A web-based augmented reality system for orthodontic biomechanics: perceived usability and acceptability among dental students - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice BMC Oral Health . 2026 Mar 7;26:671. doi: 10.1186/s12903-026-08004-3 Search in PMC Search in PubMed View in NLM Catalog Add to search A web-based augmented reality system for orthodontic biomechanics: perceived usability and acceptability among dental students Xiao Li Xiao Li 1 Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200 Malaysia Find articles by Xiao Li 1 , Tia Farisha Shaharul Miza Tia Farisha Shaharul Miza 2 Department of Computing, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Malaysia Find articles by Tia Farisha Shaharul Miza 2 , Syaidatul Salmah Nurbalqis Saiful Syaidatul Salmah Nurbalqis Saiful 1 Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200 Malaysia Find articles by Syaidatul Salmah Nurbalqis Saiful 1 , Gururajaprasad Kaggal Lakshmana Rao Gururajaprasad Kaggal Lakshmana Rao 3 Department of Orthodontics, Penang International Dental College, NB Tower 5050, Jalan Bagan Luar, Butterworth, 12000 Malaysia Find articles by Gururajaprasad Kaggal Lakshmana Rao 3 , Fakhitah Ridzuan Fakhitah Ridzuan 4 Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, Kota Bharu, Kelantan 16100 Malaysia Find articles by Fakhitah Ridzuan 4 , Hasnah Hashim Hasnah Hashim 5 Department of Dental Public Health, Faculty of Dentistry, Asian Institute of Medicine, Science and Technology (AIMST) University, Bedong, Kedah 08100 Malaysia Find articles by Hasnah Hashim 5 , Zuhaila Ismail Zuhaila Ismail 6 Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor 81310 Malaysia Find articles by Zuhaila Ismail 6 , Nordin Zakaria Nordin Zakaria 2 Department of Computing, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Malaysia Find articles by Nordin Zakaria 2 , Norehan Mokhtar Norehan Mokhtar 7 Dental Simulation and Virtual Learning Research Excellence Consortium, Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200 Malaysia Find articles by Norehan Mokhtar 7, ✉ Author information Article notes Copyright and License information 1 Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200 Malaysia 2 Department of Computing, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Malaysia 3 Department of Orthodontics, Penang International Dental College, NB Tower 5050, Jalan Bagan Luar, Butterworth, 12000 Malaysia 4 Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, Kota Bharu, Kelantan 16100 Malaysia 5 Department of Dental Public Health, Faculty of Dentistry, Asian Institute of Medicine, Science and Technology (AIMST) University, Bedong, Kedah 08100 Malaysia 6 Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor 81310 Malaysia 7 Dental Simulation and Virtual Learning Research Excellence Consortium, Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200 Malaysia ✉ Corresponding author. Received 2025 Jun 27; Accepted 2026 Feb 24; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . PMC Copyright notice PMCID: PMC13081589 PMID: 41794718 Abstract Introduction The orthodontic biomechanics of tooth movement form the foundation of orthodontics and is essential for successful treatment outcomes. However, the traditional teaching method faces challenges, such as the complexity of concepts and difficulties in explaining three-dimensional (3D) tooth movement. To address these challenges, a web-based augmented reality (AR) system for orthodontic biomechanics (WAR Orthobiomechanics) was developed as a blended learning tool. Aims To evaluate the user satisfaction and behavioural intention of the WAR Orthobiomechanics system. Materials and methods A total of 56 undergraduate dental students from Years 3 to 5 at three institutions in Malaysia participated in this study. The System Usability Scale (SUS) and Technology Acceptance Model (TAM) were employed to assess participants’ perceived usability and acceptability. Data were collected between July and August 2023 and analysed using descriptive statistics, independent t -test, one-way ANOVA analysis, and Pearson correlation analyses. Results Both SUS and TAM indicated positive user experiences. The overall SUS score was 68.97 ± 12.87 (moderate to good). The TAM showed satisfactory acceptability with a mean score of 76.92 ± 13.22. Pearson correlation analyses showed a moderate positive association between SUS and the overall TAM score ( r = 0.493, p < 0.01), indicating concurrent validity between perceived usability and technology acceptance. Conclusion The web-based AR system demonstrated acceptable usability and positive student acceptability in supporting the learning of orthodontic biomechanics. The evaluation results indicate that the system is feasible as a supplementary instructional tool. Keywords: Orthodontic education, Educational technology, Augmented reality, System usability scale, Technology acceptance model Introduction Orthodontic treatment involves applying orthodontic force on the dental crowns to realign misaligned teeth. A thorough understanding of the biomechanics of orthodontic tooth movement (OTM) is essential for effective and safe orthodontic treatment [ 8 , 34 ]. Misapplication of biomechanical principles may lead to undesirable movement or damage to periodontal tissue [ 25 ]. A solid grasp of orthodontic biomechanics may reduce treatment time by enabling more efficient tooth movement [ 50 ]. Therefore, the accurate application of biomechanical principles is crucial to achieving successful orthodontic outcomes. Based on the Curriculum Guidelines for Orthodontics issued by the American Dental Education Association (ADEA) [ 11 ], the biomechanics of OTM and types of tooth movement should be an important element of undergraduate dental education. A modified Delphi study by Ferrer et al. [ 16 ] further emphasised the need for novice orthodontists to understand key subtopics within OTM, as outlined by the revised Bloom’s taxonomy [ 4 ]. Furthermore, the World Federation of Orthodontics (WFO) guidelines highlight the importance of establishing strong foundational biomechanical competence prior to advanced clinical training [ 2 ]. Collectively, these guidelines underscore the need for a solid foundation in orthodontic biomechanics during undergraduate education. However, learning the biomechanics of OTM presents several challenges for dental students and practitioners [ 17 , 18 ]. The subject matter is inherently complex, requiring the application of mechanical principles to biological systems. Students often struggle with foundational concepts such as force, torque, stress, and strain, which are crucial before advancing to more complex topics. Moreover, the biomechanics of OTM integrates knowledge from various disciplines, including physics, biology, anatomy, and materials science, making it challenging to understand fully [ 2 ]. In addition, OTM involves complex 3D structural changes, which demand strong spatial reasoning abilities; however, conventional teaching methods offer limited capacity to visualise these 3D dynamics effectively [ 48 ]. Consistent with these limitations, an e-Delphi study conducted by Kaggal Lakshmana Rao et al. [ 27 ] revealed that current teaching methods lack effective visualisation, leading to difficulty in understanding orthodontic biomechanics and dental structure responses. Similarly, Poblete et al.’s study [ 42 ] revealed that 70% of students and instructors viewed OTM biomechanics as an area where 3D digital resources would be particularly valuable. Given these challenges and the recognised need for improved visualisation, improving instructional strategies for teaching orthodontic biomechanics has become increasingly important. Numerous studies have explored the use of AR or virtual reality (VR) to provide interactive learning experiences and enhance clinical preparation [ 10 , 24 , 26 , 28 ]. Although simulation-based teaching shows promising educational benefits in orthodontics [ 6 , 39 ], few studies have applied these technologies specifically to orthodontic biomechanics, indicating a gap in current educational resources [ 44 ]. Therefore, this study introduces the WAR Orthobiomechanics system, an AR-enhanced learning tool designed to address the visualisation challenges inherent in teaching orthodontic biomechanics. AR enables the presentation of complex 3D movements and interactive force systems in an intuitive and immersive manner, offering dental students opportunities to manipulate tooth movement scenarios [ 14 ]. By allowing learners to actively engage with virtual models, AR provides flexible, remote access to learning resources. To evaluate students’ initial experiences with this newly developed system, the study adopts the reaction level (Level 1) of the Kirkpatrick Model. As the foundational tier of the framework, the reaction level focuses on learners’ immediate perceptions, satisfaction, and acceptance, which are critical indicators of whether an instructional innovation is usable, engaging, and aligned with educational needs [ 30 ]. Assessing student reactions is especially appropriate for a preliminary study, as early perceptions often influence willingness to continue using the system and inform subsequent refinement [ 30 ]. In line with the focus on students’ initial reactions, this study assesses the usability and acceptability of the WAR Orthobiomechanics system. Usability is a core component of learner experience and influences the extent to which students can interact effectively with digital educational tools [ 29 , 38 ]. Acceptability further reflects students’ perceptions of usefulness and their willingness to engage with the system [ 45 ], both of which are essential when introducing a new instructional technology. To capture these complementary aspects, the study employs two widely established measures: the System Usability Scale (SUS) for perceived usability and the Technology Acceptance Model (TAM) for perceived usefulness (PU) and perceived ease of use (PEOU). Using both instruments provides a comprehensive understanding of how students experience the system during its initial implementation. Materials and methods All participants were required to sign a consent form, confirming their understanding of the research objectives and tasks. Ethical approval was granted by the Human Research Ethics Committee (HREC) of Universiti Sains Malaysia (USM/JEPeM/21110756). Content design of WAR Orthobiomechanics The content of the WAR Orthobiomechanics system was designed using the Nominal Group Technique (NGT), which identified key topics considered most beneficial for virtual learning in orthodontic biomechanics [ 44 ]. Orthodontic experts prioritised physiology of tooth movement, tooth movement (definition, types, theory, and indications), and force systems [ 44 ]. Accordingly, the system content was organised into five sections: types of tooth movement, tissue responses to orthodontics, orthodontic wires, anchorage, and simulated tooth movement. Figure 1 outlines the content development workflow. Fig. 1. Open in a new tab WAR Orthobiomechanics development process. *AR: Augmented reality; PDL: periodontal ligament; NGT: Nominal Group Technique; SUS: System Usability Scale; TAM: Technology Acceptance Model Development of the website Responsive web platform The platform uses responsive web design (RWD) to ensure consistent access across laptops, tablets and smartphones. The layout automatically adjusted to screen size to optimise usability and visual clarity. The system integrates text, animations and AR modules into a single learning interface (Fig. 2 ). Fig. 2. Open in a new tab a Elements in the website b Snapshots of the marker-based AR system Marker-based AR module Data processing and 3D model preparation A cone-beam computer tomography (CBCT) dataset was segmented using 3D Slicer, and tooth models were exported in STL format for use in Unity. AR tracking and visualisation Vuforia was used to generate an image-target marker and enable six-degree-of-freedom tracking for real-time visualisation of the dentition model. Tooth movement simulation A physics-based engine written in C# enabled the simulation of tipping, bodily movement, and torque (Fig. 2 ). Simulations were checked against clinical records and laboratory measurements to ensure plausibility. Mobile implementation The Android version was optimised using Unity’s IL2CPP backend to improve execution speed. Interface scaling and touch interactions were adjusted to support various screen resolutions. The final application was compressed and obfuscated to ensure stable performance on a wide range of devices. Periodontal ligament stress analysis module The periodontal ligament (PDL) stress analysis module was developed using finite element analysis (FEA) to illustrate stress and strain responses under orthodontic loading. COMSOL Multiphysics was used to model the PDL as a deformable layer between the root and alveolar bone. Orthodontic force was applied digitally to simulate stress distribution and displacement patterns. Output visualisations included von Mises stress maps and displacement fields. Perceived usability and acceptability evaluation of WAR Orthobiomechanics Study design This study employed a cross-sectional design, involving students from three dental schools in Malaysia: Universiti Sains Malaysia (USM), Penang International Dental College (PIDC), and Universiti Sains Islam Malaysia (USIM). The inclusion criteria were as follows: 1) Undergraduate dental students in Years 3 to 5 who had attended the orthodontic biomechanics lecture at their respective institutions, and 2) Students who have access to the WAR Orthobiomechanics system using a compatible device. Students who did not complete the consent form were excluded from the study. A purposive sampling method was used. Perceived usability evaluation The SUS was employed to assess the perceived usability of the WAR Orthobiomechanics system. SUS is a widely adopted and validated instrument for evaluating digital systems across diverse contexts [ 7 ] and remains reliable even with small sample sizes [ 22 , 32 , 49 , 51 ]. The SUS consists of 10 statements presented in an alternating positive and negative format. Participants rate each item on a five-point scale, ranging from “Strongly Disagree” to “Strongly Agree” [ 51 ]. In this study, the questionnaire included five demographic questions and 10 SUS items, with “WAR Orthobiomechanics” substituted for the generic term “system.” Scores were calculated following the standard SUS scoring procedure, producing a total score ranging from 0 to 100. Usability levels were interpreted using the curved grading scale (CGS) [ 33 ] (Table 1 ) and the adjective rating scheme proposed by Bangor et al. [ 5 ]. Table 1. The curved grading scale for SUS interpretation [ 33 ] Range of SUS Score Grading Percentile Range 84.1–100 A + 96–100 80.8–84.0 A 90–95 78.9–80.7 A − 85–89 77.2–78.8 B + 80–84 74.1–77.1 B 70–79 72.6–74.0 B − 65–69 71.1–72.5 C + 60–64 65.0–71.0 C 41–59 62.7–64.9 C − 35–40 51.7–62.6 D 15–34 0.0–51.6 F 0–14 Open in a new tab Acceptability assessment Acceptability of the WAR Orthobiomechanics system was evaluated using the TAM, which explains technology adoption through PU, PEOU, attitude toward use (AT), and behavioural intention (BI) [ 12 ]. TAM has become a widely accepted model for evaluating the acceptability of information systems due to its versatility, simplicity, and robustness [ 1 ]. Research further supports TAM and its variations as a significant scientific framework and a reliable model for assessing the adoption of new technologies in educational settings [ 21 ]. In this study, a TAM questionnaire developed by Ghani et al. [ 19 ] was selected because it is validated for mobile learning contexts. The questionnaire consisted of five demographic items and 20 TAM items across four dimensions (PU: 5 items, PEOU: 6 items, AT: 5 items, BI: 4 items), each rated on a 7-point Likert scale. An open-ended question captured additional user feedback. The final TAM score (0–100) was computed using the established scoring procedure described by Pal and Vanijja [ 41 ], with higher scores indicating greater acceptability. Data collection To ensure a meaningful learning experience, participants were instructed to download the AR application and view a demonstration video before use. Each student had 90 min to engage with the system, after which SUS and TAM questionnaires were administered via Google Forms. Each questionnaire should be completed within 15 min. Participation was voluntary and confidential. Statistical analyses Cronbach’s Alpha coefficient was calculated to ensure internal consistency for both SUS and TAM items. Descriptive statistics, including mean, standard deviation (SD), and percentages, were computed to summarise participant characteristics and overall usability and acceptability scores. Independent t -tests were conducted to explore differences by gender, institution, and educational level. USM was analysed separately due to its direct involvement in system development, which may have influenced students’ exposure and familiarity. In contrast, USIM and PIDC were grouped as external institutions with similar evaluation conditions. Students were categorised into preclinical (Year 3) and clinical stages (Years 4 & 5) based on their educational level. One-way ANOVA was used to investigate differences among various device types. To assess the concurrent validity between the SUS and TAM, as well as the internal associations within the TAM framework, Pearson’s correlation analyses were performed. All analyses were conducted using IBM SPSS Statistics version 29.0, and statistical significance was set at p < 0.05. Result Participant demographics Out of 72 eligible students, 56 participated in this study (response rate: 77.8%). All participants in this study successfully followed instructions, engaged with the required learning content, and completed both the SUS and TAM questionnaires. Table 2 provides a comprehensive overview of the respondents’ demographics. Among the participants, 34 (60.7%) were female, and 22 (39.3%) were male, with an even split between preclinical (Year 3) and clinical (Years 4 and 5) students. The participant group included 25 students (44.6%) from USM and 31 (55.4%) from other institutions. During the usability test, 27 participants (48.2%) used a laptop, 15 (26.8%) used a mobile phone, and 14 (25.0%) used an iPad or tablet. Table 2. Descriptive statistics of the respondents’ characteristics Variable Frequency ( n = 56) Percentage (%) Gender Male 22 39.3 Female 34 60.7 Institution USM 25 44.6 Others (USIM & PIDC) 31 55.4 Educational level Preclinical phase 28 50.0 Clinical phase 28 50.0 Types of devices use for marker-based AR system Laptop 27 48.2 Mobile phone 15 26.8 Ipad/Tablet 14 25.0 Open in a new tab Reliability and validity of SUS and TAM The SUS demonstrated acceptable internal consistency (Cronbach’s α = 0.715), exceeding Nunnally’s [ 40 ] recommended threshold of 0.70. Reliability was high across all TAM constructs (Table 3 ). Table 3. Mean ± SD and internal consistency of TAM Variables Item Mean SD Cronbach’s Alpha PU PU1 5.83 0.90 0.949 PU2 5.61 0.98 PU3 5.78 1.05 PU4 6.09 0.78 PU5 5.96 0.97 PU composite mean 5.85 0.86 PEOU PEOU1 5.37 1.20 0.926 PEOU2 5.57 1.05 PEOU3 5.52 1.05 PEOU4 5.35 1.23 PEOU5 5.57 1.11 PEOU6 5.76 0.95 PEOU composite mean 5.52 0.94 AT AT1 6.09 0.84 0.913 AT2 5.87 0.91 AT3 5.85 1.01 AT4 5.57 1.09 AT5 5.87 0.89 AT composite mean 5.85 0.82 BI BI1 5.63 1.06 0.862 BI2 4.91 1.17 BI3 5.15 1.15 BI4 4.96 1.21 BI composite mean 5.16 0.97 Open in a new tab PU perceived usefulness, PEOU perceived ease of use, AT attitude toward use, BI behavioural intention Pearson correlation analyses (Table 4 ) showed a moderate positive correlation between SUS and the TAM score ( r = 0.493, p < 0.01), indicating concurrent validity between perceived usability and technology acceptance. SUS also demonstrated significant positive correlations with all TAM constructs, including PU ( r = 0.486, p < 0.01), PEOU ( r = 0.456, p < 0.01), AT ( r = 0.444, p < 0.01), and BI ( r = 0.346, p < 0.05). TAM constructs were strongly intercorrelated ( r = 0.833–0.914, p < 0.01), consistent with the theoretical expectations of the TAM model. Table 4. Pearson’s correlation matrix for all measurements SUS TAM PU PEOU AT BI SUS 1 TAM 0.493** 1 PU 0.486** 0.901** 1 PEOU 0.456** 0.891** 0.720** 1 AT 0.444** 0.914** 0.734** 0.697** 1 BI 0.346* 0.833** 0.624** 0.658** 0.720** 1 Open in a new tab * p < 0.05; ** p < 0.01 (2-tailed) SUS and TAM score The mean SUS score was 68.97 ± 12.87 (Table 5 ), corresponding to a grade of C on the CGS [ 33 ]. This score suggests moderate to good usability [ 5 ]. Table 5. SUS and TAM score according to CGS n Minimum Range Maximum Range Mean SD Range SUS 56 47.50 F 95.00 A + 68.97 12.87 C TAM 56 48.34 - 100.00 - 76.92 13.22 - Open in a new tab The TAM score was 76.92 ± 13.22 (Table 5 ), indicating that the tool is useful and easy to use for mobile learning platforms [ 3 ]. The mean, SD and internal consistency of TAM were presented in Table 3 . PU items produced a composite mean of 5.85. The item “Using WAR Orthobiomechanics will enhance the effectiveness of learning” received the highest score of 6.09, suggesting that the system may support students in improving their understanding of biomechanics. PEOU showed a composite mean of 5.52. AT scores averaged 5.85, with “Studying using WAR Orthobiomechanics is a good idea” rated highest (6.09). BI items had a composite mean of 5.16, with the items “I intend to use WAR Orthobiomechanics heavily” and “I intend to repetitively use WAR Orthobiomechanics as often as possible” receiving slightly lower scores of 4.91 and 4.96, respectively. SUS and TAM scores were generally comparable across gender, institution, educational level, and device type, with no statistically significant differences observed for overall TAM scores. A significant difference was detected only in SUS scores between preclinical and clinical students ( p < 0.05) (Table 6 ). Table 6. SUS and TAM score by demographic characteristics of participants Variables SUS Mean ± SD TAM Mean ± SD PU Mean ± SD PEOU Mean ± SD AT Mean ± SD BI Mean ± SD Gender Male ( n = 22) 68.47 ± 14.02 75.48 ± 14.77 5.78 ± 0.96 5.52 ± 0.94 5.68 ± 0.94 5.04 ± 1.07 Female ( n = 34) 69.29 ± 12.34 77.84 ± 12.32 5.90 ± 0.80 5.52 ± 0.96 5.96 ± 0.73 5.24 ± 0.91 t 0.296 1.102 2.100 0.013 1.563 3.189 p 0.589 0.300 0.154 0.909 0.218 0.081 Institution USM ( n = 25) 71.25 ± 12.18 76.56 ± 14.58 5.83 ± 0.86 5.46 ± 1.08 5.82 ± 0.89 5.21 ± 1.02 Others ( n = 31) 67.21 ± 13.35 77.19 ± 12.37 5.87 ± 0.7 5.57 ± 0.84 5.87 ± 0.77 5.13 ± 0.94 t 0.954 0.306 0.005 1.023 0.152 0.012 p 0.335 0.583 0.946 0.317 0.699 0.915 Educational level Preclinical phase ( n = 28) 71.25 ± 10.33 78.07 ± 12.19 5.79 ± 0.73 5.56 ± 0.96 5.93 ± 0.78 5.43 ± 0.87 Clinical phase ( n = 28) 67.50 ± 14.26 76.18 ± 14.01 5.89 ± 0.94 5.50 ± 0.95 5.79 ± 0.85 4.99 ± 1.00 t 0.963 0.470 −0.398 0.193 0.564 1.525 p 0.022* 0.460 0.327 0.986 0.361 0.583 Types of devices Laptop ( n = 27) 68.33 ± 13.92 75.40 ± 14.46 5.80 ± 0.92 5.42 ± 1.03 5.73 ± 0.94 5.08 ± 0.96 Mobile phone ( n = 15) 67.92 ± 11.77 77.52 ± 12.40 5.83 ± 0.72 5.58 ± 0.54 5.97 ± 0.53 5.13 ± 1.18 iPad/Tablet ( n = 14) 73.21 ± 11.15 79.85 ± 11.64 6.00 ± 0.91 5.70 ± 0.87 6.00 ± 0.83 5.40 ± 0.72 F 0.442 0.406 0.189 0.344 0.558 0.380 p 0.645 0.669 0.828 0.711 0.577 0.686 Open in a new tab * p < 0.05 Discussion This study evaluated dental students’ perceptions of the WAR Orthobiomechanics system using established measures of usability and acceptability. Overall, the findings suggest that the system demonstrated moderate-to-good usability (SUS = 68.97) and high levels of acceptability (TAM = 76.92), suggesting its potential as a supplementary learning tool for orthodontic biomechanics. Key findings in context The SUS score (68.97 ± 12.87) indicates that the system’s usability has reached an acceptable level, which falls within the range (63.38—90.33) observed in comparable studies for dental AR applications [ 28 , 31 , 47 , 52 ]. However, the wide range of SD indicates that some students encounter difficulties in application, as shown in Table 5 , where the lowest SUS score reached 47.50. Prior research has shown that SUS scores for educational AR tools may be lower during early adoption due to novelty effects, limited prior exposure, and increased cognitive load during complex visualisation tasks [ 13 , 15 , 36 ]. The TAM results support students’ perceived acceptability, with high scores for PU (5.85) and positive attitudes toward the 3D simulation system (5.85). Previous studies have demonstrated that students show stronger engagement with 3D models compared to traditional teaching methods [ 23 , 35 , 37 ]. The PU and AT items may indicate that students perceived the system as helpful for visualising 3D tooth movement, a key component of orthodontic biomechanics. PEOU was also rated positively (mean = 5.52), indicating that most students were able to navigate the system and manipulate AR content without major difficulties. PEOU is consistently rated positively by students in digital and AR learning environments, indicating that users generally find these systems navigable and manageable [ 20 , 46 ]. The slightly lower BI score (mean = 5.16) remains positive overall and may reflect practical constraints such as device compatibility or the need for multiple devices during use. The moderate positive correlation between SUS and the overall TAM score supports the concurrent validity of perceived usability and technology acceptance (Table 4 ). Significant correlations between SUS and each TAM construct indicate that students who perceived higher usability also tended to report more favourable acceptance, consistent with previous research on AR and e-learning systems [ 9 , 41 , 43 ]. Practical implications In addition to the overall positive usability and acceptability ratings, several subgroup patterns provide practical insights into how the WAR Orthobiomechanics system can be best integrated into orthodontic curricula. Preclinical students scored higher than clinical students on both SUS (71.25 vs. 67.50) and TAM (78.07 vs. 76.18) scores, with a large effect size observed for usability (d = 0.89) (Table 6 ). This pattern aligns with cognitive load theory, which posits that novices benefit more from structured visualisation tools than advanced learners [ 10 ]. In contrast, clinical students with greater prior knowledge may need more clinically contextualised scenarios to maintain their engagement. Additionally, none of the demographic variables, including gender, institution, educational level, or device type, had a significant effect on SUS and TAM scores, underscoring the system’s broad usability and acceptability across learner groups. Therefore, WAR Orthobiomechanics may be particularly valuable as a supplementary tool in preclinical teaching, where its 3D visualisation functions can help bridge theoretical biomechanics and early clinical understanding. For instructors, structured faculty development workshops may enhance effective integration, as educator familiarity with these tools is known to improve student outcomes [ 39 ]. For more advanced learners, incorporating clinical case simulations may further strengthen the system’s educational relevance. System limitations and future improvements Several limitations were identified based on participant feedback, which could be addressed in future iterations of the system (Table 7 ). First, iPhone users faced limited access to the 3D module as the AR application is Android-based. Some students found it challenging to use the application, as it requires at least two devices for full functionality, which could be a deterrent. Additionally, the AR system’s performance was occasionally hindered by technical issues such as lagging, and difficulties were reported with the zoom scale functionality, making it less efficient to zoom in and out. To maximise its educational impact, technical optimisations such as improving cross-platform compatibility (especially for iOS), reducing lag, and implementing intuitive interactions like pinch-to-zoom are essential for enhancing accessibility and usability. Table 7. User responses to WAR Orthobiomechanics Positive feedback Areas need improvement This application is indeed very interesting as it helps to enhance my understanding on the tooth movement via the provided 3D module. Somehow, I find it a bit difficult to use it on-the-go as we need at least two devices to benefit this web-based application. Very good alternative for learning, especially for things that is hard to just imagine or try to understand it with words only. For me, AR require better smartphone to view and use the AR techs smoothly, otherwise it can become lagging and stuck sometimes. The idea of web-based application is good but using AR is kind of awkward because the experience could be the deal breaker for me. It is really a commendable effort and would really be useful for dental student. I think it’ll be better to zoom in and out and rotating the image by pinching our fingers rather than using the scaling. Quite easy and interesting to use future! The zoom scale feature for the app is not working which causes model scale to be small I hope the AR also can be IOS-friendly. Open in a new tab To further validate and enhance the WAR Orthobiomechanics system, longitudinal studies should examine whether sustained usage improves academic performance or clinical skill development over time based on the Kirkpatrick model (Level 2). Limitations and future work This study has several limitations. The primary limitation is the small sample size, which restricts the generalizability of the findings to a broader population. Future research with a larger sample size will be conducted to confirm these findings. Additionally, the research model did not account for external factors such as students’ self-efficacy, students’ capacity and experience with AR, system accessibility, and quality. Future studies should incorporate a structural equation model (SEM) to explore the impact of these external factors on PU, PEOU, AT, and BI. Furthermore, this study primarily focused on evaluating the perceived usability and acceptability of the system. However, it did not include objective measurements to assess the system’s impact on actual learning outcomes. Future research should include a comprehensive validation process to evaluate the effectiveness of the system in enhancing learning when compared to traditional learning methods. Conclusion The proposed WAR Orthobiomechanics system demonstrated acceptable usability and positive levels of acceptability, as indicated by SUS and TAM scores. These findings indicate that the system is feasible as a supplementary learning tool in dental education. Student feedback will guide further refinement of the interface, with emphasis on improving system performance and enhancing cross-platform stability. Future studies should address the current limitations and investigate the system’s long-term educational impact to better determine its role in orthodontic teaching. Acknowledgements The authors would like to acknowledge all participants who willingly participated in this study. The authors also would like to acknowledge the Ministry of Higher Education Malaysia through the Dental and Simulation and Virtual Learning Research Excellence Consortium (KKP Programme) JPT(BKPI)1000/016/018/25 Jld. 2(2). Abbreviations 3D Three-dimensional ADEA American Dental Education Association AR Augmented Reality AT Attitude Toward Use BI Behavioural Intention CBCT Cone-Beam Computed Tomography CGS Curve Grading Scale FEA Finite Element Analysis HREC Human Research Ethics Committee NGT Nominal Group Technique OTM Orthodontic Tooth Movement PDL Periodontal ligament PEOU Perceived Ease of Use PIDC Penang International Dental College PU Perceived Usefulness RWD Responsive Web Design SD Standard Deviation SEM Structural Equation Model SUS System Usability Scale TAM Technology Acceptance Model USIM Universiti Sains Islam Malaysia USM Universiti Sains Malaysia VR Virtual Reality WAR Orthobiomechanics Web-based Augmented Reality System for Orthodontic biomechanics WFO World Federation of Orthodontics Authors’ contributions L.X: Writing-original draft; Writing—Review and Editing; Investigation; Methodology; Formal analysis; Data curation; Visualization. T.F.S.M: Software. S.S.N.S: Investigation. G.K.L.R: Investigation; Writing—Review and Editing. F.R: Software; Writing—Review and Editing. H.H: Methodology; Writing—Review and Editing. Z.I: Software. N.Z: Software. N.M: Conceptualization; Funding Acquisition; Supervision; Project Administration. Funding This study was supported by the KKP Programme, project NO. JPT(BKPI)1000/016/018/25 Jld. 2(2). Data availability The data used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Each participant was provided with a consent form prior to participation in this study, authorising the use of their data for research purposes. Participants have provided their written informed consent to participate in this study. All participant data were anonymised to maintain confidentiality. Ethical approval was granted by the Human Research Ethics Committee (HREC) of Universiti Sains Malaysia (USM/JEPeM/21110756). This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Al-Emran M, Mezhuyev V, Kamaludin A. Technology acceptance model in M-learning context: a systematic review. Comput Educ. 2018;125:389–412. [ Google Scholar ] 2. Athanasiou AE, Darendeliler MA, Eliades T, Hägg U, Larson BE, Pirttiniemi P, et al. World Federation of Orthodontists (WFO) guidelines for postgraduate orthodontic education. World J Orthod. 2009;10(2):153–66. [ PubMed ] 3. Al-Rahmi AM, Al-Rahmi WM, Alturki U, Aldraiweesh A, Almutairy S, Al-Adwan AS. Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability. 2021;13(14):7893. [ Google Scholar ] 4. Anderson LW, Krathwohl DR. A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives: complete edition. New York: Addison Wesley Longman, Inc.; 2001. 5. Bangor A. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4(3):114–23. [ Google Scholar ] 6. Baxmann M, Baráth Z, Kárpáti K. Efficacy of typodont and simulation training in orthodontic education: a systematic review. BMC Med Educ. 2024;24(1):1443. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Brooke J. SUS - a quick and dirty usability scale. In: Jordan PW, Thomas B, Weerdmeester BA, McClelland IL, editors. Usability evaluation in industry, vol 189. London: Taylor and Francis; 1996. p. 189–194. 8. Burstone CJ, Choy K. The biomechanical foundation of clinical orthodontics. Chiago: Quintessence Publishing Co, Inc.; 2015. 9. Cheah WH, Jusoh NM, Aung MMT, Ab Ghani A, Rebuan HMA. Mobile technology in medicine: development and validation of an adapted system usability scale (SUS) questionnaire and modified technology acceptance model (TAM) to evaluate user experience and acceptability of a mobile application in MRI safety screening. Indian J Radiol Imaging. 2023;33(01):036–45. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Chen D, Liu X, Liu Y, Wang X, Zheng J, Wu L. Virtual reality used in undergraduate orthodontic education. Eur J Dent Educ. 2023;00:1–9. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Curriculum guidelines for orthodontics. AADS Section on Orthodontics and the Council on Orthodontic Education of the American Association of Orthodontists. J Dent Educ. 1993;57(9):707–10. https://pubmed.ncbi.nlm.nih.gov/8408905 . [ PubMed ] 12. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci. 1989;35(8):982–1003. [ Google Scholar ] 13. Dutta R, Mantri A, Singh G. Evaluating system usability of mobile augmented reality application for teaching Karnaugh-Maps. Smart Learn Environ. 2022;9(1):6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Elendu C, Amaechi DC, Okatta AU, Amaechi EC, Elendu TC, Ezeh CP, et al. The impact of simulation-based training in medical education: a review. Medicine. 2024;103(27):e38813. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Escalada-Hernandez P, Soto-Ruiz N, Ballesteros-Egüés T, Larrayoz-Jiménez A, Martín-Rodríguez LS. Usability and user expectations of a HoloLens-based augmented reality application for learning clinical technical skills. Virtual Reality. 2024;28(2):102. [ Google Scholar ] 16. Ferrer VLS, Van Ness C, Iwasaki LR, Nickel JC, Venugopalan SR, Gadbury‐Amyot CC. Expert consensus on didactic clinical skills development for orthodontic curricula. J Dent Educ. 2021;85(6):747–55. [ DOI ] [ PubMed ] [ Google Scholar ] 17. Fiorelli G, Linhares GB, Sakima MT, Martins RP, Oliveira WD. An interview with Giorgio Fiorelli. Dent Press J Orthod. 2018;23(5):24–38. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Firth FA, Farrar R, Farella M. Investigating orthodontic tooth movement: challenges and future directions. J R Soc N Z. 2020;50(1):67–79. [ Google Scholar ] 19. Ghani MT, Hamzah M, Ramli S, Ab W, Daud AW, Romli TR, et al. A questionnaire-based approach on technology acceptance model for mobile digital game-based learning. Journal of Global Business and Social Entrepreneurship (GBSE). 2019;5(14):11–21. 20. Ghobadi M, Shirowzhan S, Ghiai MM, Mohammad Ebrahimzadeh F, Tahmasebinia F. Augmented reality applications in education and examining key factors affecting the users’ behaviors. Educ Sci. 2022;13(1):10. [ Google Scholar ] 21. Granić A, Marangunić N. Technology acceptance model in educational context: a systematic literature review. Br J Educ Technol. 2019;50(5):2572–93. [ Google Scholar ] 22. Hajesmaeel-Gohari S, Khordastan F, Fatehi F, Samzadeh H, Bahaadinbeigy K. The most used questionnaires for evaluating satisfaction, usability, acceptance, and quality outcomes of mobile health. BMC Med Inform Decis Mak. 2022;22(1):22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3‐dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022;26(3):435–45. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Huang Y, Cheng X, Chan U, Zheng L, Hu Y, Sun Y, et al. Virtual reality approach for orthodontic education at School of Stomatology, Jinan University. J Dent Educ. 2022;86(8):1025–35. [ DOI ] [ PubMed ] [ Google Scholar ] 25. Jacob HB, Azeredo RG, Spreafico CS. Biomechanical considerations for tooth movement and strategies to avoid undesirable side effects. Semin Orthod 2024;30:557–71. 10.1053/j.sodo.2024.02.006. 26. Joda T, Gallucci GO, Wismeijer D, Zitzmann NU. Augmented and virtual reality in dental medicine: a systematic review. Comput Biol Med. 2019;108:93–100. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Kaggal Lakshmana Rao G, P Iskandar YH, Mokhtar N. Developing consensus in identifying challenges of undergraduate orthodontic education in Malaysian public universities using e‐Delphi. Eur J Dent Educ. 2020;24(3):590–600. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Kihara T, Keller A, Ogawa T, Armand M, Martin-Gomez A. Evaluating the feasibility of using augmented reality for tooth preparation. J Dent. 2024;148:105217. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Kim H, Lee SH, Cho NB, You H, Choi T, Kim J. User-dependent usability and feasibility of a swallowing training mHealth app for older adults: mixed methods pilot study. JMIR Mhealth Uhealth. 2020;8(7):e19585. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Kirkpatrick DL, Kirkpatrick JD. Implementing the four levels: a practical guide for effective evaluation of training programs. San Francisco: Berrett-Koehler Publishers; 2009. 31. Lemos M, Wolfart S, Rittich AB. Assessment and evaluation of a serious game for teaching factual knowledge in dental education. BMC Med Educ. 2023;23(1):521. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Lewis JR. Measuring perceived usability: the CSUQ, SUS, and UMUX. Int J Hum Comput Interact. 2018;34(12):1148–56. [ Google Scholar ] 33. Lewis JR, Sauro J. Can I leave this one out? The effect of dropping an item from the SUS. 2017;13(1). 34. Li Y, Zhan Q, Bao M, Yi J, Li Y. Biomechanical and biological responses of periodontium in orthodontic tooth movement: up-date in a new decade. Int J Oral Sci. 2021;13(1):20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Liu L, Qin J, Pan Y, Wang H, Lu X, Chu F, et al. Evaluating a preclinical orthodontic training using haptic-enhanced VR simulation system: a longitudinal study. BMC Med Educ. 2025;25(1):1–11. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Lu J, Schmidt M, Lee M, Huang R. Usability research in educational technology: a state-of-the-art systematic review. Educ Technol Res Dev. 2022;70(6):1951–92. [ Google Scholar ] 37. Mai HY, Mai HN, Lee DH. Computer-based 3D simulation method in dental occlusion education: student response and learning effect. Appl Sci. 2020;10(17):6073. [ Google Scholar ] 38. Maqbool B, Herold S. Potential effectiveness and efficiency issues in usability evaluation within digital health: a systematic literature review. J Syst Softw. 2024;208:111881. [ Google Scholar ] 39. Moussa R, Alghazaly A, Althagafi N, Eshky R, Borzangy S. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022;16(01):14–31. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Nunnally JC. Psychometric theory— 25 years ago and now. Educ Res. 1975;4(10):7–21. [ Google Scholar ] 41. Pal D, Vanijja V. Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Child Youth Serv Rev. 2020;119:105535. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Poblete P, McAleer S, Mason AG. 3D technology development and dental education: what topics are best suited for 3D learning resources? Dent J. 2020;8(3):3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Rêgo TJRD, Lemos JVM, Matos APL, Caetano CFF, Dantas TS, Sousa FB, et al. Development and professional validation of an App to support oral cancer screening. Braz Dent J. 2022;33(6):44–55. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Ridzuan F, Rao GKL, Wahab RMA, Dasor MM, Mokhtar N. Enabling virtual learning for biomechanics of tooth movement: a modified nominal group technique. Dent J Basel. 2023;11(2):53. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Shetty SR, Murray C, Kawas SA, Jaser S, Talaat W, Madi M, et al. Acceptability of fully guided virtual implant planning software among dental undergraduate students. BMC Oral Health. 2023;23(1):336. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Shyr WJ, Wei BL, Liang YC. Evaluating students’ acceptance intention of augmented reality in automation systems using the technology acceptance model. Sustainability. 2024;16(5):2015. [ Google Scholar ] 47. Silva A, Silva M, Silva NN, Moretti E, Lima F, Neto O, et al. Anatomy alive: aplicativo móvel para facilitar o ensino-aprendizagem de anatomia de cabeça e pescoço aplicada à Odontologia. Rev ABENO. 2024;24:2201. [ Google Scholar ] 48. Staley RN, Reske NT. Essentials of orthodontics diagnosis and treatment. 1st ed. Iowa: Wiley; 2011. 49. Tullis TS, Stetson JN. A comparison of questionnaires for assessing website usability. InUsability professional association conference 2004;1:1–12. https://www.researchgate.net/publication/228609327_A_Comparison_of_Questionnaires_for_Assessing_Website_Usability . 50. Viglianisi G, Polizzi A, Lombardi T, Amato M, Grippaudo C, Isola G. Biomechanical and biological multidisciplinary strategies in the orthodontic treatment of patients with periodontal diseases: a review of the literature. Bioengineering. 2025;12(1):49. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Vlachogianni P, Tselios N. Perceived usability evaluation of educational technology using the System Usability Scale (SUS): a systematic review. J Res Technol Educ. 2022;54(3):392–409. [ Google Scholar ] 52. Zorzal ER, Paulo SF, Rodrigues P, Mendes JJ, Lopes DS. An immersive educational tool for dental implant placement: a study on user acceptance. Int J Med Inform. 2021;146:104342. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data used and/or analysed during the current study are available from the corresponding author on reasonable request. Articles from BMC Oral Health are provided here courtesy of BMC ACTIONS View on publisher site PDF (1.5 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top