earlier research largely concentrated on decision support systems, personal training, and BIM, more recent studies have shifted towards AI, risk management, ML, and deep learning. This indicates that the application of AI in civil engineering is evolving rapidly, with new research focusing on AI’s potential to manage risk and enhance project decision-making processes, as well as optimizing civil engineering education through data-driven simulations. However, the distribution of keywords also reveals underexplored areas across the disciplines. For example, while AI and VR have received significant attention, keywords related to sustainability, risk assessment, and safety management appear infrequently and are less central in the network, especially in civil engineering. This suggests that while specific technological applications have been heavily researched, important aspects like the environmental impact and operational challenges within immersive technology adoption in education are still in need of deeper exploration. Figure 9 highlights the frequency of average citations across the three disciplines, using a color gradient that transitions from blue and green for lower citations to yellow for higher citations. In General, the network analysis highlights those keywords such as “artificial intelligence”, “learning systems”, and “machine learning” are among the most highly cited, emphasizing the widespread influence and central role of AI-based approaches in educational research. Additionally, the frequent appearance of terms like “virtual reality” and “e-learning” reflects their significant integration into a variety of educational practices, signaling the growing adoption of immersive and digital learning environments. Although AI and ML are consistently relevant across all three disciplines examined, their applications differ, illustrating each discipline’s unique focus. In the context of education overall, there is a broader interest in “e-learning” and “virtual reality”, which points to current efforts in educational innovation aimed at enhancing student engagement and learning outcomes. By contrast, engineering education places a stronger emphasis on “automation”, “safety engineering”, and “decision-making”, reflecting the field’s alignment with cutting-edge technological advancements and the practical demands of the industry. Civil engineering education also leverages AI technologies but with a more specialized and targeted approach. The research in this field prioritizes infrastructure-specific applications, such as “BIM” and “structural health monitoring”, highlighting a narrower yet highly practical focus on using AI for infrastructure management. This emphasis suggests that civil engineering research is more concerned with applied technological solutions for real-world infrastructure challenges, reinforcing a practical and industry-aligned perspective. 2.6 Update Keywords and Combination Following the search process described in Section 2.4, the next step involved updating and expanding the keyword combinations based on the preliminary findings. This phase aimed to ensure that the search strategy captured a comprehensive range of relevant literature on civil engineering education and the integration of immersive technologies. The keyword refinement was conducted iteratively, allowing for a thorough revision of terms and concepts based on the evolving scope of the review. The process ensured that critical areas of research were included while irrelevant fields were excluded. The revised Table 4 lists 11 distinct keyword combinations designed to enhance search precision, thereby improving both the breadth and depth of the literature retrieval. The refinement of these keywords was essential to maintaining the search’s alignment with the review’s objectives. By focusing on specific combinations, the search process was able to target studies directly related to CAR technologies within civil engineering education. This structured approach reduced the inclusion of extraneous studies while increasing the efficiency of the search process. The goal was not only to capture the most relevant literature but also to eliminate unrelated fields, such as those focusing purely on theoretical aspects or non-engineering education sectors. While numerous other combinations could have been explored, the selection was deliberately limited to those yielding unique and highly relevant outcomes. For example, keyword pairs like “mixed reality AND engineering education” and “artificial environment AND project management” were employed to pinpoint specific implementations of these technologies in both academic and professional engineering environments. The expansion and refinement of keywords play a critical role in streamlining the overall review process, ensuring that the literature retrieved directly addresses the educational applications of CAR technologies within civil engineering. As CAR technologies continue to transform engineering education, it is essential to capture studies that focus on hands-on training, simulation-based learning, and active learning environments. By integrating targeted exclusions, the search methodology was further optimized to exclude fields that did not contribute to the core research objectives. For instance, studies that solely focused on non-civil engineering disciplines or were unrelated to immersive technology applications were filtered out during this phase. The keywords and combinations established in this phase will form the foundation for a comprehensive literature search, guiding the subsequent phases of in-depth analysis. Table 4 Keyword expansion combinations Keywords Civil Engineering 1 Computerized simulation OR Artificial environment OR Digital twins OR 3D modeling AND 2 Course* OR curricula OR lab OR Active learning AND 3 Project management OR architecture, engineering, and construction OR “Architecture, Engineering, and Construction” OR AEC 2.7 Include and Exclude Studies The include and exclude studies phase is now specifically focused on research related to civil engineering education, ensuring that only studies relevant to this discipline are selected for the final review. Therefore, the selection process for the studies included in this review was conducted using the PRISMA approach, with specific inclusion and exclusion criteria applied at different stages. Figure 10 outlines the process, starting from the initial search and concluding with the final selection of related research. Inclusion criteria 1 involved searching for papers published between 2014 and 2023, limiting the results to articles and conference papers, as determined in Section 2.2. The search terms included keywords related to CAR technologies, as outlined in Section 2.3. This phase yielded an initial set of 1,507 papers. Next, inclusion criteria 2 expanded the keyword search by incorporating new terms discovered in the titles, abstracts, and keywords of the initially retrieved papers, as outlined in Section 2.6. This method, known as pearl growing, added 11 more papers, bringing the total to 1,518 papers. Finally, inclusion criteria 3 involved a snowballing technique, both backward (examining reference lists of related articles) and forward (identifying papers citing the selected articles) (Jalali & Wohlin, 2012 ). This phase added another 26 papers, bringing the total to 1,544 papers. Following the inclusion criteria, duplicate papers were identified and removed (e.g., (Kerdan et al., 2015 ; Malatji et al., 2012 ; Prada et al., 2014 )), leaving 1,371 unique papers for further evaluation. The remaining papers were subjected to three phases of exclusion criteria based on their relevance, empirical scope, and alignment with the research topic. In exclusion phase 1, papers were excluded based on a review of their title and abstract. A total of 428 papers were excluded for being unrelated to the topic, 128 for being theoretical rather than empirical, 37 for focusing on the wrong population (e.g., non-student or clinical groups), 18 for being in a language other than English, and 4 for being impossible to retrieve. After this phase, 756 papers remained. Exclusion phase 2 involved a detailed screening of the papers to check their relevance to the scope of the review. Here, 248 papers were excluded for not focusing on civil engineering education, and another 120 were excluded for being theoretical. This left 388 papers for the final phase. In exclusion phase 3, a full-text review was conducted. Papers were excluded if they did not involve CAR (14 papers) if they focused on unrelated outcomes like usability or user experience (8 papers), or if they were theoretical (4 papers). Additionally, three papers were excluded for not addressing the correct educational subject. After the final exclusion phase, a total of 359 papers were retained and included in the review for detailed analysis. Of these, 64.38% were journal articles, while the remaining 35.62% consisted of conference papers. This systematic approach ensured that only the most relevant studies, focused on civil engineering education and CAR technologies like VR, AR, and MR, were selected for further examinations. 2.8 Conduct Review Analysis The review analysis process was structured in a way that allowed for a thorough examination of the final related research. Figure 11 outlines the stages of the analysis, starting with the evaluation of each study and the progress through qualitative and quantitative synthesis. The first step involved the evaluation of topics for each selected study, ensuring that the core themes relevant to civil engineering education and the integration of CAR technologies were addressed. This evaluation was conducted in parallel with both qualitative and quantitative data to ensure a comprehensive synthesis of findings. For studies that included qualitative data, themes such as learning outcomes, student engagement, and teaching methods were synthesized to capture the broader educational implications. Similarly, for studies with quantitative data, statistical measures were analyzed to understand trends in technology adoption, student performance, and implementation challenges. 2.8.1 Synthesize Qualitative Data Figure 12 a provides the connections between key topics in civil engineering education. Central to the network are nodes such as “civil engineering,” “education,” “student,” and “curriculum,” which are strongly linked to “teaching,” “e-learning,” and “virtual reality.” This suggests a significant emphasis on integrating advanced digital tools into the educational processes. The presence of “design,” “construction,” and “building information modeling (BIM)” indicates a focus on practical applications of these tools in more technical aspects of civil engineering. While the network's average publication year visualization (Fig. 12 b) indicates a temporal shift, where earlier studies concentrated on foundational educational themes and have progressively embraced advanced technologies like AI and VR. This transition suggests an ongoing evolution within civil engineering education, adapting to the accelerating pace of technological advancement to better equip students with the necessary tools to address modern engineering challenges. Moreover, the average normalized citations network suggests that recent studies focusing on advanced computational technologies, sustainability, and risk management are gaining traction and impact within academic and professional circles, indicating their growing relevance in shaping future educational and professional practices. The citation analysis (Fig. 12 c) provides insights into the influence and relevance of specific research areas within the academic and professional communities. Keywords like “augmented reality” (AR), “virtual reality” (VR), and “artificial intelligence” are highlighted with lighter hues, particularly yellow, indicating higher citation rates and, therefore, relatively larger academic interest and potentially increasing significance and relevance even in the industry. The projection of these topics within the citation network illustrates their strategic importance in not only advancing educational practices but also in improving professional standards and practices within civil engineering. This multi-dimensional analysis suggests a dynamic academic field where traditional educational methods are enhanced by immersive and interactive technologies. These advancements are not only improving educational outcomes but also aligning them closely with industry needs, emphasizing practical, real-world applications. The integration of AI, VR, and BIM within civil engineering education highlights a trend towards more interactive, technologically integrated learning environments that reflect the broader shifts towards digitalization in higher education. This alignment is particularly evident in the emphasis on real-world applications such as BIM and sustainability, which are crucial for preparing students to meet the challenges of modern civil engineering projects. The networks also reveal areas that may require further exploration, such as the environmental impacts of engineering practices and the broader application of safety and risk management in curriculum development, suggesting potential directions for future research and curriculum enhancement. Figure 13 summarizes the objectives, outcomes, limitations, and recommendations of the 359 publications that integrate several advanced CAR technologies in civil engineering education. 1) Research objectives : The ten most common objectives identified in the analysis underscore a comprehensive strategy to enhance civil engineering education. Firstly, integrating BIM across engineering curricula is essential, ensuring that students are well-prepared for industry demands by enhancing their skills in design, management, and interdisciplinary collaboration. Secondly, the adoption of VR and AR improves educational experiences by offering immersive environments that allow students to visualize complex structures and simulate real-world scenarios, significantly improving spatial understanding and retention. Promoting interdisciplinary collaboration is another key objective; by merging architectural, civil, and engineering courses, students develop the ability to work effectively on multi-disciplinary projects, which is vital for fostering innovation and teamwork in professional settings. The use of AI and ML in construction-related educational tracks introduces advanced analytical and problem-solving tools to students, making them adept in areas such as structural health monitoring and risk analysis. Sustainability concepts are increasingly incorporated into teaching to help students tackle global environmental challenges and adapt to developing industry standards regarding energy efficiency and green building practices. Moreover, enhancing student engagement through active learning strategies such as flipped classrooms, serious games, and project-based learning not only supports participation but also improves critical thinking and creativity. Developing virtual tools for practical skills training allows students to refine their technical abilities in surveying, design modeling, and construction through VR-based tools and digital simulations. Advanced visualization tools employing 3D modeling and AR-based aids significantly enhance the capability of students to interpret complex data, which is necessary for understanding complex design processes. Expanding project-based learning with technology integration like BIM and the IoT equips students to address real-world problems, enhancing their innovation and practical experience. Lastly, combining physical and virtual learning environments by blending virtual simulations with physical labs and fieldwork ensures a well-rounded educational experience, balancing theoretical knowledge with essential hands-on skills for future engineering professionals. 2) Outcomes and results The implementation of CAR technological tools and methods in civil engineering education has yielded significant outcomes and results across various domains of student learning and readiness. Enhanced student engagement and motivation have been observed following the integration of AR, VR, and BIM tools. These technologies have significantly increased student interest, participation, and overall satisfaction with learning experiences, indicating a positive shift in educational dynamics. Students have shown improved 3D visualization and spatial understanding, especially in comprehending architectural and civil engineering concepts through immersive learning environments facilitated by AR and VR technologies. This enhancement in spatial awareness is crucial for effective design and construction management. Moreover, project-based learning and interdisciplinary approaches have adopted better real-world readiness and collaboration among students, enhancing teamwork, problem-solving, and communication skills essential for meeting industry demands. Academically, there has been a marked increase in performance and learning outcomes. The use of interactive and formative assessment tools has not only enhanced learning efficiency but also strengthened critical thinking and practical application of knowledge in design and engineering tasks. In the field of sustainability, the use of AR, VR, and AI tools has enabled students to understand better energy-efficient designs, sustainable practices, and environmental impacts, thus strengthening a generation of engineers capable of developing innovative and sustainable solutions. The effective integration of BIM into curricula has provided students with early exposure to complex design tools, improving interdisciplinary collaboration and aligning academic training more closely with industry requirements. Additionally, the application of AI and ML has improved understanding and operational capabilities in SHM, providing students with skills in accurate damage detection and predictive maintenance. Students have also enhanced their technical and practical skills, gaining hands-on experience with advanced technologies such as Geographic Information Systems (GIS), the IoT, and ML, which are key in increasing employment skills in the engineering fields. There has been an increased awareness of architectural heritage and cultural conservation, with tools like Unmanned Aerial Vehicles (UAVs) and photogrammetry allowing students to engage actively with preservation projects, effectively blending technology with historical studies. Finally, there has been a positive shift toward active and experiential learning methodologies. Techniques such as flipped classrooms, serious games, and virtual field trips have not only encouraged deeper learning but have also promoted more flexible, personalized educational experiences. 3) Research limitations The key recommendations for future research and development in civil engineering education, as gathered from the reviewed papers, emphasize several areas to enhance learning and industry readiness. These recommendations include expanding the integration of emerging CAR technologies across more courses to adopt enhanced interdisciplinary learning and practical applications. Interdisciplinary collaboration can be further promoted by developing joint courses that integrate BIM, sustainability, and engineering principles, enabling teamwork for engineering students to better meet industry demands. Moreover, efforts should also focus on enhancing accessibility and affordability by developing cost-effective solutions, reducing hardware dependency, and providing educator training to facilitate adoption in resource-constrained institutions. Advancing data integration is critical, particularly through improving the interoperability of BIM, GIS, and IoT systems to manage complex projects, promote real-time collaboration, and ensure sustainable infrastructure management. In addition, expanding hands-on and hybrid learning approaches by blending physical fieldwork and labs with digital simulations will ensure students gain both real-world and advanced virtual experiences. Structured BIM education should be implemented earlier in curricula, with expanded applications across disciplines and continuous updates to align with evolving industry standards. The scope of AR and VR applications should be broadened to include diverse engineering fields, emphasizing interactive design reviews, construction safety, and sustainability education. Larger and longitudinal studies are necessary, involving more participants and institutions to assess the long-term impacts of digital tools like AR, VR, and AI on student learning and career preparedness. Strengthening collaborations with industry stakeholders is essential to refine educational content, enhance faculty training, and integrate real-world projects into academic programs for skill development. Finally, inclusive and scalable learning models must be developed, featuring personalized, scalable digital platforms that address diverse student needs and integrate lifelong learning frameworks to support continuous professional growth. 4) Future recommendations Despite the numerous advantages offered by CAR technological tools in civil engineering education, their adoption and integration are not without considerable limitations. One of the primary limitations is the high costs associated with acquiring, maintaining, and updating advanced setups like AR, VR, and BIM, which can limit accessibility for many educational institutions. Furthermore, there is often a lack of faculty expertise and training in these advanced tools, which makes it harder for them to be widely adopted and less effective in the educational environment. The complexity of integrating these interdisciplinary technologies into existing curricula presents another substantial challenge. For example, combining BIM with AR and GIS requires extensive resources and coordination, making it a resource-intensive effort. Additionally, many institutions struggle with limited access to necessary resources such as equipment, high-quality data, solid infrastructure, and up-to-date software, further complicating the implementation of advanced educational methods. Studies involving these technologies often suffer from small sample sizes and limited scope, typically restricted to specific universities, courses, or scenarios, which reduces the generalizability of the findings. Technical challenges and usability issues also pose significant barriers, as students and educators may struggle with complex interfaces and high computational demands, particularly when using VR and AR systems. Resistance to change is another notable limitation, with both faculty and students sometimes showing reluctance to adopt new technologies or methods due to steep learning curves or a preference for traditional educational approaches. Moreover, while various tools and methods are tested within theoretical or controlled environments, they often lack sufficient validation in practical, real-world settings, which questions their applicability and effectiveness outside the classroom. The high rational load associated with learning advanced tools like BIM, alongside core engineering concepts, can overwhelm students, particularly those who are beginners, potentially delaying their overall learning experience. Lastly, health and accessibility concerns, such as VR-related motion sickness, limited access to necessary hardware, and prevalent digital divides, further affect the scalability and inclusivity of implementing these technologies in civil engineering education. 2.8.2 Synthesize Quantitative Data The global distribution of publications after filtering civil engineering education highlights significant disparities in research contributions across regions. As illustrated in Fig. 14 and unlike in Fig. 6 , the US leads the global landscape with 86 publications, suggesting a significant investment in civil engineering research and education. China follows with 56, driven by strategic modernization of education and engineering programs. European countries also contribute notably, with countries such as Spain, Germany, and Turkey playing pivotal roles. Spain, with 21 publications, demonstrates a strong emphasis on integrating digital tools and BIM into civil engineering education. Germany and Turkey, with 9 and 8 publications, respectively, emphasize practical applications of immersive technologies, particularly in construction and infrastructure-focused education. Regions such as South Asia (e.g., India, with seven publications) show growing engagement, supported by initiatives like India’s National Education Policy 2020 (Mahajan, 2022 ). However, contributions from developing regions, including Africa and South America, remain limited, potentially due to challenges such as inadequate funding and infrastructure. This distribution underscores the need for global collaboration to bridge gaps and ensure wider adoption of innovative technologies in civil engineering education. Figure 15 illustrates the distribution of various technologies within civil engineering education. The largest segment is BIM, which accounts for 23.53%, which not only reflects its foundational role in modern civil engineering education but also underscores its potential for facilitating collaborative projects and sustainable design practices. VR follows closely, representing 21.76%, indicating its importance in immersive learning environments. AR, at 13.82%, extends this further by blending digital elements into the real world, enhancing students' ability to visualize and manipulate engineering concepts on-site. AI and the IoT, representing 6.76% and 2.65%, respectively, suggest promising areas ripe for growth. AI could revolutionize how data is utilized in civil engineering education, offering predictive analytics in areas such as urban planning and infrastructure management, while IoT could connect various sensors and devices on construction sites, providing real-time data to enhance decision-making processes. Lesser, but still notable, percentages are held by AI at 6.76% and the IoT at 2.65%. MR and XR are relatively minor, constituting only 0.59% and 1.18% respectively. Surprisingly, the Metaverse also appears, though it accounts for a minimal 0.59% of the technologies used. The remaining 29.12% of the chart is labeled as 'Other', suggesting a diverse range of additional technologies not specified within the main categories. This distribution underscores the varied and technologically advanced approaches being integrated into civil engineering education to enhance learning and practical application. The analysis of Fig. 16 and Fig. 17 provides a detailed insight into the prioritization and application of immersive technologies within civil engineering education. Figure 16 highlights general civil engineering as the most extensively covered area, with 97 studies dedicated to this field. This is followed by construction engineering and management with 79 studies, reflecting the industry’s evolution towards complex management needs that benefit from advanced technological interventions. Structural engineering also receives considerable attention, with 44 studies highlighting the critical need for innovative solutions in infrastructure safety and efficiency. However, limited studies focus on specialized fields such as geotechnical, environmental, and water resources engineering, indicating potential gaps in the current integration of immersive technologies, which could be crucial in these areas given their importance in the broader scope of civil engineering. Meanwhile, Fig. 17 shifts the focus towards the target groups benefiting from these educational advancements, with students being the primary group involved in 238 studies. This underlines a strategic emphasis on improving student learning and engagement through interactive and immersive environments that bridge theoretical knowledge with practical application. The considerable number of studies focusing on professionals and engineers, totaling 89, suggests that immersive technologies are also being extensively used for professional development, aligning with the needs highlighted in construction and structural engineering. This targeted application supports ongoing professional training and skill enhancement in a risk-free virtual setting. Researchers and teachers, each involved in 24 studies, highlight a more focused but critical engagement with immersive technologies to explore educational efficiencies and academic advancements in civil engineering education. The integration of CAR technologies into civil engineering education has been facilitated by various software and platforms. Factors including educational objectives, user expertise, and the specific requirements of the curriculum influence the selection of these tools. Figure 18 illustrates various types of software utilized in research studies focused on civil engineering and related fields, highlighting their frequency of use across studies. According to the figure, BIM software ranks as the most utilized tool, with over 32 references, reflecting the growing importance of BIM in visualizing and managing construction projects digitally. Autodesk Revit© and VR software also feature prominently, highlighting their roles in creating immersive, interactive environments that facilitate virtual construction simulations and design explorations. Autodesk AutoCAD© and AR software are also widely used, particularly for tasks that involve 3D modeling and overlaying virtual information onto physical environments. Other notable software include SketchUp© and 3D Max®, both of which are essential for architectural modeling and rendering, providing detailed visualizations of structures and spatial configurations. Software like Navisworks©, Unity©, and Quest3D© are popular for more advanced simulations and interactive experiences, often enabling collaborative or multi-user scenarios within educational environments. On the lower end, software like SPSS® and Google-based software are included, likely for data analysis and supporting various educational tools rather than for immersive simulations. ML-based software, Civil 3D©, and GIS software appear less frequently, suggesting niche applications in specific areas such as geographic analysis, specialized structural simulations, and data-driven insights. The “Other” category shows a significant presence, indicating a variety of additional software tools in use, possibly including custom applications and less common platforms tailored to specific research or educational needs. This distribution highlights the diversity of software used in civil engineering education, encompassing a range of immersive, analytical, and visualization tools that enhance both theoretical learning and practical training. It is worth noting that out of the total studies analyzed (359), 228 explicitly mentioned the use of software tools, while the remaining 131 studies did not specify or utilize any software, potentially due to differences in research focus, methodology, or technological requirements. 2.8.3 Results and Discussion The integration of CAR technologies, including VR, AR, and MR, has emerged as a transformative approach in education. Table 5 provides a comprehensive comparison of CAR technologies, which highlights their unique features, applications, and contributions to educational and professional domains. These technologies collectively represent transformative tools that reshape how knowledge is delivered, and skills are developed, particularly in disciplines like civil engineering. Each technology offers unique benefits and challenges, shaping their application and effectiveness in learning environments. 1) Interaction with the real world : VR immerses users completely in a simulated environment, isolating them from the physical world and making it suitable for tasks requiring undivided attention. AR enhances physical surroundings by overlaying digital information, allowing users to interact with their environment while benefiting from virtual enhancements. MR blends the real and digital worlds, enabling users to manipulate both simultaneously and offering a seamless hybrid experience. XR encompasses the entire spectrum of interaction, offering varying degrees of immersion from fully virtual to augmented environments tailored to specific use cases. 2) Applications : Each CAR technology caters to different educational and professional needs. VR is widely used for immersive training simulations, virtual tours, and healthcare applications. AR finds its strength in real-time instruction, navigation, and maintenance tasks, making it practical for hands-on learning. MR extends these applications by enabling real-time collaboration in design, healthcare, and education. XR provides a comprehensive framework by combining AR, VR, and MR for diverse environments like industry-specific training, entertainment, and education. 3) Level of immersion : The level of immersion varies significantly across these technologies. VR offers the highest level of immersion, fully disconnecting users from reality to engage with virtual scenarios. AR provides low to moderate immersion by layering virtual elements onto the physical world. MR balances high immersion by integrating real-world and digital interactions seamlessly. XR spans the entire range, offering adaptive immersion levels depending on the context, from minimal augmentations to fully immersive virtual settings. 4) Hardware requirements : The hardware demands of these technologies reflect their complexity and immersion levels. VR requires specialized headsets, motion-tracking devices, and high-performance computers to create a fully virtual environment. AR is more accessible, relying on smartphones, tablets, or AR glasses. MR necessitates advanced hybrid devices capable of combining AR and VR functionalities, while XR integrates a mix of technologies like HoloLens and Oculus Quest, depending on the desired experience. 5) Advantages : VR provides an immersive learning experience ideal for simulating dangerous or complex tasks in a controlled environment, making it cost-effective for training. AR excels in enhancing real-world interaction, offering on-the-job learning and instant feedback. MR combines these strengths, supporting collaborative and interactive applications. XR stands out for its versatility, leveraging the benefits of VR, AR, and MR across multiple environments and disciplines. 6) Limitations : Each technology comes with its limitations. VR's need for complete user isolation and expensive hardware may deter widespread adoption and raise issues like motion sickness. AR is constrained by a limited field of view, dependency on external lighting, and hardware capabilities. MR faces high computational requirements and cost challenges, while XR's implementation complexity and scalability present barriers to adoption in broader contexts. 7) Industry use cases : The industry applications of CAR technologies highlight their versatility. VR is frequently used in architecture, medicine, entertainment, and education for simulations and training. AR thrives in retail, navigation, and maintenance scenarios where real-time visualization is key. MR is applied in engineering, healthcare, and industrial training, facilitating collaboration and interaction. XR provides cross-disciplinary solutions in fields like automotive, construction, and aerospace, combining the strengths of AR, VR, and MR. 8) Cognitive benefits : Each CAR technology supports cognitive development in distinct ways. VR enhances spatial awareness and problem-solving through immersive simulations. AR improves memory retention by providing contextual, hands-on learning experiences. MR combines these cognitive advantages, enabling multi-sensory engagement and real-time adaptability. XR caters to diverse cognitive styles by integrating AR, VR, and MR for personalized and scalable learning pathways. 9) Social interaction : Social interaction capabilities vary across technologies. VR restricts interaction to virtual avatars, often limiting physical-world engagement. AR enables collaborative work in physical spaces with augmented digital overlays. MR supports seamless interaction between real-world collaborators and virtual avatars. XR offers the flexibility to adapt social interactions across fully virtual, partially augmented, or hybrid settings, catering to diverse collaboration needs. 10) Cost considerations : Cost is a significant factor influencing the adoption of CAR technologies. VR and MR are expensive due to their high-end hardware requirements, while AR is more affordable, utilizing existing devices like smartphones and tablets. XR's costs vary based on the complexity of the application, often combining the expenses of AR, VR, and MR technologies, making it the most variable option. 11) Ethical concerns : Ethical challenges differ across these technologies. VR raises concerns about overuse, addiction, and reduced physical interaction. AR faces privacy issues, particularly in data collection within real-world environments. MR's integration of physical and digital interactions introduces potential ethical dilemmas in managing personal data. XR must address concerns related to user privacy, equitable access, and the secure integration of AR, VR, and MR applications. 12) Future directions : The future of CAR technologies points towards greater integration and innovation. VR is expected to become more realistic and affordable with advancements in AI. AR is anticipated to achieve enhanced precision through AI and IoT integration. MR will likely see the development of lightweight, adaptable devices for seamless interaction. XR aims to unify these advancements, leveraging AI for adaptive, scalable solutions across educational and professional domains. Table 5 Comparison of features and aspects of CAR technologies in civil engineering education Feature/Aspect VR AR MR XR Interaction with Real World Fully replaces the real world with a simulated one. Adds virtual elements to enhance the real world. Combines real and virtual elements for interaction. Offers varying levels of immersion, from augmented to fully virtual. Applications Training, gaming, virtual tours, education, healthcare. Navigation, real-time instructions, design, interactive manuals. Collaborative design, training, surgeries, and industrial tasks. Combines AR/VR/MR for training, entertainment, and industry solutions. Level of Immersion Fully immersive. Low to moderate immersion with real-world integration. High immersion balancing real and virtual interactions. Adjustable immersion levels, from partial to full virtual. Hardware Requirements Headsets, motion trackers, high-performance computers. Smartphones, tablets, AR glasses. Advanced AR/VR headsets high-performance systems. A mix of AR, VR, and MR devices like HoloLens Oculus. Advantages Immersive learning and safe simulations. Enhances real-world learning with immediate feedback. Combines AR and VR for collaborative, practical applications. Versatile and integrates multiple CAR technologies effectively. Limitations Isolating is expensive and may cause motion sickness. Limited by lighting, hardware, and field of view. Expensive and computationally demanding. Complex to implement; scalability challenges. Learning Potential Deep engagement in simulated environments. Real-time enhancements improve retention and skills. Combines simulations with real-world interaction. Scalable systems combine immersive and real-world contexts. Industry Use Cases Architecture, surgery simulation, gaming, safety training. Retail, navigation, maintenance, anatomy visualization. Engineering, surgeries, industrial training. Cross-industry solutions in healthcare, construction, and aerospace. Cognitive Benefits Boosts spatial awareness and problem-solving. Enhances memory and hands-on learning. Combines VR and AR for multi-sensory engagement. Personalized and adaptive learning paths. Social Interaction Limited to virtual avatars and interactions. Encourages collaboration in physical spaces. Merges virtual and real-world collaboration. Offers hybrid and flexible social experiences. Cost Considerations Expensive hardware and computing needs. Affordable, works with existing devices. Higher costs for advanced hybrid systems. Costs vary depending on the technology mix and application. Ethical Concerns Risks of overuse and isolation. Privacy concerns real-world data collection. Data management challenges between real and virtual. Balances privacy, security, and equitable access. Future Directions AI integration for realistic simulations and cost reduction. Enhanced precision with AI and IoT. Lightweight, adaptable devices for seamless interaction. Unified systems combining AR, VR, and MR with AI-driven scalability. 2.9 Opportunities, Trends, Challenges, and Future Research Directions The synthesis of the entire review, particularly in addressing the identified research characteristics, establishes a clear progression from opportunities to future research directions. Specifically, the existing research characteristics underscore the untapped potential and emphasize the opportunities inherent in this research area. Emerging trends provide evidence of progress and instill optimism in realizing these opportunities. However, challenges temper this optimism by highlighting the realistic barriers that must be addressed. Together, these insights create a roadmap that aligns opportunities and trends with actionable future research directions, ensuring a balanced and forward-looking perspective. This approach not only enhances the credibility of the review but also establishes a clear pathway for formulating actionable future research directions. After a comprehensive analysis of the 359 studies, ten main characteristics were identified in CAR technologies in civil engineering education. Figure 19 illustrates the percentage distribution of key methodological characteristics identified in the reviewed research articles on CAR technologies. The insights reveal critical deficiencies across multiple domains: 1) Ethical and psychological concerns : Representing the highest percentage (95.31%), this category highlights the overwhelming lack of focus on ethical and psychological considerations. For instance, while issues like privacy, data security, and prolonged VR-induced discomfort are crucial, very few studies address them comprehensively. Moreover, there is a notable deficiency in the investigation of long-term psychological impacts, presenting a significant opportunity for future research in understanding and mitigating these effects. 2) Sample size and representativeness : Approximately 64.82% of studies were either vague about their sample size or relied on small participant groups, often fewer than 10. For example, one study included only eight participants, limiting the generalizability of its findings. This significant gap underscores the need for larger, more representative test groups to enhance the reliability and applicability of future research. 3) Lack of real-world implementation : Around 56% of studies remained confined to theoretical or controlled environments, lacking practical validation in real-world scenarios. While tools and methods have been tested in pilot phases, they often fail to transition to broader educational or professional applications. Collaborations with industries and institutions could help bridge this gap and enable real-world deployment and evaluation. 4) Neglect of instructor perspectives : Nearly 46% of studies overlooked the perspectives of educators, focusing primarily on student outcomes. This lack of teacher feedback limits the understanding of the feasibility and practicality of implementing immersive technologies in classrooms or training environments. 5) Short-term assessment : About 44.23% of the research focused on short-term evaluations, with little attention to longitudinal studies. The absence of evidence for long-term effects restricts the understanding of sustained impacts on learning outcomes or user experiences. 6) Cost considerations : Although the high cost of immersive tools is recognized as a limitation, only 33.36% of studies discussed how financial constraints influenced their research processes or outcomes. This represents a gap in understanding how economic barriers can affect technological adoption and scalability. 7) Infrastructure and institutional support : The lack of institutional support, such as insufficient infrastructure or policies, also poses a significant barrier. Without proper frameworks, scaling immersive technologies for widespread use remains a challenge, though this was less frequently discussed compared to other characteristics. 8) Software and tool limitations : Around 21.17% of studies failed to explore how the limitations of tools like Unity or Oculus Rift affected participant engagement or outcomes. Challenges like steep learning curves and accessibility issues remain underexplored, impacting replicability and accessibility. 9) Complexity and scalability issues : Complexity in system integration and a lack of scalability solutions were noted by 14.01% of studies. Few researchers provided detailed accounts of how technical challenges were resolved, hindering replication and scalability. 10) Measurement tools : Only 6.62% of studies specified standardized methods for assessing outcomes such as student engagement or understanding. The absence of clear metrics reduces the reliability of findings and limits the ability to compare results across studies. The findings emphasize a need for comprehensive strategies to address these characteristics, including ethical guidelines, larger sample sizes, real-world implementations, and standardized assessment frameworks. These improvements would enhance the credibility and applicability of immersive technology research. Figure 20 summarizes the opportunities, trends, challenges, and future research directions of CAR technologies in civil engineering education. CAR technologies offer numerous opportunities to enhance civil engineering education. Simplified integration models can streamline the adoption of immersive tools, making them more scalable and user-friendly. Specifically, the integration of AI technologies, such as ML and neural networks, with BIM facilitates predictive analysis, resource optimization, and increased design efficiency. This convergence allows for advanced simulations and real-time feedback, which enhance both the learning experience and educational outcomes. Skill development is another promising area, as immersive technologies provide students with hands-on experience and a deeper understanding of complex engineering concepts. Examples include VR simulations used in civil engineering to visualize building stress points and AR applications to design or analyze construction projects virtually. Cost optimization strategies, such as leveraging open-source software and hardware modularity, can significantly enhance accessibility, while public-private partnerships and financial incentives can accelerate adoption. The advancement of supporting infrastructure, such as more affordable VR headsets and accessible cloud-based platforms, plays a crucial role in the widespread deployment of immersive educational tools. Inclusive design principles, incorporating features like multilingual support and simplified user interfaces, ensure that these technologies are accessible to a broader audience, including individuals with disabilities or limited technical expertise. The integration of CAR technologies into civil engineering education has seen several emerging trends that shape the future of learning and teaching in the field. One of the most significant trends is the widespread adoption of BIM, which enhances collaboration and streamlines decision-making processes in projects. BIM's real-time 3D modeling capabilities allow students and professionals to visualize projects in unprecedented detail, facilitating better communication and efficiency. Another critical development is the implementation of strong cybersecurity measures designed to protect sensitive data within immersive platforms. As these technologies increasingly handle large volumes of confidential project information, ensuring data integrity and security has become crucial. The emergence of metaverse environments represents a leap towards more dynamic and interactive educational experiences. These virtual spaces simulate real-world construction sites and engineering challenges, offering students the opportunity to engage with complex scenarios in a controlled, risk-free setting. Additionally, the integration of IoT within civil engineering curricula enables real-time data sharing and monitoring. This connectivity not only enhances the educational tools available but also mirrors the shift towards smart technologies in the professional sphere. Finally, digital learning platforms powered by these technologies have gained significant traction, making engineering education more accessible and interactive. Platforms such as online simulations and virtual labs allow students from diverse geographical locations to participate in hands-on learning without the need for physical presence in a traditional classroom. While the integration of CAR technologies in civil engineering education offers numerous benefits, it also introduces several challenges that need careful management. One of the primary challenges is the complexity of integrating interdisciplinary tools such as VR systems and integrated project delivery (IPD) models. These require seamless connectivity and compatibility across different platforms, which can be technically demanding and resource-intensive. The high costs associated with cutting-edge immersive technologies like AR and metaverse platforms pose another significant barrier, especially for resource-constrained institutions. These costs often extend beyond just purchasing the technology to include maintenance and updates, making them less accessible. Infrastructure requirements further complicate the adoption of these technologies. High-speed internet connections, advanced visualization labs, and other technical facilities are essential but can entail substantial logistical and financial investment. Usability also presents a challenge, particularly in terms of accessibility and the user experience for specific groups, such as those with disabilities or limited tech proficiency. Ensuring that these technologies are inclusive and user-friendly is crucial but often overlooked in development phases. Security is another critical concern, with the need to protect sensitive data and prevent unauthorized access in immersive platforms. Implementing robust cybersecurity measures is essential but can be complex and expensive. Ethical issues, including equitable access and potential biases in AI-driven systems, add another layer of complexity. Ensuring that these technologies are fair and do not perpetuate existing disparities is vital. Lastly, the current limitations in providing tactile feedback and fully immersive experiences reveal gaps that existing technologies have not yet bridged. This highlights the need for continued innovation and development in the field. Future research in CAR technologies must address critical areas to maximize their potential in civil engineering education. Developing advanced visualization tools that enable real-time feedback, and more immersive interaction is essential for enhancing user experience and educational outcomes. Such tools should not only replicate real-world environments but also allow for the manipulation and testing of variables in ways that traditional methods cannot. To bridge the digital divide, research must also focus on enhancing global accessibility. This includes creating scalable solutions that can be adapted for use in diverse geographic, economic, and educational contexts, thereby making these advanced technologies accessible to a broader range of institutions. Real-world implementation and validation through field trials in educational, industrial, and healthcare settings will provide insights into the efficacy and scalability of these technologies. Collaboration with a broad range of stakeholders is vital for ensuring that the development of these technologies aligns with real-world demands. This involves partnerships with policymakers, industry experts, educators, and technology developers to create context-specific solutions that address the unique challenges and opportunities in each domain. Lastly, research should explore economic impacts and develop cost-effective design approaches. Studies on the economic implications of adopting these technologies will guide investment decisions. Additionally, promoting open-source platforms and modular designs can significantly reduce costs, making these advanced tools more accessible to educational institutions worldwide. 3 Summary and Conclusions The integration of technology in education, particularly through the adoption of CAR technologies, represents a transformative shift that addresses the increasing complexity of civil engineering education. These technologies, encompassing VR, AR, MR, and other technologies, are not mere supplements but pivotal tools in bridging the gap between theoretical frameworks and practical applications. Existing studies and reviews emphasize the importance of CAR in other engineering disciplines; however, civil engineering education has not been comprehensively analyzed for its specific applications of CAR. Therefore, this research systematically analyzed the different CAR technologies utilized in education (overall), engineering education (in general), and, finally, civil engineering education (specifically). The review process developed to explore these technologies follows a nine-step methodology. The first step involves a detailed analysis of the literature to identify various themes through categorization, taxonomy, and clustering. This thematic identification serves as the foundation for the review structure, maintaining focus on the pivotal issues within the field of civil engineering education and CAR technologies. After identifying key themes, a review strategy was formulated, detailing methodologies, tools, databases, and a timeline to ensure a comprehensive and systematic literature search. In this review, the EBSCO database was selected, with searches limited to English-language publications from 2014 to 2023, a period marking significant advancements in immersive technologies like VR, AR, and MR. The next step involves defining specific keywords and keyword combinations based on the identified themes to ensure that the literature search remains targeted. A comprehensive search across selected databases then gathers a focused collection of literature pertinent to the review’s objectives. Preliminary results from this search were compiled and summarized to highlight key themes and trends, providing a basis for further refinement. Then, statistical analysis was employed to provide quantitative insights into the study's impact and emerging trends. The analysis was conducted in four parts. First, the number of articles retrieved from the literature search was recorded: 262,898 studies for education overall, 25,423 for engineering education, and 1,647 for civil engineering education. Second, the distribution between journals and conferences was analyzed. In education overall, 52.96% were journal articles, and 47.04% were conference papers. For engineering education, the split leaned heavily towards conference papers (66.50%) compared to journal articles (33.50%), and civil engineering education showed a similar trend with 57.07% conference papers and 42.93% journal articles. Third, the distribution across academic disciplines was assessed, focusing on natural sciences, life sciences and health, engineering and technology, social sciences and humanities, and other studies. Lastly, global distribution revealed that about 30% of studies came from the US and China, with 59,441 from the US and 49,596 from China, underscoring their dominance in educational research. However, in engineering education, Spain led with 11.36%, followed by Germany at 10.13%, while in civil engineering education, Italy ranked first with 0.90%, followed by Spain at 0.81%. Further, a bibliometric analysis was conducted to quantitatively assess the impact and relationships within the literature in different academic disciplines and CAR technologies. This involved analyzing keyword co-occurrence, average yearly publications, and citation data to gain deeper insights into the research field's structure and development. Finally, the studies were meticulously screened using established inclusion and exclusion criteria to ensure that only relevant, high-quality studies were retained. The selection of studies was periodically revised based on insights from the statistical analysis and initial literature findings. After a final exclusion phase, 359 papers were retained for detailed analysis, with the majority being journal articles. This rigorous process ensured that the review captured a comprehensive and relevant body of literature, thereby significantly contributing to the field of civil engineering education and the integration of CAR technologies. Once a final selection of studies has been made, the next step is to perform a detailed review analysis. This involves the evaluation of each study and progress through qualitative and quantitative syntheses. The qualitative data synthesis starts with a keyword co-occurrence network within civil engineering education. Central nodes such as “civil engineering,” “education,” “student,” and “curriculum” are prominently linked to “teaching,” “e-learning,” and “virtual reality.” The network's visualization over time indicates a shift from initial studies focused on foundational educational themes to more recent studies embracing advanced technologies such as AI and VR. The analysis of the 359 publications further explores various commonalities among them, including the objectives, outcomes, limitations, and recommendations that often recur. Objectives are centered around enhancing civil engineering education by integrating BIM across curricula, adopting immersive technologies like VR and AR for better educational experiences, and fostering interdisciplinary collaboration. The outcomes from the implementation of these CAR technologies were notably positive, with enhanced student engagement and motivation, improved 3D visualization and spatial understanding, and better readiness for real-world challenges. Additionally, the recommendations underscore the need for future research to focus on expanding the integration of CAR technologies, improving accessibility and affordability, and ensuring effective integration of BIM, GIS, and IoT systems. The quantitative analysis provides insights into the global distribution of the studies and the specific technologies utilized. The US leads with 86 publications, followed by China with 56, highlighting significant investments in civil engineering education. European contributions are also notable, with Spain (21 publications), Germany (9), and Turkey (8) emphasizing the integration of digital tools and practical applications of immersive technologies. In terms of technology distribution, BIM is the most prominent, accounting for 23.53% of the studies, followed by VR and AR, which represent 21.76% and 13.82%, respectively. These statistics highlight the foundational role of BIM in modern civil engineering education and the growing importance of immersive learning environments. The primary focus on students, who are involved in 238 studies, demonstrates the significant engagement of these technologies in educational contexts, with professionals and engineers also notably involved in many studies. Each technology offers unique benefits and challenges, shaping its application and effectiveness in learning environments, which include interaction with the real world, applications, level of immersion, hardware requirements, advantages, limitations, industry use cases, cognitive benefits, social interaction, cost considerations, and ethical concerns. As a final step, the review integrated identified trends, challenges, and opportunities into a broader discussion, synthesizing key findings into actionable insights and exploring future research directions. This stage leverages the review's comprehensive nature to provide a forward-looking perspective that is valuable both academically and practically. By identifying opportunities for innovation, recognizing field-specific challenges, and analyzing emerging trends, the review offers a roadmap aligning these elements with actionable future research directions. This not only enhances the credibility of the review but also establishes a clear pathway for developing actionable insights in civil engineering education. After a detailed analysis of 359 studies, ten primary characteristics affecting CAR technologies in civil engineering education were identified. These insights reveal critical deficiencies across multiple domains: 1. Ethical and Psychological Concerns : With the highest prevalence at 95.31%, this category underscores a significant lack of focus on ethical considerations and psychological impacts, such as privacy, data security, and VR-induced discomfort, highlighting a major area for future research. 2. Sample Size and Representativeness : Approximately 64.82% of studies suffered from vague or small participant groups, limiting the generalizability of findings and underscoring the need for larger, more representative samples. 3. Lack of Real-World Implementation : About 56% of the studies did not advance beyond theoretical or controlled environments, pointing to a gap in real-world application and validation. 4. Neglect of Instructor Perspectives : Nearly 46% of studies overlooked educator feedback, limiting insights into the practicality of immersive technologies in educational settings. 5. Short-term Assessment : Focusing on immediate outcomes, 44.23% of studies lacked long-term evaluations, which are crucial for understanding sustained impacts. 6. Cost Considerations : Only 33.36% of studies addressed how financial constraints affect research and outcomes, highlighting a need for cost-effective technology solutions. 7. Infrastructure and Institutional Support : A notable barrier, with insufficient support limiting the scalability of immersive technologies. 8. Software and Tool Limitations : About 21.17% of studies noted the limitations of tools like Unity or Oculus Rift, impacting participant engagement and study outcomes. 9. Complexity and Scalability Issues : Only 14.01% of studies detailed how they addressed technical challenges, which is critical for replication and scalability. 10. Measurement Tools : Just 6.62% of studies used standardized methods for outcome assessment, underscoring a need for reliable metrics. The integration of CAR technologies offers numerous opportunities to enhance civil engineering education, from simplifying the adoption of immersive tools to enhancing learning through advanced simulations and real-time feedback. However, several challenges, such as high costs, technical requirements, and integration complexities, must be carefully managed. To address these issues, future research should focus on developing advanced visualization tools that provide immersive experiences and real-time feedback, enhancing global accessibility, validating real-world applications, and fostering collaborations that align technological development with real-world needs. Additionally, economic studies could guide cost-effective solutions, promoting wider adoption of these transformative educational tools. This research, therefore, highlights the transformative potential of CAR technologies in civil engineering education, emphasizing their ability to foster adaptive, scalable, and accessible learning environments. The findings will not only inform policy and curriculum development but also equip future engineers to meet the evolving challenges of the profession. Abbreviations AI Artificial Intelligence AR Augmented Reality BIM Building Information Modelling CAR Computer-altered Reality XR Extended reality EBSCO Elton Bryson Stephens Company GIS Geographic Information Systems IoT Internet of Things ML Machine Learning MR Mixed Reality NEP National Education Policy R&D Research and Development STEM Science, Technology, Engineering, and Mathematics UAVs Unmanned Aerial Vehicles VR Virtual Reality WOS Web of Science Declarations Funding: No funding was received to assist with the preparation of this manuscript. 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04:59:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1320478,"visible":true,"origin":"","legend":"\u003cp\u003eVOSviewer\u003csup\u003e©\u003c/sup\u003e keyword co-occurrence network for three academic disciplines: a) education overall (top), b) engineering education (middle), and c) civil engineering education (bottom)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/2168a7a5a40e3f5953b8830f.png"},{"id":76073336,"identity":"29d48630-db1f-4e79-8fe6-95e755b3dabb","added_by":"auto","created_at":"2025-02-12 04:59:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":429733,"visible":true,"origin":"","legend":"\u003cp\u003eVOSviewer\u003csup\u003e©\u003c/sup\u003e average yearly publication network for three academic disciplines: a) education overall (top), b) engineering education (middle), and c) civil 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10","display":"","copyAsset":false,"role":"figure","size":91203,"visible":true,"origin":"","legend":"\u003cp\u003eExpanded subprocess of “Include and Exclude Studies” process in the proposed methodology\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/fc20e048fb19e91007d11cee.png"},{"id":76073481,"identity":"fe75249f-d5e7-41ab-af1c-0276f98ecc95","added_by":"auto","created_at":"2025-02-12 05:07:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":63497,"visible":true,"origin":"","legend":"\u003cp\u003eExpanded subprocess of “Conduct Review Analysis” process in the proposed methodology\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/b56c73dc67309214e5bb9daf.png"},{"id":76073359,"identity":"18a87317-4f75-4709-b051-dfa76a59312b","added_by":"auto","created_at":"2025-02-12 04:59:46","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":398622,"visible":true,"origin":"","legend":"\u003cp\u003eVOSviewer© networks for civil engineering education after the “Include and Exclude Studies” process: a) keyword co-occurrence (top), b) average publication year (bottom left), and c) average normalized citations (bottom right)\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/83ac42e43882da4c198895d3.png"},{"id":76075425,"identity":"88720771-a7c3-4a03-bf10-d7f9aa0baa7e","added_by":"auto","created_at":"2025-02-12 05:32:26","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":110959,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of objectives, outcomes, limitations, and recommendations to integrate advanced technologies in civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/07040ad5d17f9313a4d23bee.png"},{"id":76073345,"identity":"718f5d53-7dc5-44c6-a83b-ec2a1f4af04c","added_by":"auto","created_at":"2025-02-12 04:59:45","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":128547,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of publications for civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/7d77711f0091c0478ec4af5d.png"},{"id":76073355,"identity":"1ee8b7d7-147d-4605-929a-104549b276f6","added_by":"auto","created_at":"2025-02-12 04:59:46","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":84465,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of various technologies within civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/8435ba7993f715e1a1ca9d28.png"},{"id":76075426,"identity":"21e12504-56da-4faf-ade8-0a4ba6850f28","added_by":"auto","created_at":"2025-02-12 05:32:31","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":54892,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of focus disciplines in civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/cb1d741a6851012f7895fbe6.png"},{"id":76073351,"identity":"449f8d01-4d8e-484c-a6e9-17a18be3c7a3","added_by":"auto","created_at":"2025-02-12 04:59:46","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":63323,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of target groups in studies using immersive technologies for civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/b2700a9f42a4b274c39410b2.png"},{"id":76073343,"identity":"718c558f-08b1-4a2e-bf11-3837035e586a","added_by":"auto","created_at":"2025-02-12 04:59:45","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":33203,"visible":true,"origin":"","legend":"\u003cp\u003eTypes of software used and the corresponding number of research studies (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/836de6391dbcc68e87dd4b00.png"},{"id":76073490,"identity":"fd1b87cb-6ee7-40da-a170-0ba31f5f7016","added_by":"auto","created_at":"2025-02-12 05:07:48","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":82031,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage distribution of methodological characteristics in civil engineering education (for a total of 359 related studies)\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/1ede8d8f94e3095afe19a5f3.png"},{"id":76075424,"identity":"f6bb4172-e4a2-4ae4-abbd-331b53597690","added_by":"auto","created_at":"2025-02-12 05:32:23","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":196186,"visible":true,"origin":"","legend":"\u003cp\u003eOpportunities, trends, challenges, and future research directions of CAR Technologies in civil engineering education\u003c/p\u003e","description":"","filename":"20.png","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/a5bb7a193a943b7fb5524cd3.png"},{"id":76075434,"identity":"8b799f9f-aad8-4afd-8d4a-53c8d0426266","added_by":"auto","created_at":"2025-02-12 05:32:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6438343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5996662/v1/2a3df15b-10bd-4ef9-8590-4848d8c70d33.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdvancing Civil Engineering Education: A Systematic Review of Opportunities, Trends, Challenges, and Future Research Directions in Computer-Altered Reality Technologies\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe primary purpose of pedagogy and education is to equip students with the knowledge, skills, and competencies needed to succeed as future professionals, leaders, and innovators. Traditionally, this has been achieved through lectures, textbook-based learning, and standardized testing. However, these conventional methods often fall short of meeting the dynamic needs of today\u0026rsquo;s students and the demands of modern industries. For instance, studies reveal that passive lecture-based learning leads to lower retention rates, with students retaining only about 5% of the information presented in lectures, compared to 75% from hands-on practice or experiential learning (Ho, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cem\u003eLearning Pyramid In Demonstrating 7 Levels of Understanding\u003c/em\u003e, 2023). Moreover, despite billions of dollars invested globally in educational infrastructure and resources, student satisfaction and learning outcomes frequently lag behind expectations. In the U.S. alone, higher education spending exceeds \u003cspan\u003e$\u003c/span\u003e600\u0026nbsp;billion annually, yet a significant proportion of graduates report feeling unprepared for real-world challenges (Irwin et al., 2021). In the UK, recent surveys show that over 30% of students believe they are not receiving value for their tuition fees, citing outdated teaching methods as a major concern (Naves \u0026amp; Hewitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese statistics underscore a growing dissatisfaction with traditional educational approaches, which often lack engagement, fail to promote critical thinking, and inadequately prepare students for complex, real-world problems. To address these challenges, educational institutions are increasingly adopting innovative pedagogical methods such as immersive technologies, project-based learning, and adaptive AI-driven platforms that aim to create a more interactive, personalized, and effective learning experience. These advancements not only enhance student engagement but also align more closely with industry demands, thereby fostering a new generation of adaptable and skilled professionals (Wang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among these advancements, computer-altered reality (CAR) stands out as an up-and-coming technology capable of creating immersive and interactive learning environments. CAR refers to a set of technologies that alter the user\u0026rsquo;s perception of reality, either by immersing them thoroughly in a virtual environment or by overlaying digital elements onto the physical world. As educators seek to bridge the gap between traditional teaching methodologies and the dynamic demands of contemporary industries, CAR is emerging as a powerful tool for improving learning outcomes across both theoretical and practical domains (Eden et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The benefits of CAR in education go beyond enhancing conceptual understanding, as these technologies also foster greater student engagement and motivation. One of the persistent challenges in education is maintaining student interest, particularly in subjects perceived as abstract or complex. CAR addresses this issue by transforming passive learning into an active, interactive experience. CAR technologies also support collaborative learning by allowing multiple users to interact within the same virtual or augmented environment. This capability is precious in disciplines such as engineering and design, where teamwork and problem-solving are essential skills. Students can collaborate on virtual projects, receive real-time feedback, and develop the critical skills necessary for professional success (Schuster et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Definitions of Computer-Altered Reality Technologies\u003c/h2\u003e \u003cp\u003eCAR encompasses a range of technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and other immersive approaches, offering the potential to revolutionize educational experiences by providing students with engaging, hands-on approaches that enhance both understanding and retention of complex concepts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. VR refers to a fully immersive digital environment that replicates real-world or imagined scenarios through computer-generated simulations (Brey, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ogrizović et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This immersion is typically achieved through headsets that block out the real world, enabling users to conduct virtual experiments, explore historical sites, or practice complex procedures in disciplines such as engineering and medicine (Radianti et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In educational contexts, VR has gained significant attention for its ability to enrich learning experiences by offering students the chance to participate in virtual simulations, conduct experiments, and explore complex systems (DeLanzo, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ramadhanya, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This technology is particularly effective in enhancing spatial understanding and problem-solving skills within a safe, controlled environment, making it a valuable tool in engineering education (FutureLearn, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, history students can use VR to virtually explore historical landmarks and events, creating a dynamic and engaging way to learn about the past (Allison, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, in medical education, for instance, students can practice surgeries or diagnostic procedures in a virtual environment (Buono et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while engineering students can simulate construction projects to test the effects of different materials without facing real-world consequences such as loss of time and resources(Kassem et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAR, on the other hand, overlays digital information in the real world, allowing users to interact with both digital models and their physical surroundings (Arena et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). AR is commonly implemented in mobile devices or wearable technology such as smart glasses, facilitating interactive learning experiences like viewing 3D models during science lessons or receiving real-time annotations during hands-on activities (Dong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). AR offers substantial potential in educational disciplines, especially in disciplines like engineering, where students can visualize and manipulate digital designs or technical data in real-time (Alvarez-Marin \u0026amp; Velazquez-Iturbide, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, AR can provide additional support or challenges based on student progress, ensuring that each learner\u0026rsquo;s needs are met (Mavroudi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This adaptability is especially useful in subjects where students may require varying levels of assistance. For instance, in mathematics, AR can project complex geometric shapes or graphs into the real world, allowing students to visualize and interact with them to understand abstract concepts like vectors or calculus better. Similarly, in language learning, AR can overlay translations and pronunciation guides onto real-world objects, helping students associate words with their meanings and improve their language skills through immersive interaction.\u003c/p\u003e \u003cp\u003eMR, which merges both VR and AR, enables users to engage with digital and physical elements simultaneously (Rokhsaritalemi et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This blending of the digital and physical worlds opens up opportunities for students to manipulate virtual models in real-world contexts, making MR valuable to many disciplines, such as medical training, design visualization, consumer experience, forensic reconstruction, and historical studies (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In an educational discipline, MR facilitates interaction with digital objects within real-world contexts, offering students a richer, more collaborative, and hands-on learning experience (Vasilevski \u0026amp; Birt, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, a study explored mixed reality environments within youth media practices to understand how metaverses are shaping interactive narrative experiences for young audiences (Prieto et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study focused on three primary aspects: first, it investigated the emergent design of metaverses, tracing their evolution from early concepts to their present technological and social developments. Second, it examined popular media platforms among young people, analyzing how these platforms\u0026rsquo; interfaces engage users, particularly in the narrative aspects. Finally, it discussed the emerging metaverse models that resonate with young audiences, offering insights into how these virtual environments might evolve to align with youth preferences and media consumption trends.\u003c/p\u003e \u003cp\u003eOther emerging technologies like haptic feedback devices, AI-driven adaptive learning systems, spatial computing, etc. are also transforming education. Haptic technologies enable tactile feedback in virtual environments, allowing students to \u0026ldquo;feel\u0026rdquo; textures or resistance, which is particularly useful in fields like design or physical therapy training. AI-powered learning systems customize educational content based on student progress, ensuring personalized learning trajectories and addressing individual weaknesses. Spatial computing, which integrates sensory data with AI and real-world inputs, provides an enhanced level of interaction and realism. For example, this technology can enable group projects in engineering, where students collaboratively build and test virtual prototypes within their physical classrooms.\u003c/p\u003e \u003cp\u003eFinally, extended reality (XR) serves as an umbrella term that encompasses VR, AR, and MR, referring to all immersive technologies that either fully immerse or enhance the user\u0026rsquo;s experience through the combination of virtual and physical worlds (de Giorgio et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). XR technologies expand the possibilities for educational applications, creating new opportunities for interactive learning by integrating virtual simulations, augmented overlays, and real-time interactions. In engineering education, XR enables students to seamlessly transition between entirely digital experiences, augmented real-world interactions, and hybrid environments, providing flexibility and depth in learning complex, technical subjects.\u003c/p\u003e \u003cp\u003eThese technologies are increasingly integrated across various educational disciplines, particularly in engineering, where they aid in the comprehension of complex concepts and foster more interactive, engaging learning environments. Recognizing these trends ensures that the review is structured to address the critical technological advancements shaping the educational landscape, offering a comprehensive framework for further analysis and guiding future research into the incorporation of CAR technologies in education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Overview of Existing Reviews\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the summary of 13 selected existing review studies out of a total of 30 studies analyzed in the realm of CAR technologies within education. These studies are categorized into various disciplines and sub-disciplines, including education (general and overall), engineering education, and civil engineering education, to emphasize their specific contexts. The primary objective is to compare and contrast the findings from these studies to identify trends, strengths, and gaps that are particularly relevant to civil engineering education. From the summarized data, it is evident that while CAR technologies have been widely applied in general and engineering education, their targeted use in civil engineering education remains underexplored. The table highlights key observations, such as the effectiveness of CAR in enhancing conceptual understanding, promoting engagement, and offering immersive learning experiences. However, it also reveals gaps, including limited studies on long-term impacts and a lack of standardized assessment frameworks. These insights set the stage for adopting and adapting successful approaches to better address the unique needs of civil engineering education.)\u003c/p\u003e \u003cp\u003eRecent studies have extensively explored the application of CAR technologies across various educational domains, including architecture (Casa\u0026ntilde;as et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hajirasouli \u0026amp; Banihashemi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), engineering (Dong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Espinoza et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kassem et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vergara et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), medicine (Buono et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sepasgozar, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), management (Pav\u0026oacute;n et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and higher education (Di Natale et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Onecha et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Over the past decade, CAR technologies have seen a significant increase in use within civil engineering education. However, while the literature emphasizes the transformative potential of these technologies, it often lacks longitudinal studies examining their sustained impact on educational outcomes and professional readiness.\u003c/p\u003e \u003cp\u003eFor instance, a review by Nagaraj et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) evaluated 82 studies focused on XR technologies in manufacturing engineering education, covering the period from 1999 to 2020. While the review highlighted the effectiveness of XR in enhancing training and educational outcomes through immersive, hands-on experiences, it did not address critical aspects such as long-term knowledge retention or the scalability of these technologies in diverse educational settings. Similarly, a 2023 study observed that 45 out of 572 articles published between 2017 and 2022 highlights the growing importance of XR in architectural design education, underlining the transformative potential of these technologies in architectural education (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this study did not explore the barriers to adoption, such as institutional costs and technical challenges, which remain significant limitations for widespread implementation.\u003c/p\u003e \u003cp\u003eSpecific applications of CAR technologies, such as VR and AR, have also been explored in more specialized educational contexts. For example, Bartels and Hahne (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasized the advantages of using VR to simulate construction processes, which enable students to better grasp the sequential and spatial aspects of projects. Nevertheless, their work did not consider the potential cognitive overload students might face or the accessibility of such simulations for institutions with limited resources. On the other hand, AR\u0026rsquo;s capability to provide real-time overlays of structural data on physical models (Schall et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) offers intuitive insights into complex structural behaviors. These applications provide students with an intuitive understanding of complex structural behaviors, bridging the gap between theoretical concepts and practical applications.\u003c/p\u003e \u003cp\u003eMoreover, artificial intelligence (AI) has also emerged as a critical element in the evolution of education in engineering. While N\u0026uacute;\u0026ntilde;ez \u0026amp; Lantada (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted AI\u0026rsquo;s role in simulating complex engineering problems, the scalability of such systems for large classrooms or diverse learning environments remains unaddressed. Similary, Wang et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further explored AI\u0026rsquo;s role in education, demonstrating that AI-driven analysis tools can assist students in identifying mistakes and providing real-time solutions during classroom activities. The fusion of AI with AR and VR technologies offers a multifaceted approach to experiential learning, particularly in disciplines requiring hands-on problem-solving. Chen et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed an AI-integrated VR system that adapts to individual student\u0026rsquo;s learning pace, creating personalized problem-solving scenarios in structural engineering. Similarly, Devagiri et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showcased an AI-powered AR system designed for real-time site inspections, enabling students to visualize structural anomalies and receive AI-driven feedback. This system effectively bridges the gap between theoretical knowledge and practical application, offering real-world insights into educational disciplines.\u003c/p\u003e \u003cp\u003eFurther validating these findings, Hwang and Chien (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a meta-analysis revealing that courses integrating AI with AR or VR report a 30% increase in student engagement and a 25% improvement in knowledge retention compared to traditional methods. However, the analysis does not address the long-term efficacy of such integrations or the challenges of scaling these technologies for diverse student demographics. Lin et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also noted that connectivity, processing power, and initial setup costs remain critical challenges for adopting AI-integrated CAR systems. Additionally, the need for continuous training of educators to effectively use these technologies highlights another research gap.\u003c/p\u003e \u003cp\u003eIn terms of future directions, ongoing research by Pan and Zhang (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) focuses on AI\u0026rsquo;s predictive capabilities integrated with VR simulations. This research aims to enhance students\u0026rsquo; problem-solving skills by presenting them with potential future scenarios in civil engineering projects. The convergence of AI, AR, and VR in civil engineering education is reshaping educational approaches with an emphasis on experiential and adaptive learning. This integration not only improves student engagement and retention but also prepares students for the evolving demands of the civil engineering profession.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevious review work related to the CAR in engineering education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDiscipline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNo. of Studies Reviewed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTime periods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReview type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGeographical Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStudy Limitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Ouyang \u0026amp; Zhang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFew design principles for AI-driven tools and insufficient research on integrating multimodal data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Nagaraj et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCritical Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUSA, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLimited empirical research on AI\u0026rsquo;s long-term impact on STEM education and a lack of diverse case studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Obeidallah et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLimited studies on XR\u0026rsquo;s long-term impact and its effectiveness across various disciplines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Tang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLack of research on the long-term effectiveness and cost-efficiency of immersive technologies in medical education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Di Natale et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMethodological flaws such as small sample sizes and non-randomized trials limit generalizability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEngineering Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(de Giorgio et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSweden, Italy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eInconsistent quantitative data on XR\u0026rsquo;s effectiveness and lack of comprehensive evaluations in manufacturing education.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Tan et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFew studies on long-term effectiveness and limited interdisciplinary applications of AR/VR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Spitzer et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFramework Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLack of clear guidance in the literature for selecting appropriate XR technologies in educational settings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Hajirasouli \u0026amp; Banihashemi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSignificant gap in developing pedagogies and teaching methods that effectively integrate AR technologies into the architecture and construction curriculum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Diao \u0026amp; Shih, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTaiwan, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eInsufficient focus on long-term studies measuring the impact of AR on student learning outcomes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCivil Engineering Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBibliometric Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChina, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBIM education is predominantly limited to engineering management, with insufficient integration with computer and IT disciplines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCritical Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAustralia, China, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFew studies address the integration of VR with new education paradigms, and limited focus on improving depth perception and comfort with VR equipment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Sampaio et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eInsufficient integration of VR with real-world projects and a lack of comparative studies on traditional versus VR methods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Gaps and Objectives\u003c/h2\u003e \u003cp\u003eDespite the growing interest in integrating technology into educational frameworks, the specific combination of emerging technologies and CAR in civil engineering education remains significantly underexplored. While current literature provides valuable insights into the broader applications of AR and VR, it often lacks a detailed examination of the combined benefits and challenges that arise when these technologies are paired with CAR tools. Additionally, there is a notable absence of comprehensive methodologies that integrate these technologies, coupled with a clear understanding of how they can transform traditional educational models within civil engineering. This gap is particularly evident in civil engineering education, where technology has the potential to address long-standing challenges, such as the visualization of complex structural concepts and the simulation of real-world scenarios. However, most studies focus either on isolated applications of AR or VR or their general implementation in education, leaving the synergistic use of CAR tools with other technologies, such as AI, the Internet of Things (IoT), and haptic feedback systems relatively unexamined. Therefore, the main aim of this review is to conduct a structured, systematic, and comprehensive analysis of multiple CAR technologies used in educational disciplines, with a specific focus on civil engineering education, to determine their opportunities, trends, challenges, and future research directions. This review seeks to bridge the gap between current technological capabilities and their potential to revolutionize the teaching and learning experience in this field. The main objectives of this study are to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCompare and contrast the adoption and implementation of CAR technologies in civil engineering education with other engineering disciplines and general education, identifying key similarities and differences.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExamine unique challenges specific to civil engineering education and explore potential lessons that can be adapted from successful implementations in other disciplines.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEvaluate the current state-of-the-art integration of CAR technologies in civil engineering education and their multifaceted impact, including student learning outcomes and teaching practices.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePropose actionable research directions and strategies to enhance the accessibility, scalability, and real-world applicability of CAR technologies in civil engineering education.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eObjectives 1 and 2 establish a foundational understanding by situating civil engineering within the broader context of CAR adoption and identifying its unique challenges. Objective 3 delves deeper into assessing the current integration and its impacts within civil engineering education, synthesizing findings from Objectives 1 and 2. Objective 4 builds on this synthesis to propose future strategies and research directions, providing a logical conclusion and actionable insights for advancing the field.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. outlines the proposed structured nine-step methodology to achieve the objectives mentioned above. The first step involves analysis of the literature to identify different themes. This can be achieved through different ways, such as categorization, taxonomy, and clustering. These themes serve as the foundation of the structure of the review, which ensures that the review remains focused on key issues in the field. After the themes were identified, a review strategy was outlined. This involves selecting appropriate methodologies, identifying tools and databases to search, and setting a clear timeline for the review. A well-articulated review strategy ensures that the literature search is comprehensive and systematically conducted. The next step is to define specific keywords and keyword combinations based on the identified themes. These keywords will be used to search relevant databases, ensuring that the literature retrieved is focused on answering the critical research questions.\u003c/p\u003e \u003cp\u003eA comprehensive search is conducted using the determined keywords across selected databases. This process helps to gather a broad but focused collection of literature relevant to the review\u0026rsquo;s objectives. After conducting the initial search, the preliminary results of the search are compiled and summarized. This step involves an initial review of the studies identified during the search process, with a focus on highlighting key themes and trends within the literature. These preliminary results serve as a foundation for further refinement and provide an early indication of the direction of the research. The results may highlight areas that require additional focus or refinement in the review strategy or keywords, ensuring the research remains on track and targeted. Moreover, statistical analysis is performed for quantitative insight. This statistical evaluation helps in understanding the impact of specific studies and authors in the field, as well as emerging trends. Citation analysis, network mapping, and other tools may be utilized to identify influential works and patterns within the literature.\u003c/p\u003e \u003cp\u003eThe retrieved studies are screened using pre-established inclusion and exclusion criteria. This step ensures that only high-quality, relevant studies are selected for further analysis, improving the severity of the review. After screening, the selection of studies is revised. This step involves evaluating whether any additional refinement of the included literature is necessary. If needed, the keywords and combinations are refined based on the insights gained from the statistical analysis and the initial findings of the literature review. This process ensures that the search captures a wider and more relevant pool of literature for subsequent searches.\u003c/p\u003e \u003cp\u003eOnce the final selection of studies is made, the next step is to perform a detailed review analysis. This involves synthesizing the literature, identifying gaps, and discussing the implications of the findings. This analysis helps to establish a comprehensive understanding of the current state of research and sets the stage for further detailed comparison. As a final step, the trends, challenges, and opportunities identified through the review are integrated into a broader discussion. This step synthesizes key findings into actionable insights and explores future research directions. By identifying opportunities for innovation, recognizing challenges faced by the field, and analyzing emerging trends, this stage ensures that the review provides a forward-looking perspective, offering valuable contributions to the academic community and practitioners.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Identify Different Themes\u003c/h2\u003e \u003cp\u003eIn any review, identification of the main themes is a foundational step that helps frame the scope of the research and comprehensive coverage of all relevant aspects of the subject. Thematic identification provides researchers with a clear framework for analyzing key areas of focus, which is particularly important in multidisciplinary fields such as in the context of this study, where technology intersects with education. By identifying these core themes, researchers can structure their analysis more effectively, ensuring a thorough exploration of the literature. In this review, thematic identification aims to explore various themes related to the overall integration of CAR technologies in education, with a specific focus on engineering education, especially within the context of civil engineering.\u003c/p\u003e \u003cp\u003eFollowing the rationale extensively discussed in Section 1.2, which elaborates on the increasing role of technology in enhancing education outcomes, three primary themes have been identified: VR, AR, and MR, which encompass the broader umbrella of CAR, as discussed earlier. These themes are prominent due to their growing influence in both the educational and industrial sectors. For instance, recent reports show that venture capital investment in CAR technologies has surged by 30\u0026ndash;40% over the past year, and this trend is expected to continue (McKinsey, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the adoption of these identified themes in educational disciplines has been linked to improved student engagement, spatial understanding, and hands-on experience, especially in disciplines like civil engineering, where visualization and simulation are critical.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Outline Review Strategy\u003c/h2\u003e \u003cp\u003eA strong review strategy is essential to ensure that the literature retrieved is comprehensive, relevant, and of high quality. A systematic and well-defined search strategy allows researchers to gather studies from reliable sources, ensuring that all relevant publications on the topic are considered. This step involves not only selecting the most appropriate databases but also establishing search parameters that align with the objectives of the review. By employing advanced search techniques, such as Boolean operators and wildcards, researchers can refine their searches and focus on the most pertinent studies while efficiently excluding irrelevant papers.\u003c/p\u003e \u003cp\u003eOnce the key themes are identified, the next step is to define search characteristics and select the most suitable databases for conducting a thorough systematic search. Databases such as Scopus, Web of Science (WoS), IEEE Xplore, and others are critical in gathering a broad yet focused collection of studies. For this review, the Elton Bryson Stephens Company (EBSCO) database was chosen, which includes both WoS and the combined databases of Elsevier and Scopus (Chadegani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This selection was made to ensure comprehensive coverage of high-impact, peer-reviewed studies, as EBSCO provides access to a wide range of academic publications across various disciplines. By utilizing WoS and Scopus, which collectively capture over 90% of peer-reviewed journals globally, this review ensures thorough coverage of the most relevant and influential studies (Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu \u0026amp; Liu, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The combination of these databases was optimal for identifying studies related to CAR technologies in education and engineering.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the detailed search criteria, and the rationale behind these selections is as follows. Initial searches revealed that the majority of relevant papers were published in English, leading to the decision to limit the search to English-language publications. This approach enhanced the relevance and accessibility of the results. Both articles and conference papers were included, as these formats typically provide the most current and relevant research on emerging technologies in educational and engineering disciplines. The search was further limited to the years 2014 to 2023, reflecting the period when immersive technologies, such as VR, AR, and MR, began gaining significant traction in educational and engineering disciplines. The rationale for selecting this 10-year window stems from the rapid technological advancements and increased adoption of immersive technologies in education during this timeframe. Prior to 2014, the development and application of these technologies were relatively nascent, with limited large-scale implementation in educational disciplines (Mohsen \u0026amp; Alangari, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, since 2014, immersive technologies have evolved considerably and become more accessible, driven by advancements in both hardware and software, making this period crucial for capturing the most relevant studies on their impact on education (Bermejo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Limiting the review to this period ensures the focus remains on the latest technological developments and research while excluding older studies that may no longer reflect the current state of immersive technology in education. To refine the search results and maintain relevance, Boolean operators such as \u0026ldquo;AND\u0026rdquo; and \u0026ldquo;OR\u0026rdquo; were employed. For example, the search strings \u0026ldquo;Virtual Reality\u0026rdquo; AND \u0026ldquo;Education\u0026rdquo; were used to target studies that intersected both disciplines, while OR was used to include related concepts, such as \u0026ldquo;immersive learning environments\u0026rdquo;.\u003c/p\u003e \u003cp\u003eAdditionally, wildcard symbols (e.g., *) were used to capture various forms of key terms. For example, \u0026ldquo;augmented realit*\u0026rdquo; was used to include variations such as \u0026ldquo;augmented realities\u0026rdquo; or \u0026ldquo;augmented reality-based systems\u0026rdquo;. To ensure the literature retrieved was highly relevant to the review\u0026rsquo;s objectives, searches were conducted across the title, abstract, and keywords fields. This approach ensured that the selected literature was directly aligned with the goals of the review. By employing the combined resources of EBSCO, including both WoS and Elsevier-Scopus, this review strategy ensured that the literature search was exhaustive and methodologically sound, capturing a wide range crucial for understanding the role of CAR technologies in education. This comprehensive strategy not only identifies existing trends but also highlights the most influential work in the field, facilitating an informed and well-rounded analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of search characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSearch type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced search\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocument type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArticles and conferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBooleans used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAND - OR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced search tool used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWildcard (*)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSearches within\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTITLE-ABS-KEY (Article title, abstract, and keywords)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determine Keywords and Combinations\u003c/h2\u003e \u003cp\u003eSelecting appropriate keywords and their combinations directly influences the scope and depth of the literature search, determining the inclusion of relevant studies while excluding irrelevant ones. By strategically combining keywords and using appropriate exclusion terms, researchers can refine their searches to target studies that align with the objectives of the review. In this review, the keyword selection process was designed to narrow the scope of the search systematically. The process began with broad research related to the use of CAR in education, followed by a more focused exploration of its application within engineering education and, finally, civil engineering education. This strategy ensured that the review captured a comprehensive range of literature, first addressing general educational applications before moving toward the specific domain of civil engineering. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the final keyword combinations used throughout the review process. The first column of keywords contains different terms that are synonymous with and otherwise pertaining to CAR technologies. For example, \u0026ldquo;virtual reality (VR)\u0026rdquo; and \u0026ldquo;artificial reality\u0026rdquo; are synonymous, while \u0026ldquo;augmented reality (AR)\u0026rdquo; and \u0026ldquo;building information modeling (BIM)\u0026rdquo; refer to specific technologies within the broader category of CAR. Other terms like \u0026ldquo;extended reality (XR)\u0026rdquo; and \u0026ldquo;computer-altered reality\u0026rdquo; highlight the diverse vocabulary used in the field, catering to the variety of terminologies adopted in different studies. The second column of keywords focuses on the educational context and includes terms such as \u0026ldquo;education,\u0026rdquo; \u0026ldquo;school,\u0026rdquo; \u0026ldquo;university,\u0026rdquo; and action-oriented words like \u0026ldquo;train*,\u0026rdquo; \u0026ldquo;teach*,\u0026rdquo; and \u0026ldquo;learn*.\u0026rdquo; These terms aim to capture literature that investigates CAR applications in various learning environments, from K-12 to higher education and professional training. The third column refines the scope of engineering disciplines. Under \u0026ldquo;Engineering Education,\u0026rdquo; the focus is on studies broadly addressing CAR technologies in engineering contexts. Meanwhile, for \u0026ldquo;Civil Engineering Education,\u0026rdquo; the search narrows further to include terms such as \u0026ldquo;civil engineering,\u0026rdquo; \u0026ldquo;survey,\u0026rdquo; and \u0026ldquo;civil engineering teaching,\u0026rdquo; which are specific to the sub-discipline.\u003c/p\u003e \u003cp\u003eDuring the initial phase, the search focused on the broader use of immersive technologies such as VR, AR, and MR in education overall. The terms \u0026ldquo;virtual reality,\u0026rdquo; \u0026ldquo;augmented reality,\u0026rdquo; and \u0026ldquo;mixed reality,\u0026rdquo; along with related phrases like \u0026ldquo;immersive simulation\u0026rdquo; and \u0026ldquo;extended reality,\u0026rdquo; were identified as the core search terms. These keywords were paired with education-related terms such as \u0026ldquo;teaching,\u0026rdquo; \u0026ldquo;learning,\u0026rdquo; and \u0026ldquo;student engagement\u0026rdquo; to capture a wide range of studies examining the use of immersive technologies in educational disciplines. However, throughout the search process, some adjustments were made to improve the relevance and scope of the results. For instance, the term \u0026ldquo;extended reality\u0026rdquo; was added, which provided a broader range of studies encompassing VR, AR, and MR technologies under a common term.\u003c/p\u003e \u003cp\u003eAs the review progressed, focusing more specifically on engineering education, additional keywords were incorporated. The second phase of the search added terms like \u0026ldquo;engineer*\u0026rdquo;, targeting how CAR technologies are being applied in technical and experiential learning disciplines, particularly within engineering disciplines. While \u0026ldquo;engineering education\u0026rdquo; initially seemed to provide relevant results, many studies pertained to general science, technology, engineering, and mathematics (STEM) education without a focus on immersive technologies (Owens \u0026amp; Hite, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address this, the search string was refined to include combinations like \u0026ldquo;immersive learning\u0026rdquo; AND \u0026ldquo;engineering\u0026rdquo;. This helped in narrowing down studies explicitly addressing the integration of CAR technologies in disciplines like mechanical, electrical, and civil engineering. This phase was crucial in identifying studies that address the challenges and opportunities of using CAR technologies in disciplines that require hands-on learning, such as engineering.\u003c/p\u003e \u003cp\u003eIn the final phase, the search was refined further to focus on civil engineering education. Keywords like \u0026ldquo;civil engineer*\u0026rdquo;, \u0026ldquo;civil*\u0026rdquo;, and \u0026ldquo;survey\u0026rdquo; were employed to locate studies discussing the application of immersive technologies within civil engineering disciplines. During this phase, it was noted that while \u0026ldquo;construction\u0026rdquo; yielded helpful studies related to engineering practices, it also returned studies focused on non-educational aspects of construction, such as project management in the field. To filter out these irrelevant results, the search was refined to include \u0026ldquo;construction education\u0026rdquo; or \u0026ldquo;civil engineering teaching\u0026rdquo;, which helped to limit results to educational disciplines. This phase aimed to understand how CAR technologies are transforming civil engineering education, focusing on improvements in teaching methodologies, student learning outcomes, and practical skills development. By using this phased approach, the search strategy ensured comprehensive coverage of CAR technologies from general to particular educational applications. The combination of these keywords facilitated the identification of relevant studies and minimized the inclusion of unrelated research.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReview strategy keywords combinations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEngineering/ Civil Engineering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVirtual reality OR VR OR artificial reality OR extended reality OR XR OR computer-altered reality OR CAR OR augmented reality OR AR OR virtual environments OR building information modeling OR BIM OR visualization OR \u0026ldquo;immersive technolog*\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAND\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation* OR school OR university OR student OR train* OR E-learning OR teach* OR learn* OR immersive learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAND\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEducation Overall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEngineer*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEngineering Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCivil engineer* OR civil* OR survey OR construction education OR civil engineering teaching OR geotechnical OR \u0026ldquo;structural\u0026rdquo; OR transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCivil Engineering Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Conduct Search\u003c/h2\u003e \u003cp\u003eOnce the search strategy was refined and relevant databases were selected, the search was conducted to collect relevant literature that was aligned with the objectives of the review. The focus during this phase was on ensuring that the gathered studies represented the broadest and most current range of research while remaining closely tied to the specific focus of CAR technologies in education overall, engineering education, and civil engineering education. The finalized keywords and combinations string after aggregating Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were entered into the EBSCO database to conduct a search using the search characteristics as described in section 2.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Perform Statistical Analysis\u003c/h2\u003e \u003cp\u003eA statistical analysis was conducted to evaluate the scope, distribution, and impact of the retrieved literature. This step was designed to both summarize the dataset and provide deeper insights into the relationships, patterns, and trends within the themes identified. The statistical analysis was performed in two main phases: a preliminary analysis, which provided an overview of the dataset (e.g., number and distribution of the articles), and a bibliometric analysis, which offered a more in-depth examination (e.g., structure, influence, and key contributions).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Preliminary analysis\u003c/h2\u003e \u003cp\u003eThe preliminary analysis was performed to capture the essential characteristics of the studies retrieved during the literature search, providing a foundational understanding of the dataset before proceeding further with an in-depth analysis. This analysis allowed for a thorough examination of the distribution and scope of research across different academic disciplines and educational themes. The analysis was conducted in four phases: (1) number of articles retrieved from the literature search, (2) distribution between conferences and journals, (3) distribution across academic disciplines, and (4) global distribution of studies. As mentioned in section 2.1, the literature search was conducted across multiple disciplines within the educational landscape, progressively narrowing the focus from general education topics to engineering education and, finally, civil engineering education. A total of 262,925 studies were obtained on the education overall topic, while engineering and civil education resulted in 25,424 and 1,646, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1) Number of Articles\u003c/b\u003e: Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the trends in the publication of research articles across three disciplines: education overall, engineering education, and civil engineering education over the period from 2014 to 2023. The dotted line with asterisks represents the overall trends in education publications, which are plotted on the right y-axis. The light orange bars represent the publication trends in engineering education, plotted on the left y-axis, while the red bars depict publication trends in civil engineering education, also plotted on the left y-axis. During the review period, the number of publications showed variability, with a consistent upward trajectory in all three disciplines, particularly in recent years. From 2014 to 2018, the number of publications in education overall grew steadily, rising from around 12,000 to 21,000 publications annually. However, from 2019, there was a noticeable acceleration in publication rates, with the total number of studies more than 50,000 in 2023, indicating a significant growth in research activities related to education as a whole. This sharp increase can be attributed to several factors, including rapid technological advancements, a growing global focus on educational reforms, the rise of digital learning platforms, the impact of the COVID-19 pandemic, and the drive towards innovation, which accelerated the integration of technology into educational settings.\u003c/p\u003e \u003cp\u003eA similar trend can be observed for engineering education as well, with the annual number of articles rising from approximately 1,200 in 2014 to around 4,000 by 2023. The growth in this category highlights the increasing emphasis on STEM education, the adoption of new teaching methodologies, and the use of immersive technologies like VR and AR in engineering disciplines. This trend also reflects the growing recognition of the need for innovation in engineering curricula to meet the demands of an evolving, technology-driven industry.\u003c/p\u003e \u003cp\u003eCivil engineering education, although a smaller subset compared to the other two categories, also shows a consistent upward trend. Starting from a relatively low base of fewer than 100 publications in 2014, the number of articles rose to nearly 400 by 2023. Despite the smaller volume, this growth highlights the expanding role of specialized educational research in civil engineering, likely driven by the need for more hands-on, practical learning approaches in management and infrastructure-related disciplines. The rise in civil engineering education research aligns with the increased use of simulation technologies, management tools, and sustainability-focused practices that are now critical components of modern civil engineering education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e2) Distribution Between Conferences and Journals\u003c/strong\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the distribution of publication types across the three disciplines. In education overall, the split between articles and conference papers is relatively balanced, with 52.96% being articles and 47.04% being conference papers. This suggests an equal reliance on journals and conferences for sharing research in this broad discipline. In contrast, engineering and civil engineering education show a higher shift toward conference papers compared to journal articles, with 66.50% and 57.07% versus 33.50% and 42.93%, respectively. This could be due to the applied nature of engineering disciplines, where conferences provide a valuable platform for engaging with industry experts and getting immediate feedback on practical developments. That is, they offer faster, more interactive ways to share and discuss cutting-edge developments with both academia and industry.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e3) Distribution Across Academic Disciplines\u003c/strong\u003e \u003cp\u003eTo further investigate the aggregated results, publications were initially distributed into different academic disciplines based on the preliminary research focus of the study by the search engines; however, these categories may be updated accordingly once the final categorization criteria are established. The five major academic disciplines chosen are namely natural sciences, life sciences and health, engineering and technology, social sciences and humanities, and other studies. These disciplines were selected based on their relevance to both the application of CAR technologies and their influence within the educational landscape. For instance, natural sciences and, engineering and technology are directly related to the use of advanced simulations, modeling, and virtual environments, which are highly applicable in educational contexts. Meanwhile, life sciences and health disciplines, although less represented in this dataset, have the potential for immersive training in areas like medical education and health sciences. Finally, social sciences and humanities and multidisciplinary studies were included to capture research that spans multiple academic disciplines and does not necessarily fit into one category.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the overview of this distribution with the total number of research papers published within each of the respective categories as a percentage within the node. The higher the percentage, the bigger the node, and vice versa. The figure highlights several key trends in the distribution of research across educational disciplines, with a notable dominance of the engineering and technology disciplines. For instance, the engineering and technology disciplines clearly dominate the research landscape, with computer science being particularly prominent across all three disciplines: education overall, engineering education, and civil engineering education. In education overall, computer science contributes 32.49% of the total research, while in engineering education, it represents 31.76%, and even in the more specialized discipline of civil engineering education, it accounts for a significant portion of research (approximately 25%). This shows the central role of computer science in educational research, likely due to the increasing focus on digital technologies, coding, artificial intelligence, and the need for STEM skills in modern education frameworks.\u003c/p\u003e \u003cp\u003eAs expected, disciplines such as mathematics and physics are prominently featured within engineering education and civil engineering education. These subjects are foundational to engineering principles, explaining their substantial representation. However, chemistry appears to have a relatively low proportion of research in comparison, despite its relevance to several engineering subfields (e.g., materials science, chemical engineering). This might indicate that chemistry\u0026rsquo;s direct applications in civil engineering education are less frequently explored in the context of immersive learning technologies, unlike mathematics and physics, which are more frequently integrated due to their fundamental role in civil engineering concepts like structural analysis and fluid mechanics.\u003c/p\u003e \u003cp\u003eInterestingly, a considerable portion of research within the three disciplines comes from the social sciences (approximately 5.72% in education overall, 8.57% in engineering education, and 10.02% in civil engineering education) and decision sciences (approximately 3.86% in education overall, 4.67% in engineering education, and 4.12% in civil engineering education). This trend reflects the increasing recognition within educational frameworks of the need to equip future engineers with increasingly essential soft and other skills such as communication, networking, and team work along with technical knowhow. Educational programs have been incorporating courses such as project management, collaboration, and stakeholder engagement to prepare students for the multifaceted challenges they will face in professional environments. These findings highlight the evolution of engineering curricula, which now frequently integrate leadership, team dynamics, and ethical decision-making as core components alongside traditional technical instruction. This shift in educational focus ensures that engineering students are not only proficient in technical skills but also capable of navigating the complex interpersonal and decision-making challenges inherent in real-world engineering projects, particularly in disciplines like civil engineering, where large-scale projects require coordinated, multidisciplinary efforts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e4) Global Distribution\u003c/strong\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the distribution map of the total number of reviewed studies over the past ten years (from 2014 to 2023), highlighting global research output across education overall, engineering education, and civil engineering education. The map uses color gradients to represent the total studies in each country for education overall, with darker shades indicating higher numbers of studies. The bar chart in the bottom left correlates to the percentage of research output per country in engineering education, showing a clear quantitative comparison among the top ten contributing countries. The percentages, in addition, indicate the research output per country in civil engineering education. Three main observations stand out from the data. The first observation is that around 30% of the studies were conducted in the United States (US) and China, with 59,441 studies from the US and 49,596 from China emphasizing their leading positions in educational research. The significant research output from the US and China can be attributed to their emphasis on STEM education, technological advancements (particularly in VR and AR), industry-academia solid collaborations, and government policies that prioritize research and development (R\u0026amp;D) in educational technologies (\u003cem\u003eCommuniqu\u0026eacute; on National Expenditures Science and Technology in 2020\u003c/em\u003e, 2021; Freyman, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors have driven the integration of CAR technologies into education disciplines, enhancing simulation-based learning and hands-on training. Interestingly, India stands out as a country that has made notable contributions despite being a developing nation. India contributed 23,278 studies to education overall, with 7.23% of research in engineering education and 0.56% in civil engineering education. This output is comparable to countries like the United Kingdom and Germany, which are known for their solid academic research ecosystems. This significant output is driven by government initiatives like Digital India and the National Education Policy (NEP) 2020, which promote technological integration and flexible learning models in education (Choudhary, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mahajan, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe second observation is that, despite the US and China leading in the total number of publications in education overall, they do not hold the highest percentages in engineering education or civil engineering education. In engineering education, Spain ranks first with 11.36%, despite being ranked 10th in education overall, followed by Germany with 10.13%. Meanwhile, in civil engineering education, Italy tops the list with 0.90%, followed by Spain at 0.81%. This highlights that although the US and China dominate general education research, European countries, particularly Spain and Italy, are leading contributors to engineering-specific research, particularly in civil engineering.\u003c/p\u003e \u003cp\u003eThe third observation is the relatively low number of studies conducted in developing countries, which contribute around 9% of the total research output in education overall. This percentage is primarily made up of contributions from countries in Africa, South Asia (excluding India), and parts of South America. While these countries are making contributions to the field, they may be focusing on different educational priorities or areas of research. For many of these countries, the focus remains on improving basic educational access and addressing fundamental infrastructure needs, which limits the resources available for conducting research on niche topics such as technology integration into education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Bibliometric analysis\u003c/h2\u003e \u003cp\u003eA bibliometric analysis was conducted to quantitatively evaluate the impact and relationships within the body of literature related to civil engineering education and the integration of CAR technologies. Bibliometrics involves applying statistical methods to publication and citation data, offering insights into the structure and development of research fields. This analysis was employed to analyze keyword co-occurrence, average yearly publications, and citation analysis. The analysis began by examining the keyword co-occurrence to identify key themes and trends in the literature (Palshikar, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The aim is to identify these keywords using the co-occurrence analysis feature in VOSviewer\u0026copy; software, retrieving their frequency, degree of centrality, betweenness, and relative importance (van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Where frequency is the count of occurrences, degree of centrality measures the number of links among the keyword, and betweenness centrality reflects how often a keyword serves as a bridge or intermediary on the shortest route between two other keywords, typically calculated across every potential keyword pair in the network (van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, three main keyword clusters were identified, each representing distinct areas of research focus within the integration of CAR technologies in education. The red cluster predominantly focuses on AI, learning systems, and machine learning (ML), reflecting the growing importance of AI-driven technologies in education. Keywords such as convolutional neural networks, classification of information, automation, and decision-making suggest that a significant portion of the research explores how AI and ML systems are applied to enhance educational frameworks, improve decision support, and optimize learning algorithms. This cluster shows a strong connection between learning algorithms and optimization techniques, indicating that much of the research is dedicated to leveraging AI for educational advancements, including personalized and adaptive learning systems. These findings align with the increasing adoption of AI tools in various educational disciplines, particularly in creating intelligent tutoring systems and real-time feedback mechanisms for students.\u003c/p\u003e \u003cp\u003eIn contrast, the green cluster primarily centers around VR, e-learning, and engineering education, highlighting the role of immersive and online learning technologies. Keywords like curricula, teaching, students, and active learning suggest that the research in this cluster is focused on the practical applications of immersive technologies, especially in fields that benefit from hands-on learning, such as civil engineering and architectural design. The cluster reflects efforts to incorporate interactive learning environments, including VR simulations and e-learning platforms, to enhance student engagement and improve educational outcomes. This emphasis on practical training aligns with the field\u0026rsquo;s need for tools that can replicate real-world engineering challenges in a controlled, virtual setting.\u003c/p\u003e \u003cp\u003eLastly, the blue cluster is focused on human studies, with keywords such as controlled study, clinical article, and diagnostic accuracy, indicating that research is more concentrated on empirical studies and human interaction with technology. This cluster, though smaller, highlights the importance of human-centered research in evaluating the impact of immersive technologies on learners, particularly concerning user experience, learning efficiency, and cognitive outcomes.\u003c/p\u003e \u003cp\u003eComparing the three networks provides a clearer picture of how CAR technologies are being integrated across different educational domains. While AI, learning systems, and ML are consistently central across all disciplines, the application of VR and BIM is more specialized within engineering education and civil engineering education. This suggests that while CAR technologies are widely recognized for their educational potential, they are particularly impactful in fields requiring practical simulations and hands-on training, such as engineering and civil engineering. Additionally, civil engineering education stands out for its emphasis on real-world applications, as seen in the co-occurrence of terms like BIM, risk management, and sustainable development. This reflects the field\u0026rsquo;s focus on preparing students for industry challenges through advanced simulation technologies. In contrast, education overall focuses more broadly on technological integration, with AI and learning systems being dominant, reflecting the general shift towards digital learning environments in education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e highlights the temporal distribution of keywords, showcasing how research interests have evolved over time across the three disciples. The color gradient, ranging from blue and green (older research, typically before 2018) to yellow (newer research, after 2021), illustrates the shifting focus in the application of CAR technologies and AI. In Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea (education overall), it is evident that early research (blue tones) focused on topics like education, learning systems, optimization, ML, and algorithms, which were essential to meeting the demands of rapidly expanding digital education environments around 2014\u0026ndash;2018. By contrast, more recent studies (green to yellow tones) increasingly center on AI, deep learning, convolution neural networks, and COVID-19, reflecting a growing interest in integrating AI to enhance personalized learning and decision-making processes.\u003c/p\u003e \u003cp\u003eThe engineering education network (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) reveals a similar temporal pattern, where early research explored education, teaching, and engineering search for hands-on training (blue to green tones, before 2019). However, recent studies, seen in yellow, show a shift toward topics like ML, deep learning, and IoT, which are crucial for applying AI-driven solutions to engineering education. This suggests that while CAR technologies remain important, there is a growing interest in leveraging AI to optimize engineering education through adaptive learning and automated assessment tools. In civil engineering education (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), the color gradient emphasizes a more noticeable shift. While earlier research largely concentrated on decision support systems, personal training, and BIM, more recent studies have shifted towards AI, risk management, ML, and deep learning. This indicates that the application of AI in civil engineering is evolving rapidly, with new research focusing on AI\u0026rsquo;s potential to manage risk and enhance project decision-making processes, as well as optimizing civil engineering education through data-driven simulations.\u003c/p\u003e \u003cp\u003eHowever, the distribution of keywords also reveals underexplored areas across the disciplines. For example, while AI and VR have received significant attention, keywords related to sustainability, risk assessment, and safety management appear infrequently and are less central in the network, especially in civil engineering. This suggests that while specific technological applications have been heavily researched, important aspects like the environmental impact and operational challenges within immersive technology adoption in education are still in need of deeper exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e highlights the frequency of average citations across the three disciplines, using a color gradient that transitions from blue and green for lower citations to yellow for higher citations. In General, the network analysis highlights those keywords such as \u0026ldquo;artificial intelligence\u0026rdquo;, \u0026ldquo;learning systems\u0026rdquo;, and \u0026ldquo;machine learning\u0026rdquo; are among the most highly cited, emphasizing the widespread influence and central role of AI-based approaches in educational research. Additionally, the frequent appearance of terms like \u0026ldquo;virtual reality\u0026rdquo; and \u0026ldquo;e-learning\u0026rdquo; reflects their significant integration into a variety of educational practices, signaling the growing adoption of immersive and digital learning environments. Although AI and ML are consistently relevant across all three disciplines examined, their applications differ, illustrating each discipline\u0026rsquo;s unique focus. In the context of education overall, there is a broader interest in \u0026ldquo;e-learning\u0026rdquo; and \u0026ldquo;virtual reality\u0026rdquo;, which points to current efforts in educational innovation aimed at enhancing student engagement and learning outcomes. By contrast, engineering education places a stronger emphasis on \u0026ldquo;automation\u0026rdquo;, \u0026ldquo;safety engineering\u0026rdquo;, and \u0026ldquo;decision-making\u0026rdquo;, reflecting the field\u0026rsquo;s alignment with cutting-edge technological advancements and the practical demands of the industry. Civil engineering education also leverages AI technologies but with a more specialized and targeted approach. The research in this field prioritizes infrastructure-specific applications, such as \u0026ldquo;BIM\u0026rdquo; and \u0026ldquo;structural health monitoring\u0026rdquo;, highlighting a narrower yet highly practical focus on using AI for infrastructure management. This emphasis suggests that civil engineering research is more concerned with applied technological solutions for real-world infrastructure challenges, reinforcing a practical and industry-aligned perspective.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Update Keywords and Combination\u003c/h2\u003e \u003cp\u003eFollowing the search process described in Section 2.4, the next step involved updating and expanding the keyword combinations based on the preliminary findings. This phase aimed to ensure that the search strategy captured a comprehensive range of relevant literature on civil engineering education and the integration of immersive technologies. The keyword refinement was conducted iteratively, allowing for a thorough revision of terms and concepts based on the evolving scope of the review. The process ensured that critical areas of research were included while irrelevant fields were excluded. The revised Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists 11 distinct keyword combinations designed to enhance search precision, thereby improving both the breadth and depth of the literature retrieval.\u003c/p\u003e \u003cp\u003eThe refinement of these keywords was essential to maintaining the search\u0026rsquo;s alignment with the review\u0026rsquo;s objectives. By focusing on specific combinations, the search process was able to target studies directly related to CAR technologies within civil engineering education. This structured approach reduced the inclusion of extraneous studies while increasing the efficiency of the search process. The goal was not only to capture the most relevant literature but also to eliminate unrelated fields, such as those focusing purely on theoretical aspects or non-engineering education sectors. While numerous other combinations could have been explored, the selection was deliberately limited to those yielding unique and highly relevant outcomes. For example, keyword pairs like \u0026ldquo;mixed reality AND engineering education\u0026rdquo; and \u0026ldquo;artificial environment AND project management\u0026rdquo; were employed to pinpoint specific implementations of these technologies in both academic and professional engineering environments.\u003c/p\u003e \u003cp\u003eThe expansion and refinement of keywords play a critical role in streamlining the overall review process, ensuring that the literature retrieved directly addresses the educational applications of CAR technologies within civil engineering. As CAR technologies continue to transform engineering education, it is essential to capture studies that focus on hands-on training, simulation-based learning, and active learning environments. By integrating targeted exclusions, the search methodology was further optimized to exclude fields that did not contribute to the core research objectives. For instance, studies that solely focused on non-civil engineering disciplines or were unrelated to immersive technology applications were filtered out during this phase. The keywords and combinations established in this phase will form the foundation for a comprehensive literature search, guiding the subsequent phases of in-depth analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKeyword expansion combinations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCivil Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComputerized simulation OR Artificial environment OR Digital twins OR 3D modeling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAND\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCourse* OR curricula OR lab OR Active learning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAND\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProject management OR architecture, engineering, and construction OR \u0026ldquo;Architecture, Engineering, and Construction\u0026rdquo; OR AEC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Include and Exclude Studies\u003c/h2\u003e \u003cp\u003eThe include and exclude studies phase is now specifically focused on research related to civil engineering education, ensuring that only studies relevant to this discipline are selected for the final review. Therefore, the selection process for the studies included in this review was conducted using the PRISMA approach, with specific inclusion and exclusion criteria applied at different stages. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e outlines the process, starting from the initial search and concluding with the final selection of related research. Inclusion criteria 1 involved searching for papers published between 2014 and 2023, limiting the results to articles and conference papers, as determined in Section 2.2. The search terms included keywords related to CAR technologies, as outlined in Section 2.3. This phase yielded an initial set of 1,507 papers. Next, inclusion criteria 2 expanded the keyword search by incorporating new terms discovered in the titles, abstracts, and keywords of the initially retrieved papers, as outlined in Section 2.6. This method, known as pearl growing, added 11 more papers, bringing the total to 1,518 papers. Finally, inclusion criteria 3 involved a snowballing technique, both backward (examining reference lists of related articles) and forward (identifying papers citing the selected articles) (Jalali \u0026amp; Wohlin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This phase added another 26 papers, bringing the total to 1,544 papers.\u003c/p\u003e \u003cp\u003eFollowing the inclusion criteria, duplicate papers were identified and removed (e.g., (Kerdan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Malatji et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Prada et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)), leaving 1,371 unique papers for further evaluation. The remaining papers were subjected to three phases of exclusion criteria based on their relevance, empirical scope, and alignment with the research topic. In exclusion phase 1, papers were excluded based on a review of their title and abstract. A total of 428 papers were excluded for being unrelated to the topic, 128 for being theoretical rather than empirical, 37 for focusing on the wrong population (e.g., non-student or clinical groups), 18 for being in a language other than English, and 4 for being impossible to retrieve. After this phase, 756 papers remained. Exclusion phase 2 involved a detailed screening of the papers to check their relevance to the scope of the review. Here, 248 papers were excluded for not focusing on civil engineering education, and another 120 were excluded for being theoretical. This left 388 papers for the final phase. In exclusion phase 3, a full-text review was conducted. Papers were excluded if they did not involve CAR (14 papers) if they focused on unrelated outcomes like usability or user experience (8 papers), or if they were theoretical (4 papers). Additionally, three papers were excluded for not addressing the correct educational subject. After the final exclusion phase, a total of 359 papers were retained and included in the review for detailed analysis. Of these, 64.38% were journal articles, while the remaining 35.62% consisted of conference papers. This systematic approach ensured that only the most relevant studies, focused on civil engineering education and CAR technologies like VR, AR, and MR, were selected for further examinations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Conduct Review Analysis\u003c/h2\u003e \u003cp\u003eThe review analysis process was structured in a way that allowed for a thorough examination of the final related research. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e outlines the stages of the analysis, starting with the evaluation of each study and the progress through qualitative and quantitative synthesis. The first step involved the evaluation of topics for each selected study, ensuring that the core themes relevant to civil engineering education and the integration of CAR technologies were addressed. This evaluation was conducted in parallel with both qualitative and quantitative data to ensure a comprehensive synthesis of findings. For studies that included qualitative data, themes such as learning outcomes, student engagement, and teaching methods were synthesized to capture the broader educational implications. Similarly, for studies with quantitative data, statistical measures were analyzed to understand trends in technology adoption, student performance, and implementation challenges.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 Synthesize Qualitative Data\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea provides the connections between key topics in civil engineering education. Central to the network are nodes such as \u0026ldquo;civil engineering,\u0026rdquo; \u0026ldquo;education,\u0026rdquo; \u0026ldquo;student,\u0026rdquo; and \u0026ldquo;curriculum,\u0026rdquo; which are strongly linked to \u0026ldquo;teaching,\u0026rdquo; \u0026ldquo;e-learning,\u0026rdquo; and \u0026ldquo;virtual reality.\u0026rdquo; This suggests a significant emphasis on integrating advanced digital tools into the educational processes. The presence of \u0026ldquo;design,\u0026rdquo; \u0026ldquo;construction,\u0026rdquo; and \u0026ldquo;building information modeling (BIM)\u0026rdquo; indicates a focus on practical applications of these tools in more technical aspects of civil engineering. While the network's average publication year visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb) indicates a temporal shift, where earlier studies concentrated on foundational educational themes and have progressively embraced advanced technologies like AI and VR. This transition suggests an ongoing evolution within civil engineering education, adapting to the accelerating pace of technological advancement to better equip students with the necessary tools to address modern engineering challenges. Moreover, the average normalized citations network suggests that recent studies focusing on advanced computational technologies, sustainability, and risk management are gaining traction and impact within academic and professional circles, indicating their growing relevance in shaping future educational and professional practices. The citation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ec) provides insights into the influence and relevance of specific research areas within the academic and professional communities. Keywords like \u0026ldquo;augmented reality\u0026rdquo; (AR), \u0026ldquo;virtual reality\u0026rdquo; (VR), and \u0026ldquo;artificial intelligence\u0026rdquo; are highlighted with lighter hues, particularly yellow, indicating higher citation rates and, therefore, relatively larger academic interest and potentially increasing significance and relevance even in the industry. The projection of these topics within the citation network illustrates their strategic importance in not only advancing educational practices but also in improving professional standards and practices within civil engineering.\u003c/p\u003e \u003cp\u003eThis multi-dimensional analysis suggests a dynamic academic field where traditional educational methods are enhanced by immersive and interactive technologies. These advancements are not only improving educational outcomes but also aligning them closely with industry needs, emphasizing practical, real-world applications. The integration of AI, VR, and BIM within civil engineering education highlights a trend towards more interactive, technologically integrated learning environments that reflect the broader shifts towards digitalization in higher education. This alignment is particularly evident in the emphasis on real-world applications such as BIM and sustainability, which are crucial for preparing students to meet the challenges of modern civil engineering projects. The networks also reveal areas that may require further exploration, such as the environmental impacts of engineering practices and the broader application of safety and risk management in curriculum development, suggesting potential directions for future research and curriculum enhancement.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e summarizes the objectives, outcomes, limitations, and recommendations of the 359 publications that integrate several advanced CAR technologies in civil engineering education. \u003cb\u003e1) Research objectives\u003c/b\u003e: The ten most common objectives identified in the analysis underscore a comprehensive strategy to enhance civil engineering education. Firstly, integrating BIM across engineering curricula is essential, ensuring that students are well-prepared for industry demands by enhancing their skills in design, management, and interdisciplinary collaboration. Secondly, the adoption of VR and AR improves educational experiences by offering immersive environments that allow students to visualize complex structures and simulate real-world scenarios, significantly improving spatial understanding and retention. Promoting interdisciplinary collaboration is another key objective; by merging architectural, civil, and engineering courses, students develop the ability to work effectively on multi-disciplinary projects, which is vital for fostering innovation and teamwork in professional settings. The use of AI and ML in construction-related educational tracks introduces advanced analytical and problem-solving tools to students, making them adept in areas such as structural health monitoring and risk analysis. Sustainability concepts are increasingly incorporated into teaching to help students tackle global environmental challenges and adapt to developing industry standards regarding energy efficiency and green building practices. Moreover, enhancing student engagement through active learning strategies such as flipped classrooms, serious games, and project-based learning not only supports participation but also improves critical thinking and creativity. Developing virtual tools for practical skills training allows students to refine their technical abilities in surveying, design modeling, and construction through VR-based tools and digital simulations. Advanced visualization tools employing 3D modeling and AR-based aids significantly enhance the capability of students to interpret complex data, which is necessary for understanding complex design processes. Expanding project-based learning with technology integration like BIM and the IoT equips students to address real-world problems, enhancing their innovation and practical experience. Lastly, combining physical and virtual learning environments by blending virtual simulations with physical labs and fieldwork ensures a well-rounded educational experience, balancing theoretical knowledge with essential hands-on skills for future engineering professionals.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e2) Outcomes and results\u003c/strong\u003e \u003cp\u003eThe implementation of CAR technological tools and methods in civil engineering education has yielded significant outcomes and results across various domains of student learning and readiness. Enhanced student engagement and motivation have been observed following the integration of AR, VR, and BIM tools. These technologies have significantly increased student interest, participation, and overall satisfaction with learning experiences, indicating a positive shift in educational dynamics. Students have shown improved 3D visualization and spatial understanding, especially in comprehending architectural and civil engineering concepts through immersive learning environments facilitated by AR and VR technologies. This enhancement in spatial awareness is crucial for effective design and construction management. Moreover, project-based learning and interdisciplinary approaches have adopted better real-world readiness and collaboration among students, enhancing teamwork, problem-solving, and communication skills essential for meeting industry demands. Academically, there has been a marked increase in performance and learning outcomes. The use of interactive and formative assessment tools has not only enhanced learning efficiency but also strengthened critical thinking and practical application of knowledge in design and engineering tasks. In the field of sustainability, the use of AR, VR, and AI tools has enabled students to understand better energy-efficient designs, sustainable practices, and environmental impacts, thus strengthening a generation of engineers capable of developing innovative and sustainable solutions. The effective integration of BIM into curricula has provided students with early exposure to complex design tools, improving interdisciplinary collaboration and aligning academic training more closely with industry requirements. Additionally, the application of AI and ML has improved understanding and operational capabilities in SHM, providing students with skills in accurate damage detection and predictive maintenance. Students have also enhanced their technical and practical skills, gaining hands-on experience with advanced technologies such as Geographic Information Systems (GIS), the IoT, and ML, which are key in increasing employment skills in the engineering fields. There has been an increased awareness of architectural heritage and cultural conservation, with tools like Unmanned Aerial Vehicles (UAVs) and photogrammetry allowing students to engage actively with preservation projects, effectively blending technology with historical studies. Finally, there has been a positive shift toward active and experiential learning methodologies. Techniques such as flipped classrooms, serious games, and virtual field trips have not only encouraged deeper learning but have also promoted more flexible, personalized educational experiences.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e3) Research limitations\u003c/strong\u003e \u003cp\u003eThe key recommendations for future research and development in civil engineering education, as gathered from the reviewed papers, emphasize several areas to enhance learning and industry readiness. These recommendations include expanding the integration of emerging CAR technologies across more courses to adopt enhanced interdisciplinary learning and practical applications. Interdisciplinary collaboration can be further promoted by developing joint courses that integrate BIM, sustainability, and engineering principles, enabling teamwork for engineering students to better meet industry demands. Moreover, efforts should also focus on enhancing accessibility and affordability by developing cost-effective solutions, reducing hardware dependency, and providing educator training to facilitate adoption in resource-constrained institutions. Advancing data integration is critical, particularly through improving the interoperability of BIM, GIS, and IoT systems to manage complex projects, promote real-time collaboration, and ensure sustainable infrastructure management. In addition, expanding hands-on and hybrid learning approaches by blending physical fieldwork and labs with digital simulations will ensure students gain both real-world and advanced virtual experiences. Structured BIM education should be implemented earlier in curricula, with expanded applications across disciplines and continuous updates to align with evolving industry standards. The scope of AR and VR applications should be broadened to include diverse engineering fields, emphasizing interactive design reviews, construction safety, and sustainability education. Larger and longitudinal studies are necessary, involving more participants and institutions to assess the long-term impacts of digital tools like AR, VR, and AI on student learning and career preparedness. Strengthening collaborations with industry stakeholders is essential to refine educational content, enhance faculty training, and integrate real-world projects into academic programs for skill development. Finally, inclusive and scalable learning models must be developed, featuring personalized, scalable digital platforms that address diverse student needs and integrate lifelong learning frameworks to support continuous professional growth.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e4) Future recommendations\u003c/strong\u003e \u003cp\u003eDespite the numerous advantages offered by CAR technological tools in civil engineering education, their adoption and integration are not without considerable limitations. One of the primary limitations is the high costs associated with acquiring, maintaining, and updating advanced setups like AR, VR, and BIM, which can limit accessibility for many educational institutions. Furthermore, there is often a lack of faculty expertise and training in these advanced tools, which makes it harder for them to be widely adopted and less effective in the educational environment. The complexity of integrating these interdisciplinary technologies into existing curricula presents another substantial challenge. For example, combining BIM with AR and GIS requires extensive resources and coordination, making it a resource-intensive effort. Additionally, many institutions struggle with limited access to necessary resources such as equipment, high-quality data, solid infrastructure, and up-to-date software, further complicating the implementation of advanced educational methods. Studies involving these technologies often suffer from small sample sizes and limited scope, typically restricted to specific universities, courses, or scenarios, which reduces the generalizability of the findings. Technical challenges and usability issues also pose significant barriers, as students and educators may struggle with complex interfaces and high computational demands, particularly when using VR and AR systems. Resistance to change is another notable limitation, with both faculty and students sometimes showing reluctance to adopt new technologies or methods due to steep learning curves or a preference for traditional educational approaches. Moreover, while various tools and methods are tested within theoretical or controlled environments, they often lack sufficient validation in practical, real-world settings, which questions their applicability and effectiveness outside the classroom. The high rational load associated with learning advanced tools like BIM, alongside core engineering concepts, can overwhelm students, particularly those who are beginners, potentially delaying their overall learning experience. Lastly, health and accessibility concerns, such as VR-related motion sickness, limited access to necessary hardware, and prevalent digital divides, further affect the scalability and inclusivity of implementing these technologies in civil engineering education.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2 Synthesize Quantitative Data\u003c/h2\u003e \u003cp\u003eThe global distribution of publications after filtering civil engineering education highlights significant disparities in research contributions across regions. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and unlike in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the US leads the global landscape with 86 publications, suggesting a significant investment in civil engineering research and education. China follows with 56, driven by strategic modernization of education and engineering programs. European countries also contribute notably, with countries such as Spain, Germany, and Turkey playing pivotal roles. Spain, with 21 publications, demonstrates a strong emphasis on integrating digital tools and BIM into civil engineering education. Germany and Turkey, with 9 and 8 publications, respectively, emphasize practical applications of immersive technologies, particularly in construction and infrastructure-focused education. Regions such as South Asia (e.g., India, with seven publications) show growing engagement, supported by initiatives like India\u0026rsquo;s National Education Policy 2020 (Mahajan, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, contributions from developing regions, including Africa and South America, remain limited, potentially due to challenges such as inadequate funding and infrastructure. This distribution underscores the need for global collaboration to bridge gaps and ensure wider adoption of innovative technologies in civil engineering education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e illustrates the distribution of various technologies within civil engineering education. The largest segment is BIM, which accounts for 23.53%, which not only reflects its foundational role in modern civil engineering education but also underscores its potential for facilitating collaborative projects and sustainable design practices. VR follows closely, representing 21.76%, indicating its importance in immersive learning environments. AR, at 13.82%, extends this further by blending digital elements into the real world, enhancing students' ability to visualize and manipulate engineering concepts on-site. AI and the IoT, representing 6.76% and 2.65%, respectively, suggest promising areas ripe for growth. AI could revolutionize how data is utilized in civil engineering education, offering predictive analytics in areas such as urban planning and infrastructure management, while IoT could connect various sensors and devices on construction sites, providing real-time data to enhance decision-making processes. Lesser, but still notable, percentages are held by AI at 6.76% and the IoT at 2.65%. MR and XR are relatively minor, constituting only 0.59% and 1.18% respectively. Surprisingly, the Metaverse also appears, though it accounts for a minimal 0.59% of the technologies used. The remaining 29.12% of the chart is labeled as 'Other', suggesting a diverse range of additional technologies not specified within the main categories. This distribution underscores the varied and technologically advanced approaches being integrated into civil engineering education to enhance learning and practical application.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e provides a detailed insight into the prioritization and application of immersive technologies within civil engineering education. Figure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e highlights general civil engineering as the most extensively covered area, with 97 studies dedicated to this field. This is followed by construction engineering and management with 79 studies, reflecting the industry\u0026rsquo;s evolution towards complex management needs that benefit from advanced technological interventions. Structural engineering also receives considerable attention, with 44 studies highlighting the critical need for innovative solutions in infrastructure safety and efficiency. However, limited studies focus on specialized fields such as geotechnical, environmental, and water resources engineering, indicating potential gaps in the current integration of immersive technologies, which could be crucial in these areas given their importance in the broader scope of civil engineering. Meanwhile, Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e shifts the focus towards the target groups benefiting from these educational advancements, with students being the primary group involved in 238 studies. This underlines a strategic emphasis on improving student learning and engagement through interactive and immersive environments that bridge theoretical knowledge with practical application. The considerable number of studies focusing on professionals and engineers, totaling 89, suggests that immersive technologies are also being extensively used for professional development, aligning with the needs highlighted in construction and structural engineering. This targeted application supports ongoing professional training and skill enhancement in a risk-free virtual setting. Researchers and teachers, each involved in 24 studies, highlight a more focused but critical engagement with immersive technologies to explore educational efficiencies and academic advancements in civil engineering education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe integration of CAR technologies into civil engineering education has been facilitated by various software and platforms. Factors including educational objectives, user expertise, and the specific requirements of the curriculum influence the selection of these tools. Figure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e illustrates various types of software utilized in research studies focused on civil engineering and related fields, highlighting their frequency of use across studies. According to the figure, BIM software ranks as the most utilized tool, with over 32 references, reflecting the growing importance of BIM in visualizing and managing construction projects digitally. Autodesk Revit\u0026copy; and VR software also feature prominently, highlighting their roles in creating immersive, interactive environments that facilitate virtual construction simulations and design explorations. Autodesk AutoCAD\u0026copy; and AR software are also widely used, particularly for tasks that involve 3D modeling and overlaying virtual information onto physical environments. Other notable software include SketchUp\u0026copy; and 3D Max\u0026reg;, both of which are essential for architectural modeling and rendering, providing detailed visualizations of structures and spatial configurations. Software like Navisworks\u0026copy;, Unity\u0026copy;, and Quest3D\u0026copy; are popular for more advanced simulations and interactive experiences, often enabling collaborative or multi-user scenarios within educational environments. On the lower end, software like SPSS\u0026reg; and Google-based software are included, likely for data analysis and supporting various educational tools rather than for immersive simulations. ML-based software, Civil 3D\u0026copy;, and GIS software appear less frequently, suggesting niche applications in specific areas such as geographic analysis, specialized structural simulations, and data-driven insights. The \u0026ldquo;Other\u0026rdquo; category shows a significant presence, indicating a variety of additional software tools in use, possibly including custom applications and less common platforms tailored to specific research or educational needs. This distribution highlights the diversity of software used in civil engineering education, encompassing a range of immersive, analytical, and visualization tools that enhance both theoretical learning and practical training. It is worth noting that out of the total studies analyzed (359), 228 explicitly mentioned the use of software tools, while the remaining 131 studies did not specify or utilize any software, potentially due to differences in research focus, methodology, or technological requirements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.8.3 Results and Discussion\u003c/h2\u003e \u003cp\u003eThe integration of CAR technologies, including VR, AR, and MR, has emerged as a transformative approach in education. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides a comprehensive comparison of CAR technologies, which highlights their unique features, applications, and contributions to educational and professional domains. These technologies collectively represent transformative tools that reshape how knowledge is delivered, and skills are developed, particularly in disciplines like civil engineering. Each technology offers unique benefits and challenges, shaping their application and effectiveness in learning environments. \u003cb\u003e1) Interaction with the real world\u003c/b\u003e: VR immerses users completely in a simulated environment, isolating them from the physical world and making it suitable for tasks requiring undivided attention. AR enhances physical surroundings by overlaying digital information, allowing users to interact with their environment while benefiting from virtual enhancements. MR blends the real and digital worlds, enabling users to manipulate both simultaneously and offering a seamless hybrid experience. XR encompasses the entire spectrum of interaction, offering varying degrees of immersion from fully virtual to augmented environments tailored to specific use cases. \u003cb\u003e2) Applications\u003c/b\u003e: Each CAR technology caters to different educational and professional needs. VR is widely used for immersive training simulations, virtual tours, and healthcare applications. AR finds its strength in real-time instruction, navigation, and maintenance tasks, making it practical for hands-on learning. MR extends these applications by enabling real-time collaboration in design, healthcare, and education. XR provides a comprehensive framework by combining AR, VR, and MR for diverse environments like industry-specific training, entertainment, and education. \u003cb\u003e3) Level of immersion\u003c/b\u003e: The level of immersion varies significantly across these technologies. VR offers the highest level of immersion, fully disconnecting users from reality to engage with virtual scenarios. AR provides low to moderate immersion by layering virtual elements onto the physical world. MR balances high immersion by integrating real-world and digital interactions seamlessly. XR spans the entire range, offering adaptive immersion levels depending on the context, from minimal augmentations to fully immersive virtual settings. \u003cb\u003e4) Hardware requirements\u003c/b\u003e: The hardware demands of these technologies reflect their complexity and immersion levels. VR requires specialized headsets, motion-tracking devices, and high-performance computers to create a fully virtual environment. AR is more accessible, relying on smartphones, tablets, or AR glasses. MR necessitates advanced hybrid devices capable of combining AR and VR functionalities, while XR integrates a mix of technologies like HoloLens and Oculus Quest, depending on the desired experience. \u003cb\u003e5) Advantages\u003c/b\u003e: VR provides an immersive learning experience ideal for simulating dangerous or complex tasks in a controlled environment, making it cost-effective for training. AR excels in enhancing real-world interaction, offering on-the-job learning and instant feedback. MR combines these strengths, supporting collaborative and interactive applications. XR stands out for its versatility, leveraging the benefits of VR, AR, and MR across multiple environments and disciplines. \u003cb\u003e6) Limitations\u003c/b\u003e: Each technology comes with its limitations. VR's need for complete user isolation and expensive hardware may deter widespread adoption and raise issues like motion sickness. AR is constrained by a limited field of view, dependency on external lighting, and hardware capabilities. MR faces high computational requirements and cost challenges, while XR's implementation complexity and scalability present barriers to adoption in broader contexts. \u003cb\u003e7) Industry use cases\u003c/b\u003e: The industry applications of CAR technologies highlight their versatility. VR is frequently used in architecture, medicine, entertainment, and education for simulations and training. AR thrives in retail, navigation, and maintenance scenarios where real-time visualization is key. MR is applied in engineering, healthcare, and industrial training, facilitating collaboration and interaction. XR provides cross-disciplinary solutions in fields like automotive, construction, and aerospace, combining the strengths of AR, VR, and MR. \u003cb\u003e8) Cognitive benefits\u003c/b\u003e: Each CAR technology supports cognitive development in distinct ways. VR enhances spatial awareness and problem-solving through immersive simulations. AR improves memory retention by providing contextual, hands-on learning experiences. MR combines these cognitive advantages, enabling multi-sensory engagement and real-time adaptability. XR caters to diverse cognitive styles by integrating AR, VR, and MR for personalized and scalable learning pathways. \u003cb\u003e9) Social interaction\u003c/b\u003e: Social interaction capabilities vary across technologies. VR restricts interaction to virtual avatars, often limiting physical-world engagement. AR enables collaborative work in physical spaces with augmented digital overlays. MR supports seamless interaction between real-world collaborators and virtual avatars. XR offers the flexibility to adapt social interactions across fully virtual, partially augmented, or hybrid settings, catering to diverse collaboration needs. \u003cb\u003e10) Cost considerations\u003c/b\u003e: Cost is a significant factor influencing the adoption of CAR technologies. VR and MR are expensive due to their high-end hardware requirements, while AR is more affordable, utilizing existing devices like smartphones and tablets. XR's costs vary based on the complexity of the application, often combining the expenses of AR, VR, and MR technologies, making it the most variable option. \u003cb\u003e11) Ethical concerns\u003c/b\u003e: Ethical challenges differ across these technologies. VR raises concerns about overuse, addiction, and reduced physical interaction. AR faces privacy issues, particularly in data collection within real-world environments. MR's integration of physical and digital interactions introduces potential ethical dilemmas in managing personal data. XR must address concerns related to user privacy, equitable access, and the secure integration of AR, VR, and MR applications. \u003cb\u003e12) Future directions\u003c/b\u003e: The future of CAR technologies points towards greater integration and innovation. VR is expected to become more realistic and affordable with advancements in AI. AR is anticipated to achieve enhanced precision through AI and IoT integration. MR will likely see the development of lightweight, adaptable devices for seamless interaction. XR aims to unify these advancements, leveraging AI for adaptive, scalable solutions across educational and professional domains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of features and aspects of CAR technologies in civil engineering education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature/Aspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction with Real World\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFully replaces the real world with a simulated one.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdds virtual elements to enhance the real world.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombines real and virtual elements for interaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOffers varying levels of immersion, from augmented to fully virtual.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining, gaming, virtual tours, education, healthcare.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigation, real-time instructions, design, interactive manuals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCollaborative design, training, surgeries, and industrial tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCombines AR/VR/MR for training, entertainment, and industry solutions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Immersion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFully immersive.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow to moderate immersion with real-world integration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh immersion balancing real and virtual interactions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjustable immersion levels, from partial to full virtual.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHardware Requirements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeadsets, motion trackers, high-performance computers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmartphones, tablets, AR glasses.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvanced AR/VR headsets high-performance systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA mix of AR, VR, and MR devices like HoloLens Oculus.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdvantages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmersive learning and safe simulations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhances real-world learning with immediate feedback.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombines AR and VR for collaborative, practical applications.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVersatile and integrates multiple CAR technologies effectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsolating is expensive and may cause motion sickness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited by lighting, hardware, and field of view.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpensive and computationally demanding.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComplex to implement; scalability challenges.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearning Potential\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeep engagement in simulated environments.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal-time enhancements improve retention and skills.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombines simulations with real-world interaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScalable systems combine immersive and real-world contexts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndustry Use Cases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchitecture, surgery simulation, gaming, safety training.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetail, navigation, maintenance, anatomy visualization.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEngineering, surgeries, industrial training.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-industry solutions in healthcare, construction, and aerospace.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive Benefits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoosts spatial awareness and problem-solving.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhances memory and hands-on learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombines VR and AR for multi-sensory engagement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePersonalized and adaptive learning paths.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited to virtual avatars and interactions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncourages collaboration in physical spaces.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMerges virtual and real-world collaboration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOffers hybrid and flexible social experiences.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCost Considerations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpensive hardware and computing needs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAffordable, works with existing devices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher costs for advanced hybrid systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCosts vary depending on the technology mix and application.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthical Concerns\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisks of overuse and isolation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivacy concerns real-world data collection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData management challenges between real and virtual.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBalances privacy, security, and equitable access.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFuture Directions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI integration for realistic simulations and cost reduction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhanced precision with AI and IoT.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLightweight, adaptable devices for seamless interaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnified systems combining AR, VR, and MR with AI-driven scalability.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Opportunities, Trends, Challenges, and Future Research Directions\u003c/h2\u003e \u003cp\u003eThe synthesis of the entire review, particularly in addressing the identified research characteristics, establishes a clear progression from opportunities to future research directions. Specifically, the existing research characteristics underscore the untapped potential and emphasize the opportunities inherent in this research area. Emerging trends provide evidence of progress and instill optimism in realizing these opportunities. However, challenges temper this optimism by highlighting the realistic barriers that must be addressed. Together, these insights create a roadmap that aligns opportunities and trends with actionable future research directions, ensuring a balanced and forward-looking perspective. This approach not only enhances the credibility of the review but also establishes a clear pathway for formulating actionable future research directions.\u003c/p\u003e \u003cp\u003eAfter a comprehensive analysis of the 359 studies, ten main characteristics were identified in CAR technologies in civil engineering education. Figure\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e19\u003c/span\u003e illustrates the percentage distribution of key methodological characteristics identified in the reviewed research articles on CAR technologies. The insights reveal critical deficiencies across multiple domains: \u003cb\u003e1) Ethical and psychological concerns\u003c/b\u003e: Representing the highest percentage (95.31%), this category highlights the overwhelming lack of focus on ethical and psychological considerations. For instance, while issues like privacy, data security, and prolonged VR-induced discomfort are crucial, very few studies address them comprehensively. Moreover, there is a notable deficiency in the investigation of long-term psychological impacts, presenting a significant opportunity for future research in understanding and mitigating these effects. \u003cb\u003e2) Sample size and representativeness\u003c/b\u003e: Approximately 64.82% of studies were either vague about their sample size or relied on small participant groups, often fewer than 10. For example, one study included only eight participants, limiting the generalizability of its findings. This significant gap underscores the need for larger, more representative test groups to enhance the reliability and applicability of future research. \u003cb\u003e3) Lack of real-world implementation\u003c/b\u003e: Around 56% of studies remained confined to theoretical or controlled environments, lacking practical validation in real-world scenarios. While tools and methods have been tested in pilot phases, they often fail to transition to broader educational or professional applications. Collaborations with industries and institutions could help bridge this gap and enable real-world deployment and evaluation. \u003cb\u003e4) Neglect of instructor perspectives\u003c/b\u003e: Nearly 46% of studies overlooked the perspectives of educators, focusing primarily on student outcomes. This lack of teacher feedback limits the understanding of the feasibility and practicality of implementing immersive technologies in classrooms or training environments. \u003cb\u003e5) Short-term assessment\u003c/b\u003e: About 44.23% of the research focused on short-term evaluations, with little attention to longitudinal studies. The absence of evidence for long-term effects restricts the understanding of sustained impacts on learning outcomes or user experiences. \u003cb\u003e6) Cost considerations\u003c/b\u003e: Although the high cost of immersive tools is recognized as a limitation, only 33.36% of studies discussed how financial constraints influenced their research processes or outcomes. This represents a gap in understanding how economic barriers can affect technological adoption and scalability. \u003cb\u003e7) Infrastructure and institutional support\u003c/b\u003e: The lack of institutional support, such as insufficient infrastructure or policies, also poses a significant barrier. Without proper frameworks, scaling immersive technologies for widespread use remains a challenge, though this was less frequently discussed compared to other characteristics. \u003cb\u003e8) Software and tool limitations\u003c/b\u003e: Around 21.17% of studies failed to explore how the limitations of tools like Unity or Oculus Rift affected participant engagement or outcomes. Challenges like steep learning curves and accessibility issues remain underexplored, impacting replicability and accessibility. \u003cb\u003e9) Complexity and scalability issues\u003c/b\u003e: Complexity in system integration and a lack of scalability solutions were noted by 14.01% of studies. Few researchers provided detailed accounts of how technical challenges were resolved, hindering replication and scalability. \u003cb\u003e10) Measurement tools\u003c/b\u003e: Only 6.62% of studies specified standardized methods for assessing outcomes such as student engagement or understanding. The absence of clear metrics reduces the reliability of findings and limits the ability to compare results across studies. The findings emphasize a need for comprehensive strategies to address these characteristics, including ethical guidelines, larger sample sizes, real-world implementations, and standardized assessment frameworks. These improvements would enhance the credibility and applicability of immersive technology research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e summarizes the opportunities, trends, challenges, and future research directions of CAR technologies in civil engineering education. CAR technologies offer numerous opportunities to enhance civil engineering education. Simplified integration models can streamline the adoption of immersive tools, making them more scalable and user-friendly. Specifically, the integration of AI technologies, such as ML and neural networks, with BIM facilitates predictive analysis, resource optimization, and increased design efficiency. This convergence allows for advanced simulations and real-time feedback, which enhance both the learning experience and educational outcomes. Skill development is another promising area, as immersive technologies provide students with hands-on experience and a deeper understanding of complex engineering concepts. Examples include VR simulations used in civil engineering to visualize building stress points and AR applications to design or analyze construction projects virtually. Cost optimization strategies, such as leveraging open-source software and hardware modularity, can significantly enhance accessibility, while public-private partnerships and financial incentives can accelerate adoption. The advancement of supporting infrastructure, such as more affordable VR headsets and accessible cloud-based platforms, plays a crucial role in the widespread deployment of immersive educational tools. Inclusive design principles, incorporating features like multilingual support and simplified user interfaces, ensure that these technologies are accessible to a broader audience, including individuals with disabilities or limited technical expertise.\u003c/p\u003e \u003cp\u003eThe integration of CAR technologies into civil engineering education has seen several emerging trends that shape the future of learning and teaching in the field. One of the most significant trends is the widespread adoption of BIM, which enhances collaboration and streamlines decision-making processes in projects. BIM's real-time 3D modeling capabilities allow students and professionals to visualize projects in unprecedented detail, facilitating better communication and efficiency. Another critical development is the implementation of strong cybersecurity measures designed to protect sensitive data within immersive platforms. As these technologies increasingly handle large volumes of confidential project information, ensuring data integrity and security has become crucial. The emergence of metaverse environments represents a leap towards more dynamic and interactive educational experiences. These virtual spaces simulate real-world construction sites and engineering challenges, offering students the opportunity to engage with complex scenarios in a controlled, risk-free setting. Additionally, the integration of IoT within civil engineering curricula enables real-time data sharing and monitoring. This connectivity not only enhances the educational tools available but also mirrors the shift towards smart technologies in the professional sphere. Finally, digital learning platforms powered by these technologies have gained significant traction, making engineering education more accessible and interactive. Platforms such as online simulations and virtual labs allow students from diverse geographical locations to participate in hands-on learning without the need for physical presence in a traditional classroom.\u003c/p\u003e \u003cp\u003eWhile the integration of CAR technologies in civil engineering education offers numerous benefits, it also introduces several challenges that need careful management. One of the primary challenges is the complexity of integrating interdisciplinary tools such as VR systems and integrated project delivery (IPD) models. These require seamless connectivity and compatibility across different platforms, which can be technically demanding and resource-intensive. The high costs associated with cutting-edge immersive technologies like AR and metaverse platforms pose another significant barrier, especially for resource-constrained institutions. These costs often extend beyond just purchasing the technology to include maintenance and updates, making them less accessible. Infrastructure requirements further complicate the adoption of these technologies. High-speed internet connections, advanced visualization labs, and other technical facilities are essential but can entail substantial logistical and financial investment. Usability also presents a challenge, particularly in terms of accessibility and the user experience for specific groups, such as those with disabilities or limited tech proficiency. Ensuring that these technologies are inclusive and user-friendly is crucial but often overlooked in development phases. Security is another critical concern, with the need to protect sensitive data and prevent unauthorized access in immersive platforms. Implementing robust cybersecurity measures is essential but can be complex and expensive. Ethical issues, including equitable access and potential biases in AI-driven systems, add another layer of complexity. Ensuring that these technologies are fair and do not perpetuate existing disparities is vital. Lastly, the current limitations in providing tactile feedback and fully immersive experiences reveal gaps that existing technologies have not yet bridged. This highlights the need for continued innovation and development in the field.\u003c/p\u003e \u003cp\u003eFuture research in CAR technologies must address critical areas to maximize their potential in civil engineering education. Developing advanced visualization tools that enable real-time feedback, and more immersive interaction is essential for enhancing user experience and educational outcomes. Such tools should not only replicate real-world environments but also allow for the manipulation and testing of variables in ways that traditional methods cannot. To bridge the digital divide, research must also focus on enhancing global accessibility. This includes creating scalable solutions that can be adapted for use in diverse geographic, economic, and educational contexts, thereby making these advanced technologies accessible to a broader range of institutions. Real-world implementation and validation through field trials in educational, industrial, and healthcare settings will provide insights into the efficacy and scalability of these technologies. Collaboration with a broad range of stakeholders is vital for ensuring that the development of these technologies aligns with real-world demands. This involves partnerships with policymakers, industry experts, educators, and technology developers to create context-specific solutions that address the unique challenges and opportunities in each domain. Lastly, research should explore economic impacts and develop cost-effective design approaches. Studies on the economic implications of adopting these technologies will guide investment decisions. Additionally, promoting open-source platforms and modular designs can significantly reduce costs, making these advanced tools more accessible to educational institutions worldwide.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Summary and Conclusions","content":"\u003cp\u003eThe integration of technology in education, particularly through the adoption of CAR technologies, represents a transformative shift that addresses the increasing complexity of civil engineering education. These technologies, encompassing VR, AR, MR, and other technologies, are not mere supplements but pivotal tools in bridging the gap between theoretical frameworks and practical applications. Existing studies and reviews emphasize the importance of CAR in other engineering disciplines; however, civil engineering education has not been comprehensively analyzed for its specific applications of CAR. Therefore, this research systematically analyzed the different CAR technologies utilized in education (overall), engineering education (in general), and, finally, civil engineering education (specifically).\u003c/p\u003e \u003cp\u003eThe review process developed to explore these technologies follows a nine-step methodology. The first step involves a detailed analysis of the literature to identify various themes through categorization, taxonomy, and clustering. This thematic identification serves as the foundation for the review structure, maintaining focus on the pivotal issues within the field of civil engineering education and CAR technologies. After identifying key themes, a review strategy was formulated, detailing methodologies, tools, databases, and a timeline to ensure a comprehensive and systematic literature search. In this review, the EBSCO database was selected, with searches limited to English-language publications from 2014 to 2023, a period marking significant advancements in immersive technologies like VR, AR, and MR. The next step involves defining specific keywords and keyword combinations based on the identified themes to ensure that the literature search remains targeted. A comprehensive search across selected databases then gathers a focused collection of literature pertinent to the review\u0026rsquo;s objectives. Preliminary results from this search were compiled and summarized to highlight key themes and trends, providing a basis for further refinement. Then, statistical analysis was employed to provide quantitative insights into the study's impact and emerging trends. The analysis was conducted in four parts. First, the number of articles retrieved from the literature search was recorded: 262,898 studies for education overall, 25,423 for engineering education, and 1,647 for civil engineering education. Second, the distribution between journals and conferences was analyzed. In education overall, 52.96% were journal articles, and 47.04% were conference papers. For engineering education, the split leaned heavily towards conference papers (66.50%) compared to journal articles (33.50%), and civil engineering education showed a similar trend with 57.07% conference papers and 42.93% journal articles. Third, the distribution across academic disciplines was assessed, focusing on natural sciences, life sciences and health, engineering and technology, social sciences and humanities, and other studies. Lastly, global distribution revealed that about 30% of studies came from the US and China, with 59,441 from the US and 49,596 from China, underscoring their dominance in educational research. However, in engineering education, Spain led with 11.36%, followed by Germany at 10.13%, while in civil engineering education, Italy ranked first with 0.90%, followed by Spain at 0.81%. Further, a bibliometric analysis was conducted to quantitatively assess the impact and relationships within the literature in different academic disciplines and CAR technologies. This involved analyzing keyword co-occurrence, average yearly publications, and citation data to gain deeper insights into the research field's structure and development. Finally, the studies were meticulously screened using established inclusion and exclusion criteria to ensure that only relevant, high-quality studies were retained. The selection of studies was periodically revised based on insights from the statistical analysis and initial literature findings. After a final exclusion phase, 359 papers were retained for detailed analysis, with the majority being journal articles. This rigorous process ensured that the review captured a comprehensive and relevant body of literature, thereby significantly contributing to the field of civil engineering education and the integration of CAR technologies.\u003c/p\u003e \u003cp\u003eOnce a final selection of studies has been made, the next step is to perform a detailed review analysis. This involves the evaluation of each study and progress through qualitative and quantitative syntheses. The qualitative data synthesis starts with a keyword co-occurrence network within civil engineering education. Central nodes such as \u0026ldquo;civil engineering,\u0026rdquo; \u0026ldquo;education,\u0026rdquo; \u0026ldquo;student,\u0026rdquo; and \u0026ldquo;curriculum\u0026rdquo; are prominently linked to \u0026ldquo;teaching,\u0026rdquo; \u0026ldquo;e-learning,\u0026rdquo; and \u0026ldquo;virtual reality.\u0026rdquo; The network's visualization over time indicates a shift from initial studies focused on foundational educational themes to more recent studies embracing advanced technologies such as AI and VR. The analysis of the 359 publications further explores various commonalities among them, including the objectives, outcomes, limitations, and recommendations that often recur. Objectives are centered around enhancing civil engineering education by integrating BIM across curricula, adopting immersive technologies like VR and AR for better educational experiences, and fostering interdisciplinary collaboration. The outcomes from the implementation of these CAR technologies were notably positive, with enhanced student engagement and motivation, improved 3D visualization and spatial understanding, and better readiness for real-world challenges. Additionally, the recommendations underscore the need for future research to focus on expanding the integration of CAR technologies, improving accessibility and affordability, and ensuring effective integration of BIM, GIS, and IoT systems.\u003c/p\u003e \u003cp\u003eThe quantitative analysis provides insights into the global distribution of the studies and the specific technologies utilized. The US leads with 86 publications, followed by China with 56, highlighting significant investments in civil engineering education. European contributions are also notable, with Spain (21 publications), Germany (9), and Turkey (8) emphasizing the integration of digital tools and practical applications of immersive technologies. In terms of technology distribution, BIM is the most prominent, accounting for 23.53% of the studies, followed by VR and AR, which represent 21.76% and 13.82%, respectively. These statistics highlight the foundational role of BIM in modern civil engineering education and the growing importance of immersive learning environments. The primary focus on students, who are involved in 238 studies, demonstrates the significant engagement of these technologies in educational contexts, with professionals and engineers also notably involved in many studies.\u003c/p\u003e \u003cp\u003eEach technology offers unique benefits and challenges, shaping its application and effectiveness in learning environments, which include interaction with the real world, applications, level of immersion, hardware requirements, advantages, limitations, industry use cases, cognitive benefits, social interaction, cost considerations, and ethical concerns.\u003c/p\u003e \u003cp\u003eAs a final step, the review integrated identified trends, challenges, and opportunities into a broader discussion, synthesizing key findings into actionable insights and exploring future research directions. This stage leverages the review's comprehensive nature to provide a forward-looking perspective that is valuable both academically and practically. By identifying opportunities for innovation, recognizing field-specific challenges, and analyzing emerging trends, the review offers a roadmap aligning these elements with actionable future research directions. This not only enhances the credibility of the review but also establishes a clear pathway for developing actionable insights in civil engineering education.\u003c/p\u003e \u003cp\u003eAfter a detailed analysis of 359 studies, ten primary characteristics affecting CAR technologies in civil engineering education were identified. These insights reveal critical deficiencies across multiple domains:\u003c/p\u003e \u003cp\u003e1. \u003cb\u003eEthical and Psychological Concerns\u003c/b\u003e: With the highest prevalence at 95.31%, this category underscores a significant lack of focus on ethical considerations and psychological impacts, such as privacy, data security, and VR-induced discomfort, highlighting a major area for future research.\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eSample Size and Representativeness\u003c/b\u003e: Approximately 64.82% of studies suffered from vague or small participant groups, limiting the generalizability of findings and underscoring the need for larger, more representative samples.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003eLack of Real-World Implementation\u003c/b\u003e: About 56% of the studies did not advance beyond theoretical or controlled environments, pointing to a gap in real-world application and validation.\u003c/p\u003e \u003cp\u003e4. \u003cb\u003eNeglect of Instructor Perspectives\u003c/b\u003e: Nearly 46% of studies overlooked educator feedback, limiting insights into the practicality of immersive technologies in educational settings.\u003c/p\u003e \u003cp\u003e5. \u003cb\u003eShort-term Assessment\u003c/b\u003e: Focusing on immediate outcomes, 44.23% of studies lacked long-term evaluations, which are crucial for understanding sustained impacts.\u003c/p\u003e \u003cp\u003e6. \u003cb\u003eCost Considerations\u003c/b\u003e: Only 33.36% of studies addressed how financial constraints affect research and outcomes, highlighting a need for cost-effective technology solutions.\u003c/p\u003e \u003cp\u003e7. \u003cb\u003eInfrastructure and Institutional Support\u003c/b\u003e: A notable barrier, with insufficient support limiting the scalability of immersive technologies.\u003c/p\u003e \u003cp\u003e8. \u003cb\u003eSoftware and Tool Limitations\u003c/b\u003e: About 21.17% of studies noted the limitations of tools like Unity or Oculus Rift, impacting participant engagement and study outcomes.\u003c/p\u003e \u003cp\u003e9. \u003cb\u003eComplexity and Scalability Issues\u003c/b\u003e: Only 14.01% of studies detailed how they addressed technical challenges, which is critical for replication and scalability.\u003c/p\u003e \u003cp\u003e10. \u003cb\u003eMeasurement Tools\u003c/b\u003e: Just 6.62% of studies used standardized methods for outcome assessment, underscoring a need for reliable metrics.\u003c/p\u003e \u003cp\u003eThe integration of CAR technologies offers numerous opportunities to enhance civil engineering education, from simplifying the adoption of immersive tools to enhancing learning through advanced simulations and real-time feedback. However, several challenges, such as high costs, technical requirements, and integration complexities, must be carefully managed. To address these issues, future research should focus on developing advanced visualization tools that provide immersive experiences and real-time feedback, enhancing global accessibility, validating real-world applications, and fostering collaborations that align technological development with real-world needs. Additionally, economic studies could guide cost-effective solutions, promoting wider adoption of these transformative educational tools. This research, therefore, highlights the transformative potential of CAR technologies in civil engineering education, emphasizing their ability to foster adaptive, scalable, and accessible learning environments. The findings will not only inform policy and curriculum development but also equip future engineers to meet the evolving challenges of the profession.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eArtificial Intelligence\u003c/p\u003e\n\u003cp\u003eAR\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eAugmented Reality\u003c/p\u003e\n\u003cp\u003eBIM\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eBuilding Information Modelling\u003c/p\u003e\n\u003cp\u003eCAR\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eComputer-altered Reality\u003c/p\u003e\n\u003cp\u003eXR\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eExtended reality\u003c/p\u003e\n\u003cp\u003eEBSCO\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eElton Bryson Stephens Company\u003c/p\u003e\n\u003cp\u003eGIS\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eGeographic Information Systems\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIoT\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eInternet of Things\u003c/p\u003e\n\u003cp\u003eML\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eMachine Learning\u003c/p\u003e\n\u003cp\u003eMR\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eMixed Reality\u003c/p\u003e\n\u003cp\u003eNEP\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eNational Education Policy\u003c/p\u003e\n\u003cp\u003eR\u0026amp;D\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eResearch and Development\u003c/p\u003e\n\u003cp\u003eSTEM\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eScience, Technology, Engineering, and Mathematics\u003c/p\u003e\n\u003cp\u003eUAVs\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eUnmanned Aerial Vehicles\u003c/p\u003e\n\u003cp\u003eVR\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eVirtual Reality\u003c/p\u003e\n\u003cp\u003eWOS\u003cspan style=\"white-space:pre;\"\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003eWeb of Science\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The authors declare that they have no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllison, J. 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A tale of two databases: the use of Web of Science and Scopus in academic papers. \u003cem\u003eScientometrics\u003c/em\u003e, \u003cem\u003e123\u003c/em\u003e(1), 321\u0026ndash;335. https://doi.org/10.1007/s11192-020-03387-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Sharjah","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computer-Altered Reality (CAR), Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Civil Engineering Education, Artificial Intelligence (AI), and Internet of Things (IoT)","lastPublishedDoi":"10.21203/rs.3.rs-5996662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5996662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing complexity of civil engineering demands innovative tools to bridge the gap between theory and practice. Computer-altered reality (CAR) technologies offer immersive environments that enhance learning outcomes. However, civil engineering education lags behind other disciplines in adopting these technologies. This study systematically reviewed 359 relevant studies from an initial pool of 1508 from 20214 to 2023 using a nine-step methodology involving keyword optimization, statistical analysis, and thematic mapping. The method employed was a systematic review following PRISMA guidelines. Key opportunities include improved visualization, increased engagement, and practical skill building, with 74% of studies reporting enhanced student performance. Trends reveal the growing integration of artificial intelligence (AI) and internet of things (IoT) into CAR platforms, enabling adaptive learning. For instance, AI-driven AR overlays improve site inspection accuracy by 36%, while IoT-linked virtual reality (VR) provides dynamic, contextual training. Comparatively, while disciplines like mechanical and aerospace engineering leverage CAR for design and manufacturing simulations, civil engineering applications are more focused on virtual construction sites and structural analysis, reflecting unique characteristics. Significant challenges persist, including high implementation costs (68%), insufficient educator training (54%), and limited infrastructure (41%). Ethical and psychological considerations remain largely unaddressed, with 95% of studies overlooking privacy, cybersecurity, and long-term psychological impacts, such as VR-induced discomfort. These gaps present critical areas for future research to ensure responsible CAR integration. Future directions include cost-effective CAR solutions, improved educator training, interdisciplinary collaborations, and a focus on ethical and cybersecurity concerns. Addressing the long-term psychological impacts of CAR technologies also remains imperative.\u003c/p\u003e","manuscriptTitle":"Advancing Civil Engineering Education: A Systematic Review of Opportunities, Trends, Challenges, and Future Research Directions in Computer-Altered Reality Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-12 04:59:39","doi":"10.21203/rs.3.rs-5996662/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adfbbd87-38b7-4644-91f1-45a133ea2314","owner":[],"postedDate":"February 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44085416,"name":"Civil Engineering"}],"tags":[],"updatedAt":"2025-02-12T04:59:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-12 04:59:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5996662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5996662","identity":"rs-5996662","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}