Evaluation of a virtual simulation system for root canal irrigation training in preclinical dental education

Evaluation of a virtual simulation system for root canal irrigation training in preclinical dental education | 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 Research Article Evaluation of a virtual simulation system for root canal irrigation training in preclinical dental education Xin Yue, Shuai Nie, Yanmei Dai, Jianping Sun, Li Xu, Jing Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7552542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2025 Read the published version in BMC Medical Education → Version 1 posted 10 You are reading this latest preprint version Abstract Background Root canal irrigation is a crucial component of endodontic treatment, yet it is often insufficiently addressed in dental education. Although its clinical significance is well recognized, dedicated simulation platforms for irrigation training remain scarce. This study aimed to develop a virtual simulation system for root canal irrigation and to evaluate its effectiveness in improving dental students’ knowledge and procedural competence. Methods Thirty-four dental students (26 undergraduates and 8 postgraduates) participated in this prospective study. After receiving standardized theoretical instruction, all students completed baseline and post-training assessments using three-dimensional printed tooth models. Training sessions were conducted with the newly developed virtual simulation system. Outcome measures included theoretical knowledge scores, pre- and post-training practical scores, and simulation-based performance scores. Data were analyzed using paired t-tests, Pearson correlation analyses, and subgroup comparisons. In addition, a 26-item Likert-scale questionnaire was administered to assess usability and learner perceptions. Results Significant improvements were observed in both theoretical knowledge (mean increase: 0.59 points, p < 0.001) and practical performance (mean increase: 3.15 points, p < 0.001). Simulation-derived performance scores demonstrated a strong positive correlation with post-training practical outcomes (r = 0.73, p < 0.001). Questionnaire analysis indicated consistently high ratings for learning effectiveness, usability, instructional value, and overall satisfaction, with Cronbach’s alpha values above 0.85 across all domains. Conclusion The proposed virtual simulation system effectively enhanced both cognitive and procedural learning in root canal irrigation. Its predictive validity and high learner acceptance support its integration into preclinical endodontic curricula. The greater relative benefit observed among undergraduates highlights its particular value for early-stage learners, providing a scalable and reproducible educational tool to address a critical gap in endodontic training. Virtual simulation Root canal irrigation Dental education Endodontic training Figures Figure 1 Figure 2 Introduction Virtual simulation (VS) education, encompassing virtual reality (VR), augmented reality (AR), mixed reality (MR), and screen-based interactive platforms, employs computer-generated three-dimensional models and immersive environments to support medical and dental training. These technologies allow learners to practice procedures outside of traditional clinical settings, thereby promoting a deeper understanding of complex concepts and facilitating the acquisition of technical skills[ 1 ]. Owing to its reproducibility, safety, and interactivity, VS has been increasingly incorporated into dental education. Root canal treatment is a cornerstone of endodontic training and comprises several critical stages, including root canal anatomy identification, access cavity preparation, canal instrumentation, irrigation and obturation. In recent years, advances in extended reality (XR) technologies have accelerated the development of simulation tools for endodontics, resulting in more engaging and effective teaching approaches. Studies have demonstrated that immersive VR, AR, and XR platforms improve students’ spatial understanding of canal morphology and increase their learning motivation compared with conventional approaches, such as two-dimensional radiographs or cone-beam computed tomography [ 2 – 4 ]. Among the procedural components, access cavity preparation has been most extensively standardized within simulation-based training. Commercial platforms, such as the Simodont Dental Trainer, have demonstrated comparable learning outcomes to natural teeth [ 5 ] and have been reported to improve fine motor skills and provide realistic haptic feedback [ 6 ]. Furthermore, stepwise instructional models that integrate VS with resin blocks or three-dimensional printed teeth have been shown to enhance student performance and confidence [ 7 , 8 ]. While mechanical preparation is indispensable in root canal therapy, it alone cannot achieve complete microbial elimination due to anatomical complexities. Chemical irrigation, therefore, plays a vital role in ensuring disinfection and treatment success [ 9 ]. Despite its importance, preclinical training in irrigation remains largely dependent on extracted human teeth [ 10 ]. In China, however, the use of such specimens has been increasingly restricted by ethical regulations [ 11 ], leading to reliance on acrylic or three-dimensional printed models [ 12 ]. These alternatives improve visibility and standardization but are costly, limited in reusability, and reliant on intensive faculty supervision. More importantly, they fail to reproduce the fluid dynamics of irrigants, restricting both students’ real-time learning and instructors’ ability to evaluate performance. Currently, no dedicated simulation platform exists for root canal irrigation training [ 13 ]. Existing systems typically cover endodontic procedures such as pulp revascularization [ 14 ] or apexogenesis [ 15 ] but only superficially address irrigation, often through simplified quizzes or linear task sequences. These approaches lack dynamic interactivity and real-time feedback, both of which are essential for effective procedural learning. To address these limitations, we developed a virtual simulation system specifically designed for irrigation training in endodontics. The system integrates experimental data from in vitro studies [ 16 ] to simulate irrigation fluid dynamics and replicates the clinical workflow through an XR-based interface with real-time performance assessment. This study aimed to evaluate the system’s effectiveness in improving dental students’ theoretical knowledge and procedural skills in root canal irrigation, as well as their overall acceptance of this novel training modality. Materials and Methods Development of the virtual simulation system The virtual simulation system was independently developed to replicate the workflow of root canal irrigation training. Its design was based on experimental data from prior in vitro irrigation studies [16].It has been deployed on the Nankai University Virtual Simulation Teaching Platform (https://ilab-x.nankai.edu.cn/#/subject/detail/112), where it is accessible to both internal and external users. The platform integrates real-time three-dimensional simulation of irrigant dynamics with an extended reality (XR) interface, providing visual feedback and automated performance evaluation. The simulation encompasses essential procedural steps, including rubber dam isolation, access cavity preparation, canal instrumentation, and a complete irrigation protocol, thereby closely mimicking clinical practice (Figure 1). Training modes The virtual simulation system provides multiple training modes to support progressive learning and assessment. These include guided learning, practice, and assessment, which allow students to transition from stepwise instruction to independent practice and formal evaluation with automated scoring. In addition, the system incorporates advanced options such as a Challenge Mode and an Expert Mode, which are designed to enhance problem-solving skills and simulate more complex clinical scenarios. Beyond procedural pathways, the system also integrates quantitative performance indicators, such as a “cleaning index,” to objectively measure irrigation effectiveness and procedural accuracy. Detailed descriptions of the training modes, operational workflow, and scoring metrics are provided in Additional file 1 . Study Design This prospective educational study was conducted at the School of Stomatology, Nankai University, between September 2024 and January 2025. A total of 34 dental students were recruited and stratified into two groups based on academic level: Undergraduate group (n = 26) : Fourth-year undergraduate students majoring in dentistry at Nankai University. Postgraduate group (n = 8) : First-year master’s students in dentistry from Nankai University and Tianjin Medical University. None of the undergraduates had received prior formal instruction in root canal irrigation, whereas all postgraduates had previously studied the topic during their undergraduate curriculum. Baseline assessment At baseline, participants attended a standardized 2-hour theoretical course covering the steps of root canal instrumentation, principles of irrigation, and properties of commonly used irrigants. Each student then performed a complete root canal preparation and irrigation procedure on a three-dimensional printed resin model of a maxillary anterior tooth. The procedures were supervised by four trained teaching assistants, who monitored the accuracy and completeness of each step. Performance was subsequently evaluated by a blinded experienced endodontist using light microscopy. Cleaning efficacy was assessed with a standardized rubric, and the resulting score was recorded as T1 . Participants also completed a theoretical quiz ( S1 ) consisting of eight single-choice questions assessing knowledge of irrigation objectives, irrigant types, mechanisms of action, procedural steps, and complications. Items were stratified by difficulty (three easy, three moderate, two difficult) and selected from a validated question bank routinely used in student examinations. Simulation training Between November 13 and 14, 2024, all participants received standardized training with the Virtual Simulation System for Root Canal Irrigation. Each student completed one session in Practice Mode and one session in Test Mode within a 45-minute session. The system automatically recorded performance metrics, including procedural completion, cleaning efficacy, and a composite score, documented as V1 . Post-training assessments To evaluate retention and delayed proficiency, a second practical assessment was conducted one week later, following the principle of the Ebbinghaus forgetting curve. Students repeated the root canal preparation and irrigation procedure on a three-dimensional printed model of a maxillary premolar. The procedures were supervised by the same teaching assistants, and cleaning efficacy was blindly evaluated by the same endodontist. This score was designated as T2 . At the same time, participants completed a second theoretical quiz ( S2 ), designed to be structurally and cognitively equivalent to S1, to assess changes in theoretical knowledge. Finally, all participants completed a structured 26-item questionnaire, which was specifically developed for this study, using a five-point Likert scale (1 = strongly disagree; 5 = strongly agree).The questionnaire evaluated five domains: ease of operation, perceived learning effectiveness, immersive experience, educational value, and willingness to adopt the system in future learning. The complete English version of all questionnaire items is provided in Additional file 3. An overview of the study design is presented in Figure 2. Statistical Analysis Methods A total of 34 participants were enrolled and stratified into an undergraduate group (n = 26) and a postgraduate group (n = 8). All statistical analyses were performed to evaluate the instructional effectiveness and practical value of the virtual simulation system for root canal irrigation. Data were analyzed using SPSS software, version 20.0 (IBM Corp., Armonk, NY, USA). All tests were two-tailed, and a p-value < 0.05 was considered statistically significant. Paired-sample t-tests were used to compare pre- and post-training theoretical knowledge scores (S1 vs. S2) and practical performance scores (T1 vs. T2), thereby assessing improvements in both cognitive learning and procedural skills. Pearson correlation coefficients were calculated to examine the relationship between virtual simulation performance scores (V1) and post-training outcomes (T2 and S2). For variables that did not meet the assumptions of normality, Spearman’s rank correlation coefficients were applied as nonparametric alternatives. Independent-sample t-tests were conducted to compare outcomes between undergraduate and postgraduate groups across multiple domains (S1, S2, T1, T2, and V1), in order to explore the potential influence of academic background on training efficacy. Independent-sample t-tests were conducted to compare outcomes between undergraduate and postgraduate groups across multiple domains (S1, S2, T1, T2, and V1), in order to explore the potential influence of academic background on training efficacy. Results (The detailed scoring criteria and sample data are provided in Additional file 2 .) Improvements in theoretical knowledge and procedural skills Post-training theoretical scores (S2) were significantly higher than pre-training scores (S1), with a mean increase of 9.3% (t(33) = 3.82, p < 0.001; Cohen’s d = 0.65). This indicates that the virtual simulation system enhanced participants’ understanding of root canal irrigation. Practical performance also improved significantly, with a mean increase of 54.4% from T1 to T2 (t(33) = 7.14, p < 0.001; Cohen’s d = 1.22), reflecting substantial gains in procedural competence following simulation-based training. Detailed results are presented in Table 1 . Table 1. Comparison of pre- and post-training theoretical and practical scores. Assessment Pre-training (M ± SD) Post-training (M ± SD) Δ M (95% CI) p-value Theory（S1→S2） 6.35±1.15 6.94±0.83 +0.59 (0.28-0.90) <0.001 Operation（T1→T2） 5.79±3.19 8.94±3.29 +3.15 (2.25-4.05) <0.001 Paired t-tests were used for all comparisons; df = 33 for all tests. Cohen’s d was 0.65 for theory and 1.22 for operation. Correlation Between Virtual Simulation Scores and Learning Performance A strong positive correlation was observed between virtual simulation performance (V1) and post-training practical performance (T2), with r = 0.73 (p < 0.001), indicating that V1 explained approximately 53% of the variance in T2. The fitted linear regression model was: T2 = 2.84 + 0.62 × V1. Moderate correlations were also identified between V1 and post-training theoretical knowledge scores (S2) (r = 0.42, p = 0.013), while a weak but statistically significant association was observed between T2 and S2 (r = 0.38, p = 0.026). These findings suggest that virtual performance not only predicts procedural competence but also partially reflects theoretical knowledge acquisition. All variables satisfied normality assumptions based on the Shapiro–Wilk test; therefore, Pearson correlation analysis was applied. Detailed correlation coefficients are presented in Table 2 . Table 2. Correlation between virtual simulation scores and learning performance Correlation Pair r 95% CI p-value R 2 V1 & T2 0.73 (0.53 to 0.85) <0.001 0.53 V1 & S2 0.42 (0.10 to 0.66) 0.013 0.18 T2 & S2 0.38 (0.05 to 0.63) 0.026 0.14 Comparison between undergraduate and postgraduate groups The postgraduate group consistently achieved higher scores than the undergraduate group across theoretical knowledge (S1, S2), practical performance (T1, T2), and virtual simulation outcomes (V1). However, none of these differences reached statistical significance (all p > 0.05; Table 3) . Baseline operational performance (T1) showed a trend toward significance (p = 0.096), with a moderate effect size (Cohen’s d = 0.67). While postgraduates demonstrated superior baseline proficiency, the performance gap narrowed after training. The improvement in practical scores was greater among undergraduates (ΔT = 3.30) than postgraduates (ΔT = 2.50), suggesting that the simulation system provided relatively greater benefits for undergraduate students by accelerating the acquisition of procedural skills. Table 3. Comparison of outcomes between undergraduate and postgraduate groups Variable Undergraduates (n=26) Mean±SD Postgraduates (n=8) Mean±SD Δ M (95% CI) p-value S1 6.15 ± 1.07 7.00 ± 1.20 -0.85 （-1.75~0.05） 0.063 S2 6.81 ± 0.80 7.38 ± 0.74 -0.57 （-1.18~0.04） 0.068 T1 5.35 ± 2.84 7.38 ± 3.50 -2.03 （-4.46~0.40） 0.096 T2 8.65 ± 3.19 9.88 ± 3.44 -1.23 （-3.83~1.37） 0.345 V1 9.65 ± 3.37 11.00 ± 3.07 -1.35 （-4.05~1.35） 0.314 Independent-sample t-tests were used for comparisons. Effect sizes (Cohen’s d) were moderate for S1, S2, and T1 (0.67–0.75) and small for T2 and V1 (<0.40). df = 32 for all tests. Questionnaire evaluation Overall descriptive statistics Student responses were highly favorable across all dimensions of the questionnaire. Mean ratings exceeded 4.2 on a five-point Likert scale, and more than 90% of responses fell within the 4–5 range, reflecting strong agreement regarding the system’s ease of use, perceived learning effectiveness, instructional value, and overall satisfaction (Table 4). Median scores closely approximated the means, indicating minimal skewness. Detailed frequency distributions for all items are provided in Additional file 3. Notably, items addressing clarity of step-by-step feedback (Q18) and appropriateness of task difficulty (Q20) received the highest proportions of maximum scores, with more than half of participants selecting “5.” In contrast, the clinical relevance item (Q21) received relatively more neutral responses (14.7%), likely due to limited clinical experience among early-stage trainees. Table 4. Mean questionnaire ratings by dimension (N = 34) Evaluation Dimension Mean ± SD Median Perceived Learning Effectiveness 4.35 ± 0.78 4.44 System Usability and Operation 4.28 ± 0.82 4.29 Instructional Value and Applicability 4.40 ± 0.75 4.43 Overall Satisfaction 4.45 ± 0.80 4.67 Scores are based on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree). Subgroup Analysis Postgraduate students rated all questionnaire dimensions marginally higher than undergraduates, with the largest differences observed in instructional value and overall satisfaction. Mean ratings for both groups exceeded 4.2 on the five-point scale, indicating generally favorable perceptions of the system ( Table 5 ). Undergraduates reported slightly more negative responses regarding system usability, reflected by a higher proportion of low ratings (scores 1–2: 4.2% vs. 1.8% among postgraduates). In particular, Item Q15, which assessed the consistency of virtual hand-motion feedback with user input, received the lowest ratings among undergraduates, with 7.7% assigning a score of 2. This highlights a potential area for further improvement in interactive feedback design. Postgraduates also demonstrated stronger recognition of the importance of refining irrigation techniques. For example, in response to Item Q23 (perceived need for skill improvement), 75.0% of postgraduates selected the highest score compared with 69.2% of undergraduates. Table 5. Questionnaire ratings by subgroup Evaluation Dimension Undergraduate Students (n = 26) Postgraduate Students (n = 8) Mean ± SD Median Mean ± SD Median Perceived Learning Effectiveness 4.32 ± 0.81 4.33 4.45 ± 0.68 4.56 System Usability and Operation 4.25 ± 0.85 4.29 4.38 ± 0.72 4.43 Instructional Value and Applicability 4.38 ± 0.78 4.43 4.48 ± 0.66 4.57 Overall Satisfaction 4.42 ± 0.83 4.67 4.54 ± 0.71 4.67 Scores are based on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree). Reliability Analysis All questionnaire dimensions demonstrated excellent internal consistency, with Cronbach’s α coefficients exceeding 0.80 across all domains. This result indicates that the items within each dimension were highly reliable in measuring the intended constructs (Table 6 ). Table 6. Internal consistency of questionnaire domains (Cronbach’s α) Evaluation Dimension No. of Items Total Undergraduate Postgraduate Perceived Learning Effectiveness 9 0.89 0.88 0.85 System Usability and Operation 7 0.87 0.86 0.82 Instructional Value and Applicability 7 0.91 0.90 0.89 Overall Satisfaction 3 0.88 0.87 0.84 Discussion This study evaluated the educational effectiveness of a self-developed virtual simulation system specifically designed for root canal irrigation training in preclinical dental education. The findings demonstrated significant improvements in both theoretical knowledge and procedural skills, and student feedback indicated high acceptance and satisfaction. Together, these results support the system’s potential integration into competency-based endodontic curricula. Enhancements in theoretical knowledge and procedural competence Post-training assessments revealed significant gains in theoretical knowledge and operational proficiency. Procedural performance improved by 54.4% with a large effect size (Cohen’s d = 1.22), while theoretical knowledge scores increased by 9.3% with a moderate effect size (Cohen’s d = 0.65). These results underscore the value of the simulation in developing psychomotor and cognitive competencies. Features such as sagittal-view visualization and real-time feedback likely contributed to these outcomes by making otherwise “invisible” irrigation dynamics more accessible to learners. These findings are consistent with earlier research demonstrating that simulation-based training supports procedural mastery and conceptual understanding in dentistry [ 2 , 7 , 17 ]. Correlation between virtual scores and learning outcomes A strong correlation was observed between simulation-derived performance scores (V1) and post-training practical performance (T2), with V1 explaining 53% of the variance in T2. This indicates that simulation metrics may serve as valid predictors of student competence. Similar observations have been reported in prior work showing that virtual training outcomes can reflect clinical readiness [ 18 ]. A moderate correlation between V1 and theoretical scores further suggests that simulation contributes to both procedural and cognitive development. Differential learning outcomes across academic levels Although postgraduates scored higher across most domains, none of the intergroup differences reached statistical significance. Notably, undergraduates demonstrated greater relative improvement in practical performance (ΔT = 3.30 vs. 2.50), suggesting that less experienced learners may derive greater benefit from simulation-based instruction. Subgroup analysis further indicated that undergraduates valued feedback mechanisms and visual guidance more highly, whereas postgraduates highlighted the need for greater task difficulty and more realistic haptic features. These findings align with prior studies suggesting that learners at different stages require tailored simulation designs [ 19 ]. Student acceptance and system usability Survey results reflected high overall satisfaction, with mean ratings exceeding 4.2 across all domains and Cronbach’s α values above 0.80, confirming strong reliability. Students particularly emphasized the value of stepwise feedback and progressive task difficulty. However, the relatively higher proportion of neutral responses to clinical relevance suggests that future iterations could incorporate case-based or clinically contextualized scenarios to strengthen applicability for preclinical learners. Innovations and educational implications Unlike existing platforms that primarily address root canal anatomy or access cavity preparation, this system uniquely targets irrigation—a critical yet underrepresented phase in dental education. By integrating immersive visualization, interactive functionality, and quantitative scoring, the system not only supports skill acquisition but also provides objective assessment. Based on these findings, we propose a three-stage educational framework: Theoretical instruction → Virtual simulation training → Phantom-head practice. This model could enhance psychomotor integration, improve procedural fluency, and accelerate readiness for clinical practice. Furthermore, the system addresses practical challenges in traditional teaching by enabling scalable, resource-efficient training and instructor-monitored performance tracking. Limitations and future directions Several limitations should be acknowledged. First, the small sample size, particularly in the postgraduate group, restricts the generalizability of subgroup findings. Second, although performance evaluation followed a standardized rubric, examiner judgment may have introduced subjectivity. Third, this study focused on short-term outcomes without assessing long-term retention. Future research should expand sample sizes, especially among advanced learners, and evaluate the Challenge and Expert modes to assess higher-order decision-making skills. Long-term follow-up assessments are needed to examine retention of knowledge and skills. Integration of AI-assisted scoring could further enhance objectivity and precision in performance evaluation. Addressing these areas will strengthen the evidence base for simulation in dental education and support its wider adoption. Conclusion This study developed and validated a novel virtual simulation system specifically designed for root canal irrigation training. The system was effective in improving both theoretical knowledge and procedural competence, with particularly notable benefits for undergraduate students. Simulation-derived performance scores correlated strongly with practical outcomes, supporting the system’s potential as an assessment and feedback tool in preclinical education. High levels of student satisfaction further demonstrated the system’s educational value and acceptability. By addressing a critical gap in irrigation-focused simulation, the platform provides a scalable and reproducible approach to enhance endodontic teaching. Future work should integrate more diverse clinical scenarios and evaluate long-term learning outcomes to support its incorporation into competency-based dental curricula. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Tianjin Stomatological Hospital (Approval No.: PH2023-B-005). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional review board and the Declaration of Helsinki (2013 revision). Written informed consent to participate was obtained from all student participants prior to enrollment in the study. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable. Funding This work was supported by the Medical Education Research Project of the Medical Education Branch of the Chinese Medical Association (Grant No. 2023B175), the Tianjin Science and Technology Planning Project (Grant No. 24KPHDRC00450), and the Tianjin Health Science and Technology Project, General Project (Grant No. TJWJ2023MS033). The funding bodies had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript. Authors’ contributions XY conceived and designed the study, collected data, and drafted the manuscript. SN contributed to data acquisition, analysis, and interpretation. YD supervised the teaching design and contributed to questionnaire analysis. JSu supported data collection and coordinated teaching implementation. LX provided technical support, software design, and system validation. JS conceived the study, supervised the research, critically revised the manuscript, and served as the corresponding author. All authors read and approved the final manuscript. Acknowledgments The authors thank Tianjin Hanhai Xingyun Technology Co., Ltd. (https://hanisun.com/) for providing technical support in the development of the Virtual Simulation System for Root Canal Irrigation Teaching utilized in this study. References Barjis J, Sharda R, Lee PD, Gupta A. Innovative teaching using simulation and virtual environments. Interdiscip J Inf Knowl Manag. 2012;7:63-76. 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Duan X, Zhang Q, Jiang Y, Yue X, Geng Y, Shen J, et al. Semiconducting polymer nanoparticles with intramolecular motion-induced photothermy for tumor phototheranostics and tooth root canal therapy. Adv Mater. 2022;34(17):2200179. Al-Saud LM, Mushtaq F, Allsop MJ, Culmer PC, Mirghani I, Yates E, et al. Feedback and motor skill acquisition using a haptic dental simulator. Eur J Dent Educ. 2017;21(4):240-7. Buchanan JA. Use of simulation technology in dental education. J Dent Educ. 2001;65(11):1225-31. Huang Q, Yan SY, Huang J, Liu X, Wang P, Chen Z, et al. Effectiveness of simulation-based clinical research curriculum for undergraduate medical students: a pre-post intervention study with external control. BMC Med Educ. 2024;24:542. Additional Declarations No competing interests reported. Supplementary Files Additionfile1.docx Additional files 1: Detailed descriptions of the training modes, operational workflow, and scoring metrics Additionfile2.docx Additional files 2: Scoring criteria and raw test results Additionfile3.docx Additional files 3: Frequency distribution of questionnaire responses Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7552542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542631436,"identity":"de04af5b-c163-4df3-a896-6fd02c4151fb","order_by":0,"name":"Xin Yue","email":"","orcid":"","institution":"Tianjin Stomatological Hospital, School of Medicine, Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yue","suffix":""},{"id":542631437,"identity":"d000b2a5-76be-46f2-8d12-62e7ca3e92aa","order_by":1,"name":"Shuai Nie","email":"","orcid":"","institution":"Tianjin Stomatological Hospital, 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University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xu","suffix":""},{"id":542631441,"identity":"0362abea-e765-4dca-a364-616eb27c1e8b","order_by":5,"name":"Jing Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACCRA2YGDgZ2ZsfJBQUUOCFsl25maDB2eOEakFBAz62dskH7YwE9YhP7v52AOLgjt2G5gZ2yoSG9gY+Nu7E/BqMbhzLN1AwuBZ8naglhuJO2QYJM6c3YBfi0SOmYSEweFky2aQljNsQJFc/FrkZ+R/A2sxOMzYVpDYxkxYC8ONHDaQFjuQFgaitBjcSAM7LEGymbFZIuHMMR6CfpGfkfxMWuLPYXt+/uMPP/6oqJHjb+8l4DAgYAbGTWIDlMNDUDkIMH5gYLAnSuUoGAWjYBSMTAAAZxdHyztXPYsAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Stomatological Hospital, School of Medicine, Nankai University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-09-06 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07:19:52","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91407,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/c7a290127fd250a25dcf042b.html"},{"id":95933001,"identity":"3bef2280-23b7-418b-89c2-de6c9f973eee","added_by":"auto","created_at":"2025-11-14 14:54:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":595888,"visible":true,"origin":"","legend":"\u003cp\u003eUser interface of the virtual simulation system for root canal irrigation training.\u003c/p\u003e\n\u003cp\u003eA. Main menu interface; B. Preoperative anesthesia module; C. Rubber dam isolation module; D. Access cavity preparation module; E. Root canal shaping module; F. Irrigation during shaping; G. Final irrigation using syringe-based technique; H. Final irrigation using ultrasonic activation.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/ab00ac36eff1a0a55dc89dcc.jpeg"},{"id":96244244,"identity":"83592b1a-c915-4ac0-9150-197be8f0db55","added_by":"auto","created_at":"2025-11-19 07:18:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213670,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the experimental design\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/7591e330fd76aaed610a9144.jpeg"},{"id":97724044,"identity":"87ffe107-4d4e-4765-b6c2-8a00ad6a68e8","added_by":"auto","created_at":"2025-12-08 16:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2080480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/2a3df0aa-6361-4c03-96a5-3ed26810bdf1.pdf"},{"id":96243657,"identity":"87198271-e653-4454-8cfd-3c1dd858296a","added_by":"auto","created_at":"2025-11-19 07:16:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2331773,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files 1: Detailed descriptions of the training modes, operational workflow, and scoring metrics\u003c/p\u003e","description":"","filename":"Additionfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/de7cf2facb314500c55e92ef.docx"},{"id":96244524,"identity":"ac1543cc-46d5-4e48-8088-b30999cc93c5","added_by":"auto","created_at":"2025-11-19 07:18:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20503,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files 2: Scoring criteria and raw test results\u003c/p\u003e","description":"","filename":"Additionfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/ce5c237af038c3b9b89213b2.docx"},{"id":95933004,"identity":"8fe9a1e5-de56-47dd-b08d-8d31531a4abf","added_by":"auto","created_at":"2025-11-14 14:54:30","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25164,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files 3: Frequency distribution of questionnaire responses\u003c/p\u003e","description":"","filename":"Additionfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7552542/v1/05ea6d1f8c50f18fd34ea812.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of a virtual simulation system for root canal irrigation training in preclinical dental education","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVirtual simulation (VS) education, encompassing virtual reality (VR), augmented reality (AR), mixed reality (MR), and screen-based interactive platforms, employs computer-generated three-dimensional models and immersive environments to support medical and dental training. These technologies allow learners to practice procedures outside of traditional clinical settings, thereby promoting a deeper understanding of complex concepts and facilitating the acquisition of technical skills[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Owing to its reproducibility, safety, and interactivity, VS has been increasingly incorporated into dental education.\u003c/p\u003e\u003cp\u003eRoot canal treatment is a cornerstone of endodontic training and comprises several critical stages, including root canal anatomy identification, access cavity preparation, canal instrumentation, irrigation and obturation. In recent years, advances in extended reality (XR) technologies have accelerated the development of simulation tools for endodontics, resulting in more engaging and effective teaching approaches. Studies have demonstrated that immersive VR, AR, and XR platforms improve students\u0026rsquo; spatial understanding of canal morphology and increase their learning motivation compared with conventional approaches, such as two-dimensional radiographs or cone-beam computed tomography [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong the procedural components, access cavity preparation has been most extensively standardized within simulation-based training. Commercial platforms, such as the Simodont Dental Trainer, have demonstrated comparable learning outcomes to natural teeth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and have been reported to improve fine motor skills and provide realistic haptic feedback [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, stepwise instructional models that integrate VS with resin blocks or three-dimensional printed teeth have been shown to enhance student performance and confidence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile mechanical preparation is indispensable in root canal therapy, it alone cannot achieve complete microbial elimination due to anatomical complexities. Chemical irrigation, therefore, plays a vital role in ensuring disinfection and treatment success [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite its importance, preclinical training in irrigation remains largely dependent on extracted human teeth [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In China, however, the use of such specimens has been increasingly restricted by ethical regulations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], leading to reliance on acrylic or three-dimensional printed models [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These alternatives improve visibility and standardization but are costly, limited in reusability, and reliant on intensive faculty supervision. More importantly, they fail to reproduce the fluid dynamics of irrigants, restricting both students\u0026rsquo; real-time learning and instructors\u0026rsquo; ability to evaluate performance.\u003c/p\u003e\u003cp\u003eCurrently, no dedicated simulation platform exists for root canal irrigation training [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Existing systems typically cover endodontic procedures such as pulp revascularization [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or apexogenesis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] but only superficially address irrigation, often through simplified quizzes or linear task sequences. These approaches lack dynamic interactivity and real-time feedback, both of which are essential for effective procedural learning.\u003c/p\u003e\u003cp\u003eTo address these limitations, we developed a virtual simulation system specifically designed for irrigation training in endodontics. The system integrates experimental data from in vitro studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to simulate irrigation fluid dynamics and replicates the clinical workflow through an XR-based interface with real-time performance assessment. This study aimed to evaluate the system\u0026rsquo;s effectiveness in improving dental students\u0026rsquo; theoretical knowledge and procedural skills in root canal irrigation, as well as their overall acceptance of this novel training modality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eDevelopment of the virtual simulation system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe virtual simulation system was independently developed to replicate the workflow of root canal irrigation training. Its design was based on experimental data from prior in vitro irrigation studies [16].It has been deployed on the Nankai University Virtual Simulation Teaching Platform (https://ilab-x.nankai.edu.cn/#/subject/detail/112), where it is accessible to both internal and external users.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe platform integrates real-time three-dimensional simulation of irrigant dynamics with an extended reality (XR) interface, providing visual feedback and automated performance evaluation. The simulation encompasses essential procedural steps, including rubber dam isolation, access cavity preparation, canal instrumentation, and a complete irrigation protocol, thereby closely mimicking clinical practice (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining modes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe virtual simulation system provides multiple training modes to support progressive learning and assessment. These include guided learning, practice, and assessment, which allow students to transition from stepwise instruction to independent practice and formal evaluation with automated scoring. In addition, the system incorporates advanced options such as a Challenge Mode and an Expert Mode, which are designed to enhance problem-solving skills and simulate more complex clinical scenarios.\u003c/p\u003e\n\u003cp\u003eBeyond procedural pathways, the system also integrates quantitative performance indicators, such as a \u0026ldquo;cleaning index,\u0026rdquo; to objectively measure irrigation effectiveness and procedural accuracy.\u003c/p\u003e\n\u003cp\u003eDetailed descriptions of the training modes, operational workflow, and scoring metrics are provided in \u003cstrong\u003eAdditional file 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective educational study was conducted at the School of Stomatology, Nankai University, between September 2024 and January 2025. A total of 34 dental students were recruited and stratified into two groups based on academic level:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eUndergraduate group (n = 26)\u003c/strong\u003e: Fourth-year undergraduate students majoring in dentistry at Nankai University.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePostgraduate group (n = 8)\u003c/strong\u003e: First-year master\u0026rsquo;s students in dentistry from Nankai University and Tianjin Medical University.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNone of the undergraduates had received prior formal instruction in root canal irrigation, whereas all postgraduates had previously studied the topic during their undergraduate curriculum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt baseline, participants attended a standardized 2-hour theoretical course covering the steps of root canal instrumentation, principles of irrigation, and properties of commonly used irrigants. Each student then performed a complete root canal preparation and irrigation procedure on a three-dimensional printed resin model of a maxillary anterior tooth. The procedures were supervised by four trained teaching assistants, who monitored the accuracy and completeness of each step.\u003c/p\u003e\n\u003cp\u003ePerformance was subsequently evaluated by a blinded experienced endodontist using light microscopy. Cleaning efficacy was assessed with a standardized rubric, and the resulting score was recorded as \u003cstrong\u003eT1\u003c/strong\u003e. Participants also completed a theoretical quiz (\u003cstrong\u003eS1\u003c/strong\u003e) consisting of eight single-choice questions assessing knowledge of irrigation objectives, irrigant types, mechanisms of action, procedural steps, and complications. Items were stratified by difficulty (three easy, three moderate, two difficult) and selected from a validated question bank routinely used in student examinations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSimulation training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween November 13 and 14, 2024, all participants received standardized training with the Virtual Simulation System for Root Canal Irrigation. Each student completed one session in Practice Mode and one session in Test Mode within a 45-minute session. The system automatically recorded performance metrics, including procedural completion, cleaning efficacy, and a composite score, documented as \u003cstrong\u003eV1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-training assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate retention and delayed proficiency, a second practical assessment was conducted one week later, following the principle of the Ebbinghaus forgetting curve. Students repeated the root canal preparation and irrigation procedure on a three-dimensional printed model of a maxillary premolar. The procedures were supervised by the same teaching assistants, and cleaning efficacy was blindly evaluated by the same endodontist. This score was designated as \u003cstrong\u003eT2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAt the same time, participants completed a second theoretical quiz (\u003cstrong\u003eS2\u003c/strong\u003e), designed to be structurally and cognitively equivalent to S1, to assess changes in theoretical knowledge.\u003c/p\u003e\n\u003cp\u003eFinally, all participants completed a structured 26-item questionnaire, which was specifically developed for this study, using a five-point Likert scale (1 = strongly disagree; 5 = strongly agree).The questionnaire evaluated five domains: ease of operation, perceived learning effectiveness, immersive experience, educational value, and willingness to adopt the system in future learning.\u0026nbsp;The complete English version of all questionnaire items is provided in Additional file 3.\u003c/p\u003e\n\u003cp\u003eAn overview of the study design is presented in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 34 participants were enrolled and stratified into an undergraduate group (n = 26) and a postgraduate group (n = 8). All statistical analyses were performed to evaluate the instructional effectiveness and practical value of the virtual simulation system for root canal irrigation. Data were analyzed using SPSS software, version 20.0 (IBM Corp., Armonk, NY, USA). All tests were two-tailed, and a p-value \u0026lt; 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaired-sample t-tests were used to compare pre- and post-training theoretical knowledge scores (S1 vs. S2) and practical performance scores (T1 vs. T2), thereby assessing improvements in both cognitive learning and procedural skills.\u003c/p\u003e\n\u003cp\u003ePearson correlation coefficients were calculated to examine the relationship between virtual simulation performance scores (V1) and post-training outcomes (T2 and S2). For variables that did not meet the assumptions of normality, Spearman\u0026rsquo;s rank correlation coefficients were applied as nonparametric alternatives.\u003c/p\u003e\n\u003cp\u003eIndependent-sample t-tests were conducted to compare outcomes between undergraduate and postgraduate groups across multiple domains (S1, S2, T1, T2, and V1), in order to explore the potential influence of academic background on training efficacy.\u003c/p\u003e\n\u003cp\u003eIndependent-sample t-tests were conducted to compare outcomes between undergraduate and postgraduate groups across multiple domains (S1, S2, T1, T2, and V1), in order to explore the potential influence of academic background on training efficacy.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e(The detailed scoring criteria and sample data are provided in \u003cstrong\u003eAdditional file 2\u003c/strong\u003e.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImprovements in theoretical knowledge and procedural skills\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-training theoretical scores (S2) were significantly higher than pre-training scores (S1), with a mean increase of 9.3% (t(33) = 3.82, p \u0026lt; 0.001; Cohen\u0026rsquo;s d = 0.65). This indicates that the virtual simulation system enhanced participants\u0026rsquo; understanding of root canal irrigation.\u003c/p\u003e\n\u003cp\u003ePractical performance also improved significantly, with a mean increase of 54.4% from T1 to T2 (t(33) = 7.14, p \u0026lt; 0.001; Cohen\u0026rsquo;s d = 1.22), reflecting substantial gains in procedural competence following simulation-based training. Detailed results are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Comparison of pre- and post-training theoretical and practical scores.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-training\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(M \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-training\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(M \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;\u003c/strong\u003e\u003cstrong\u003eM (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eTheory（S1\u0026rarr;S2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6.35\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.94\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e+0.59 (0.28-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eOperation（T1\u0026rarr;T2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5.79\u0026plusmn;3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.94\u0026plusmn;3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e+3.15 (2.25-4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePaired t-tests were used for all comparisons; df = 33 for all tests. Cohen\u0026rsquo;s d was 0.65 for theory and 1.22 for operation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between Virtual Simulation Scores and Learning Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA strong positive correlation was observed between virtual simulation performance (V1) and post-training practical performance (T2), with r = 0.73 (p \u0026lt; 0.001), indicating that V1 explained approximately 53% of the variance in T2. The fitted linear regression model was: T2 = 2.84 + 0.62 \u0026times; V1.\u003c/p\u003e\n\u003cp\u003eModerate correlations were also identified between V1 and post-training theoretical knowledge scores (S2) (r = 0.42, p = 0.013), while a weak but statistically significant association was observed between T2 and S2 (r = 0.38, p = 0.026). These findings suggest that virtual performance not only predicts procedural competence but also partially reflects theoretical knowledge acquisition.\u003c/p\u003e\n\u003cp\u003eAll variables satisfied normality assumptions based on the Shapiro\u0026ndash;Wilk test; therefore, Pearson correlation analysis was applied. Detailed correlation coefficients are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Correlation between virtual simulation scores and learning performance\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation Pair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eV1 \u0026amp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.53 to 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eV1 \u0026amp; S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.10 to 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eT2 \u0026amp; S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.05 to 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eComparison between undergraduate and postgraduate groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe postgraduate group consistently achieved higher scores than the undergraduate group across theoretical knowledge (S1, S2), practical performance (T1, T2), and virtual simulation outcomes (V1). However, none of these differences reached statistical significance (all p \u0026gt; 0.05; \u003cstrong\u003eTable 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBaseline operational performance (T1) showed a trend toward significance (p = 0.096), with a moderate effect size (Cohen\u0026rsquo;s d = 0.67). While postgraduates demonstrated superior baseline proficiency, the performance gap narrowed after training. The improvement in practical scores was greater among undergraduates (\u0026Delta;T = 3.30) than postgraduates (\u0026Delta;T = 2.50), suggesting that the simulation system provided relatively greater benefits for undergraduate students by accelerating the acquisition of procedural skills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of outcomes between undergraduate and postgraduate groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUndergraduates\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=26) Mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostgraduates\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=8) Mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;\u003c/strong\u003e\u003cstrong\u003eM (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6.15 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e7.00 \u0026plusmn; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003cp\u003e（-1.75~0.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6.81 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e7.38 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003cp\u003e（-1.18~0.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5.35 \u0026plusmn; 2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e7.38 \u0026plusmn; 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003cp\u003e（-4.46~0.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e8.65 \u0026plusmn; 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e9.88 \u0026plusmn; 3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-1.23\u003c/p\u003e\n \u003cp\u003e（-3.83~1.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e9.65 \u0026plusmn; 3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e11.00 \u0026plusmn; 3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003cp\u003e（-4.05~1.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eIndependent-sample t-tests were used for comparisons. Effect sizes (Cohen\u0026rsquo;s d) were moderate for S1, S2, and T1 (0.67\u0026ndash;0.75) and small for T2 and V1 (\u0026lt;0.40). df = 32 for all tests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire evaluation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudent responses were highly favorable across all dimensions of the questionnaire. Mean ratings exceeded 4.2 on a five-point Likert scale, and more than 90% of responses fell within the 4\u0026ndash;5 range, reflecting strong agreement regarding the system\u0026rsquo;s ease of use, perceived learning effectiveness, instructional value, and overall satisfaction (Table 4). Median scores closely approximated the means, indicating minimal skewness.\u003c/p\u003e\n\u003cp\u003eDetailed frequency distributions for all items are provided in \u003cstrong\u003eAdditional file 3.\u0026nbsp;\u003c/strong\u003eNotably, items addressing clarity of step-by-step feedback (Q18) and appropriateness of task difficulty (Q20) received the highest proportions of maximum scores, with more than half of participants selecting \u0026ldquo;5.\u0026rdquo; In contrast, the clinical relevance item (Q21) received relatively more neutral responses (14.7%), likely due to limited clinical experience among early-stage trainees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Mean questionnaire ratings by dimension (N = 34)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvaluation Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePerceived Learning Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.35 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSystem Usability and Operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.28 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eInstructional Value and Applicability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.40 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eOverall Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.45 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eScores are based on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePostgraduate students rated all questionnaire dimensions marginally higher than undergraduates, with the largest differences observed in instructional value and overall satisfaction. Mean ratings for both groups exceeded 4.2 on the five-point scale, indicating generally favorable perceptions of the system (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eUndergraduates reported slightly more negative responses regarding system usability, reflected by a higher proportion of low ratings (scores 1\u0026ndash;2: 4.2% vs. 1.8% among postgraduates). In particular, Item Q15, which assessed the consistency of virtual hand-motion feedback with user input, received the lowest ratings among undergraduates, with 7.7% assigning a score of 2. This highlights a potential area for further improvement in interactive feedback design.\u003c/p\u003e\n\u003cp\u003ePostgraduates also demonstrated stronger recognition of the importance of refining irrigation techniques. For example, in response to Item Q23 (perceived need for skill improvement), 75.0% of postgraduates selected the highest score compared with 69.2% of undergraduates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Questionnaire ratings by subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003eEvaluation Dimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUndergraduate Students (n = 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 213px;\"\u003e\n \u003cp\u003ePostgraduate Students (n = 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003ePerceived Learning Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.32 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.45 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSystem Usability and Operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.25 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.38 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eInstructional Value and Applicability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.38 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.48 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eOverall Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.42 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.54 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eScores are based on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll questionnaire dimensions demonstrated excellent internal consistency, with Cronbach\u0026rsquo;s \u0026alpha; coefficients exceeding 0.80 across all domains. This result indicates that the items within each dimension were highly reliable in measuring the intended constructs \u003cstrong\u003e(Table 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Internal consistency of questionnaire domains (Cronbach\u0026rsquo;s \u0026alpha;)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvaluation Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUndergraduate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostgraduate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePerceived Learning Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSystem Usability and Operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eInstructional Value and Applicability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eOverall Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the educational effectiveness of a self-developed virtual simulation system specifically designed for root canal irrigation training in preclinical dental education. The findings demonstrated significant improvements in both theoretical knowledge and procedural skills, and student feedback indicated high acceptance and satisfaction. Together, these results support the system\u0026rsquo;s potential integration into competency-based endodontic curricula.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEnhancements in theoretical knowledge and procedural competence\u003c/h2\u003e\u003cp\u003ePost-training assessments revealed significant gains in theoretical knowledge and operational proficiency. Procedural performance improved by 54.4% with a large effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.22), while theoretical knowledge scores increased by 9.3% with a moderate effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.65). These results underscore the value of the simulation in developing psychomotor and cognitive competencies. Features such as sagittal-view visualization and real-time feedback likely contributed to these outcomes by making otherwise \u0026ldquo;invisible\u0026rdquo; irrigation dynamics more accessible to learners. These findings are consistent with earlier research demonstrating that simulation-based training supports procedural mastery and conceptual understanding in dentistry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between virtual scores and learning outcomes\u003c/h2\u003e\u003cp\u003eA strong correlation was observed between simulation-derived performance scores (V1) and post-training practical performance (T2), with V1 explaining 53% of the variance in T2. This indicates that simulation metrics may serve as valid predictors of student competence. Similar observations have been reported in prior work showing that virtual training outcomes can reflect clinical readiness [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A moderate correlation between V1 and theoretical scores further suggests that simulation contributes to both procedural and cognitive development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDifferential learning outcomes across academic levels\u003c/h2\u003e\u003cp\u003eAlthough postgraduates scored higher across most domains, none of the intergroup differences reached statistical significance. Notably, undergraduates demonstrated greater relative improvement in practical performance (ΔT\u0026thinsp;=\u0026thinsp;3.30 vs. 2.50), suggesting that less experienced learners may derive greater benefit from simulation-based instruction. Subgroup analysis further indicated that undergraduates valued feedback mechanisms and visual guidance more highly, whereas postgraduates highlighted the need for greater task difficulty and more realistic haptic features. These findings align with prior studies suggesting that learners at different stages require tailored simulation designs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStudent acceptance and system usability\u003c/h2\u003e\u003cp\u003eSurvey results reflected high overall satisfaction, with mean ratings exceeding 4.2 across all domains and Cronbach\u0026rsquo;s α values above 0.80, confirming strong reliability. Students particularly emphasized the value of stepwise feedback and progressive task difficulty. However, the relatively higher proportion of neutral responses to clinical relevance suggests that future iterations could incorporate case-based or clinically contextualized scenarios to strengthen applicability for preclinical learners.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eInnovations and educational implications\u003c/h2\u003e\u003cp\u003eUnlike existing platforms that primarily address root canal anatomy or access cavity preparation, this system uniquely targets irrigation\u0026mdash;a critical yet underrepresented phase in dental education. By integrating immersive visualization, interactive functionality, and quantitative scoring, the system not only supports skill acquisition but also provides objective assessment. Based on these findings, we propose a three-stage educational framework:\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical instruction \u0026rarr; Virtual simulation training \u0026rarr; Phantom-head practice.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis model could enhance psychomotor integration, improve procedural fluency, and accelerate readiness for clinical practice. Furthermore, the system addresses practical challenges in traditional teaching by enabling scalable, resource-efficient training and instructor-monitored performance tracking.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future directions\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the small sample size, particularly in the postgraduate group, restricts the generalizability of subgroup findings. Second, although performance evaluation followed a standardized rubric, examiner judgment may have introduced subjectivity. Third, this study focused on short-term outcomes without assessing long-term retention.\u003c/p\u003e\u003cp\u003eFuture research should expand sample sizes, especially among advanced learners, and evaluate the Challenge and Expert modes to assess higher-order decision-making skills. Long-term follow-up assessments are needed to examine retention of knowledge and skills. Integration of AI-assisted scoring could further enhance objectivity and precision in performance evaluation. Addressing these areas will strengthen the evidence base for simulation in dental education and support its wider adoption.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed and validated a novel virtual simulation system specifically designed for root canal irrigation training. The system was effective in improving both theoretical knowledge and procedural competence, with particularly notable benefits for undergraduate students. Simulation-derived performance scores correlated strongly with practical outcomes, supporting the system\u0026rsquo;s potential as an assessment and feedback tool in preclinical education.\u003c/p\u003e\u003cp\u003eHigh levels of student satisfaction further demonstrated the system\u0026rsquo;s educational value and acceptability. By addressing a critical gap in irrigation-focused simulation, the platform provides a scalable and reproducible approach to enhance endodontic teaching. Future work should integrate more diverse clinical scenarios and evaluate long-term learning outcomes to support its incorporation into competency-based dental curricula.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tianjin Stomatological Hospital (Approval No.: PH2023-B-005). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional review board and the Declaration of Helsinki (2013 revision). Written informed consent to participate was obtained from all student participants prior to enrollment in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Medical Education Research Project of the Medical Education Branch of the Chinese Medical Association (Grant No. 2023B175), the Tianjin Science and Technology Planning Project (Grant No. 24KPHDRC00450), and the Tianjin Health Science and Technology Project, General Project (Grant No. TJWJ2023MS033). The funding bodies had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXY conceived and designed the study, collected data, and drafted the manuscript. SN contributed to data acquisition, analysis, and interpretation. YD supervised the teaching design and contributed to questionnaire analysis. JSu supported data collection and coordinated teaching implementation. LX provided technical support, software design, and system validation. JS conceived the study, supervised the research, critically revised the manuscript, and served as the corresponding author. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Tianjin Hanhai Xingyun Technology Co., Ltd. (https://hanisun.com/) for providing technical support in the development of the Virtual Simulation System for Root Canal Irrigation Teaching utilized in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarjis J, Sharda R, Lee PD, Gupta A. Innovative teaching using simulation and virtual environments. Interdiscip J Inf Knowl Manag. 2012;7:63-76.\u003c/li\u003e\n\u003cli\u003eReymus M, Liebermann A, Diegritz C. Virtual reality: an effective tool for teaching root canal anatomy to undergraduate dental students\u0026mdash;a preliminary study. Int Endod J. 2020;53(11):1581-7.\u003c/li\u003e\n\u003cli\u003eAlsalleeh F, Okazaki K, Alkahtany S, Al-Hazmi N, Al-Ahdal R, Alqarni M, et al. Augmented reality improved knowledge and efficiency of root canal anatomy learning: a comparative study. Appl Sci. 2024;14(15):6813.\u003c/li\u003e\n\u003cli\u003eDiegritz C, Fotiadou C, Fleischer F, Zehentmeier S, Jansen T, Liebermann A, et al. Tooth Anatomy Inspector: a comprehensive assessment of an extended reality (XR) application designed for teaching and learning of root canal anatomy by students. Int Endod J. 2024;57(11):1682-8.\u003c/li\u003e\n\u003cli\u003eBa-Hattab R, Helvacioglu-Yigit D, Anweigi L, Al-Ostoot E, Aksoy S, Altunsoy M, et al. Impact of virtual reality simulation in endodontics on the learning experiences of undergraduate dental students. Appl Sci. 2023;13(2):981.\u003c/li\u003e\n\u003cli\u003eSlaczka DM, Shah R, Liu C, Nguy J, Goel A, Chen J, et al. Endodontic access cavity training using artificial teeth and Simodont\u0026reg; dental trainer: a comparison of student performance and acceptance. Int Endod J. 2024;57(9):1121-30.\u003c/li\u003e\n\u003cli\u003eWei Y, Peng Z. Application of Simodont virtual simulation system for preclinical teaching of access and coronal cavity preparation. PLoS One. 2024;19(12):e0315732.\u003c/li\u003e\n\u003cli\u003eDuan M, Lv S, Fan B, Chen Z, Yang Y, Zhang L, et al. Effect of 3D printed teeth and virtual simulation system on the pre-clinical access cavity preparation training of senior dental undergraduates. BMC Med Educ. 2024;24(1):913.\u003c/li\u003e\n\u003cli\u003eZou X, Zheng X, Liang Y, Wang Q, Hu T, Gao Y, et al. Expert consensus on irrigation and intracanal medication in root canal therapy. Int J Oral Sci. 2024;16(1):23.\u003c/li\u003e\n\u003cli\u003eSaeed S, Abdulrahman Y. The dentistry students\u0026rsquo; experiences of training with Simodont\u0026reg; compared to traditional pre-clinical training. Eur J Dent Educ. 2021;25(4):681-7.\u003c/li\u003e\n\u003cli\u003eNational Health Commission of the People\u0026rsquo;s Republic of China. Notice on issuing ethical review measures for life sciences and medical research involving humans [Internet]. Beijing: The Commission; 2023 Feb 18 [cited 2025 Jun 1]. Available from: https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm\u003c/li\u003e\n\u003cli\u003eHuang Q, Zeng Q, Wang X, Li Y, Chen J, Zhang L, et al. Customized virtual simulation platform for practicing root canal filling in undergraduate preclinical course. J Dent Educ. 2025;89(5):e13901.\u003c/li\u003e\n\u003cli\u003eMoussa R, Alghazaly A, Althagafi N, AlMalki A, Alqahtani A, AlShehri H, et al. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022;16(1):14-31.\u003c/li\u003e\n\u003cli\u003eHu J, Wang X, Chen R, Liu Y, Zhao L, Li P, et al. Use of virtual simulation for regenerative endodontic training: randomized controlled trial. BMC Med Educ. 2025;25(1):254.\u003c/li\u003e\n\u003cli\u003eMa L, Lai H, Zhao W. Evaluating the effectiveness of a virtual simulation platform for apexification learning. Dent J (Basel). 2024;12(2):27.\u003c/li\u003e\n\u003cli\u003eDuan X, Zhang Q, Jiang Y, Yue X, Geng Y, Shen J, et al. Semiconducting polymer nanoparticles with intramolecular motion-induced photothermy for tumor phototheranostics and tooth root canal therapy. Adv Mater. 2022;34(17):2200179.\u003c/li\u003e\n\u003cli\u003eAl-Saud LM, Mushtaq F, Allsop MJ, Culmer PC, Mirghani I, Yates E, et al. Feedback and motor skill acquisition using a haptic dental simulator. Eur J Dent Educ. 2017;21(4):240-7.\u003c/li\u003e\n\u003cli\u003eBuchanan JA. Use of simulation technology in dental education. J Dent Educ. 2001;65(11):1225-31.\u003c/li\u003e\n\u003cli\u003eHuang Q, Yan SY, Huang J, Liu X, Wang P, Chen Z, et al. Effectiveness of simulation-based clinical research curriculum for undergraduate medical students: a pre-post intervention study with external control. BMC Med Educ. 2024;24:542.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Virtual simulation, Root canal irrigation, Dental education, Endodontic training","lastPublishedDoi":"10.21203/rs.3.rs-7552542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7552542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRoot canal irrigation is a crucial component of endodontic treatment, yet it is often insufficiently addressed in dental education. Although its clinical significance is well recognized, dedicated simulation platforms for irrigation training remain scarce. This study aimed to develop a virtual simulation system for root canal irrigation and to evaluate its effectiveness in improving dental students\u0026rsquo; knowledge and procedural competence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThirty-four dental students (26 undergraduates and 8 postgraduates) participated in this prospective study. After receiving standardized theoretical instruction, all students completed baseline and post-training assessments using three-dimensional printed tooth models. Training sessions were conducted with the newly developed virtual simulation system. Outcome measures included theoretical knowledge scores, pre- and post-training practical scores, and simulation-based performance scores. Data were analyzed using paired t-tests, Pearson correlation analyses, and subgroup comparisons. In addition, a 26-item Likert-scale questionnaire was administered to assess usability and learner perceptions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSignificant improvements were observed in both theoretical knowledge (mean increase: 0.59 points, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and practical performance (mean increase: 3.15 points, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Simulation-derived performance scores demonstrated a strong positive correlation with post-training practical outcomes (r\u0026thinsp;=\u0026thinsp;0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Questionnaire analysis indicated consistently high ratings for learning effectiveness, usability, instructional value, and overall satisfaction, with Cronbach\u0026rsquo;s alpha values above 0.85 across all domains.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe proposed virtual simulation system effectively enhanced both cognitive and procedural learning in root canal irrigation. Its predictive validity and high learner acceptance support its integration into preclinical endodontic curricula. The greater relative benefit observed among undergraduates highlights its particular value for early-stage learners, providing a scalable and reproducible educational tool to address a critical gap in endodontic training.\u003c/p\u003e","manuscriptTitle":"Evaluation of a virtual simulation system for root canal irrigation training in preclinical dental education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 14:54:25","doi":"10.21203/rs.3.rs-7552542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T11:21:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T20:13:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"785022580131828748880975297890354049","date":"2025-11-06T05:12:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T19:04:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40391386686731932256700813528284766030","date":"2025-11-05T12:55:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T13:52:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T12:57:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T14:14:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T10:40:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-09-26T10:37:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eeb178af-1870-4326-ad12-9fccfc15ea5d","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:05:42+00:00","versionOfRecord":{"articleIdentity":"rs-7552542","link":"https://doi.org/10.1186/s12909-025-08387-x","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-12-03 15:57:40","publishedOnDateReadable":"December 3rd, 2025"},"versionCreatedAt":"2025-11-14 14:54:25","video":"","vorDoi":"10.1186/s12909-025-08387-x","vorDoiUrl":"https://doi.org/10.1186/s12909-025-08387-x","workflowStages":[]},"version":"v1","identity":"rs-7552542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7552542","identity":"rs-7552542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}