Designing a serious-game-inspired digital laboratory for biomechatronics: a pilot study in engineering education

Abstract

Introduction:

Virtual laboratories (vLabs) are increasingly used in engineering education to support preparation for complex experimental work. We report the implementation and exploratory evaluation of a serious-game-inspired vLab that mimics a biomechatronics device, the MyoRobot, within a Master's-level course.

Methods:

The Unity-based desktop simulation combines a realistic 3D laboratory, an interactive technical anatomy model, and a pipetting and operation workflow that links virtual actions to plausible force-recording outcomes. In an initial cohort (N = 8), students prepared either using a written manual (n = 4) or primarily the vLab (n = 4). Open-ended questionnaires administered before and after the physical lab assessed perceived preparedness, confidence, engagement, and overall user experience.

Results:

vLab users reported improved conceptual orientation and procedural confidence, along with greater independence in handling sensitive equipment. However, they also noted a higher perceived time investment, a reduced novelty effect during the physical lab, and a desire for more specific feedback.

Discussion:

We interpret these findings as context-specific insights from a pilot study and derive design implications for realistic vLabs that aim to balance guidance, workload, and authenticity in engineering education.

1 Introduction

Arguably, a formidable challenge in STEM (science, technology, engineering, and mathematics) education is translating theoretical knowledge into practical applications (Wolf, 2010). In engineering education specifically, students are often expected to operate complex laboratory equipment, interpret experimental data, and troubleshoot systems while simultaneously understanding the underlying theory. It is well established that hands-on experimentation fosters higher-order cognitive skills and facilitates the development of directed problem-solving strategies (Jones et al., 2019). However, the requisite abundance and resourcefulness of practical classes often face impediments, including the following:

Figure 1

Nonetheless, the pedagogical design and technical implementation of such concept-based instructional vLabs are demanding. A longstanding challenge identified years ago concerns the need for vLabs to faithfully replicate authentic experimental tasks in a simulated environment that accurately mirrors the real lab setting (Woodfield et al., 2005). While pedagogical implementation and instructional design have markedly improved through the study of learning behavior, technical implementation has struggled to keep pace. This lag is particularly concerning, given that the appeal and user-friendliness of vLabs are pivotal for student acceptance (Christopoulos et al., 2022; Hao et al., 2021). To provide an implementation-focused proof of concept for a realistic representation of a complex STEM-based laboratory in an engineering education context, we initiated a virtual transformation of our conventional muscle cellular biomechatronics course. The vLab was developed as a desktop-based 3D simulation and is operated on standard laptop/desktop computers using keyboard and mouse interaction. The biomechatronics course is part of a Master’s-level Life Science Engineering/Biotechnology curriculum and introduces students to an advanced biomechatronics research device—the MyoRobot (Haug et al., 2019).

Our objective was to elevate realism in both graphical representation and user interaction while faithfully replicating the workflow experience, including typical challenges and failures encountered in a real lab. The central focus of our pilot study was the MyoRobot (Haug et al., 2019), our primary opto-biomechatronics research device. While the MyoRobot is thoroughly explained in its original publications (Haug et al., 2018, 2019), we briefly summarize its working principle here to contextualize the educational simulation.

The core of the system consists of two pins connected to a force transducer and a voice coil actuator, respectively (Figure 2, left). The laboratory workflow involves preparing a rack with a range of bioactive solutions, mounting a single muscle fiber between the pins, and executing protocols in which the fiber is immersed in different wells of the rack containing distinct bioactive solutions. For example, immersion in calcium-rich or relaxing solutions enables students to explore force generation and relaxation and to derive parameters such as calcium sensitivity or passive restoration forces (by actuating a length controller). In our vLab, we digitally reproduce this workflow, enabling engineering students to develop a conceptual understanding of the system and its operation before entering the physical laboratory.

Figure 2

To transform this concept into the virtual space, we set up the device within a 3D laboratory environment accessible via any laptop or desktop computer using a mouse and keyboard. Here, learners interact with an immersive environment where they can operate the equipment in the form of a higher-education game. The rising demand for “serious games” among students (Christopoulos et al., 2022) is noteworthy, as these games, unlike interactive videos, allow individuals to engage with knowledge at their own pace through active interaction with a dynamic environment (Ravyse et al., 2017). Numerous studies support the effectiveness of such platforms in designing and developing situated, problem-based educational activities (Pellas et al., 2020, 2021). In this context, we aim to present the early outcomes of our virtual reality augmented learning (ViRAL) implementation as a case study in engineering education. Inspired by and tailored to meet the demands of a serious, intuitive higher-education game, we offer preliminary user feedback from the inaugural student cohort and discuss strategies to further enhance engaging realism in virtual teaching simulations. Accordingly, the present work is positioned as an implementation-focused case study: we describe the design and technical realization of a realistic desktop vLab and report an exploratory, qualitative evaluation within a single course cohort.

2 Materials and methods

It is essential to emphasize that this paper and our implementation are primarily focused on the technical and experiential aspects, aiming to explore how visual and operational realism in a vLab affects its acceptance among engineering students. While it is widely recognized that pedagogical design and storytelling play a critical role in shaping virtual education scenarios (Checa-Romero and Pascual Gómez, 2018; Thomas and Schneider, 2018), it is evident that academic teaching simulations have yet to match the photorealism, intuitive operation, and user guidance observed in entertainment sector games (Dalgarno et al., 2009). In that, we see major potential to close this gap and increase students’ acceptance of otherwise well-designed teaching simulations.

2.1 Educational setting

Our virtual laboratory experience draws inspiration from a traditional lab course offered to Master’s students in life science engineering (now biotechnology at FAU), a STEM discipline with substantial engineering content. The course introduces students to an advanced biomechatronics research device, the MyoRobot, and integrates muscle biomechanics performance, its manipulation, and the assessment of motility at the cellular level.

In the year of implementation, the practical class was limited to a maximum of eight students. All enrolled students participated in the study, and no student declined participation. The evaluation was integrated into the course preparation phase, and participation in the open-ended questionnaire was voluntary. All students provided feedback.

2.2 vLab modality and technical implementation

The vLab was developed as a desktop-based 3D simulation using the Unity engine. It was operated on standard laptop or desktop computers and operated via a keyboard and mouse, with no requirement for head-mounted display or augmented reality hardware. To balance realism with educational clarity, gamification elements were restricted to interaction and navigation mechanics, while the laboratory equipment and workflows were digitally twinned from the original MyoRobot computer assisted design (CAD) models, aligning with the recommendations of Pellas et al. (2021). The field of view responds to the movement of the mouse cursor, and basic navigation controls follow standard conventions (WASD for movement, SPACE to jump, C to crouch, and E to interact); sprinting was intentionally disabled, consistent with traditional laboratory practice. When a user approaches a “hit box” surrounding each respective MyoRobot (depicted as circles in Figure 3A) and initiates interaction, the relevant graphical user interface (GUI) is displayed, as shown in Figures 3B,C. Subsequent user interactions are performed through point-and-click (and scrolling), mirroring real laboratory procedures.

Figure 3

Inspired by the concept of intuitive quest guidance, our approach introduces a central quest-giver or virtual mentor who delivers instructions through dialog-based interactions. While the current task progression follows a linear storyline, our goal is to introduce diverse dialog options that enable users to engage in decision-making processes that impact the design of experiments. For example, when presented with the task to “set up the rack according to the provided table and run a calcium sensitivity recording,” users can either follow the instructions or choose to delay the task. In the former case, pipetting instructions are accessible via a table displayed in the user interface, mirroring the identical printed table used in a physical laboratory. The virtual mentor will also play a pivotal role in delivering adaptive feedback, particularly at higher levels, such as when interpreting experimental outcomes. Currently, user feedback and guidance on the storyline are conveyed through text elements in the GUI and by highlighting specific objects within the virtual environment.

To date, our vLab comprises two primary simulations: the MyoRobot Simulation & Pipetting experience and the MyoRobot Anatomy Model. Both simulations are accessible from a designated location within the virtual 3D laboratory (Figure 3A). User interaction activates a GUI for each implementation. The pipetting station used to set up the rack is displayed in Figure 3B, and the anatomy model for close-up investigation of the device is depicted in Figure 3C.

The MyoRobot Anatomy Model empowers the user to scrutinize the structural design of the biomechatronics robotics system, gaining insights into how its configuration facilitates the investigation of cellular muscle biomechanics. To achieve this, we developed a digital twin based on the original CAD drawings produced in our laboratory. This virtual replica offers unrestricted rotation (including zoom) for accessing all individual parts. Each elementary building block (e.g., linear motors, voice coils, lenses) comes with an accompanying info text explaining its function, accessible in the “info text” box (see Figure 3C) via left-clicking. Additional buttons enable users to highlight the beam path of the inbuilt optics system or visualize real-world motions such as voice coil actuation and lifting/lowering the sample in and out of the rack’s wells (Figure 4). To provide biomedical scientists and engineers with the necessary overview in this multi-layered model, all parts can be made visible or invisible by right-clicking. This functionality allows users to peer “behind the scenes” and scrutinize the device’s manufacturing concept in intricate detail, an opportunity not feasible in a real-world laboratory setting.

Figure 4

In line with real laboratory procedures, prior to conducting any experiments, the user is required to prepare all equipment. In the virtual environment, this involves detaching the rack from the MyoRobot (achieved by left-clicking) and placing it on a designated spot in the virtual wet lab (scenes switchable by hovering to the left and right corners of the screen). Within the virtual wet lab, users can choose from a range of pipettes. For authenticity, we incorporated the most common pipette volumes (volumes adjustable by a slider and mouse wheel within defined limits), each with corresponding limits. After attaching a correctly sized pipette tip (feedback on this is provided), users can hover over a highlighted Erlenmeyer flask to access the pipetting graphical user interface (GUI). This GUI displays a table with instructions on how to fill each well of the rack (18 wells in total) with the respective mixture of stock solutions (three stock solutions are provided, each distinguishable by label and color within the GUI). Upon completion (ideally, with each well filled precisely to 1 mL following the instructions), users must eject the pipette tip, and the rack is retransferred to the MyoRobot (Figure 5). Interacting with the device triggers the GUI for MyoRobot operation. Buttons and sliders allow users to initialize the system (where motors must be driven to a reference position) and execute an experiment protocol, such as a pCa–force curve that tests graded force development in response to different calcium levels. Running the experiment updates a real-time chart displaying the acquired data. The green force trace represents the ideal negative decadic logarithm of free calcium ion concentration (pCa)–force curve (assuming precise adherence to pipetting instructions), while the user’s actual data are presented in red. This curve depends on the user’s pipetting performance in the previous steps and may deviate from the ideal curve (Figure 5).

Figure 5

2.3 Adaptive feedback design

Adaptive feedback was implemented primarily within the pipetting simulation. Text-based notifications informed users about incorrect pipette selection, volume violations, missing initialization steps, or incorrect rack placement (Figure 6). When wells were filled precisely, visual indicators confirmed correct execution. This feedback aimed to prevent procedural misconceptions before students entered the physical laboratory.

Figure 6

2.4 Study design and procedure

The study was designed as an exploratory qualitative pilot study embedded within a single course cohort, with its primary contribution being the implementation and feasibility evaluation of the vLab in authentic teaching practice. Using an exploratory case design, the study involved a single course cohort (N = 8). Students were divided into two groups:

The questionnaire was distributed via the institutional learning management system. Upon submission, no identifying data (e.g., name, matriculation number, gender, IP address) was collected by the investigators. Responses remained fully anonymous.

2.5 Qualitative analysis

Given the small cohort size and exploratory character of this initial validation, responses were analyzed using an inductive thematic approach. No automated coding software was employed. All responses were manually reviewed by three individuals: the course coordinator, the head of the institute, and the software developer. This manual thematic clustering was conducted to generate descriptive, actionable design insights rather than to claim theory generation, or statistical generalizability.

Recurring themes were identified through iterative reading and comparison of responses. Statements reflecting similar content were grouped into overarching thematic categories (e.g., confidence, time effort, equipment handling, conceptual understanding). The frequency with which a theme appeared within each group was quantified descriptively (e.g., 3/4 students expressing a similar perception).

Due to the limited cohort size, no statistical testing was performed. The results are therefore interpreted as exploratory and context-bound rather than statistically generalizable.

2.6 Participants and evaluation of user experience

Our objective was to evaluate the effectiveness of our virtual training scenario for a student practical class in an engineering-oriented program. Our investigation aimed to assess how well participants could navigate these tasks based on their level of preparation and the chosen preparation modality (written manual vs. vLab).

To prepare participants, students were divided into two groups. One group relied on traditional preparation materials, including reading a 30-page operation manual and supplementary information on muscle physiology, while the other group was granted access to the vLab platform before the commencement of the practical course (both groups were given access to the vLab after the final evaluation to prepare equally well for the exam). To gauge user experience, we administered a questionnaire to both groups. Group A was assessed immediately before (i) and after (ii) the practical course. Group B, in contrast, was assessed after completing the vLab (i) and again after the practical course (ii).

3 Results

3.1 Participant overview

The entire course cohort (N = 8) participated in the study. Four students were assigned to manual-based preparation (Group A) and four to vLab-based preparation (Group B). All students completed both pre- and post-practical questionnaires (Figure 7A). No participant declined participation. The entire cohort received vLab access after the practical for exam preparation.

Figure 7

3.2 Pre-practical perceptions of preparedness

Before entering the physical laboratory, responses from Group A commonly expressed uncertainty regarding equipment handling and procedural confidence. Several students found the manual difficult to translate into concrete operational steps. The effort required to interpret this knowledge and the length of the course manual were reported as frustrating. Recurring themes included difficulty translating written instructions into operational steps and apprehension about working independently with sensitive equipment (Figure 7B).

In contrast, responses from Group B more often indicated increased conceptual orientation and greater confidence in navigating the anticipated workflow after interacting with the vLab. Several students reported that interacting with the vLab clarified the sequence of experimental steps and reduced anxiety about operating the MyoRobot (Figure 7B). The preparation phase (using the vLab) was perceived as overall intuitive and helpful, and students indicated a likelihood of revisiting it to refresh their knowledge, particularly when preparing for exams.

3.3 Post-practical reflections

After the practical course, group-specific emphases remained evident in the open-ended responses. Students from Group A reported requiring extended familiarization time with the equipment and occasionally relied on supervisor assistance during initial phases of experimentation (Figure 7B).

Group B students reported greater independence in handling equipment and described feeling more confident during experimental execution. Recurring themes included an improved understanding of complex system interactions, reduced fear of making procedural mistakes, and more responsible handling of sensitive components (Figure 7B).

However, Group B also reported a comparatively higher time investment during preparation and noted that the initial “wow effect” of encountering the laboratory for the first time was somewhat diminished due to prior familiarization with the virtual environment.

3.4 Emerging themes across groups

Across both preparation modalities, several cross-cutting themes emerged:

4 Discussion

Our efforts to implement a realistic and intuitive digital lab simulation resonated well with the first cohort of students. Despite room for further improvement and the need to enroll more students to gather additional feedback, the MyoRobot vLab led to a greater learner engagement through its realistic design (with more feedback received from the group with access to the vLab). A similar increase in motivation and enjoyment was observed in another appealing educational simulation by Christopoulos et al. (2022). Two major success factors in our implementation were rated positively: intuitive operation and easy navigation, highlighting the importance of these aspects for the successful digitization of engineering education (Dalgarno et al., 2009).

This observation is in line with a growing body of literature on virtual laboratories and serious-game-inspired learning environments in engineering education. For example, gamified virtual laboratories have been used to simulate experimental workflows in engineering courses and have been reported to support student preparation before entering physical laboratory sessions (Vahdatikhaki et al., 2024). Similarly, Unity-based virtual laboratory environments have been developed to replicate engineering laboratory settings, enabling students to explore experimental procedures within a safe and repeatable digital environment (Hatchard et al., 2019). More broadly, immersive virtual laboratory systems have been explored as tools for enabling experiential learning and improving conceptual understanding of complex engineering processes (Perez and Keleş, 2025; Strazzeri et al., 2024). Compared with these approaches, the present work places particular emphasis on creating a digital twin of a specific biomechatronic research device and enabling realistic interaction with experimental workflows, including pipetting procedures and system operation. While many existing systems focus primarily on conceptual experiments or generic engineering scenarios, our implementation aims to reproduce the operational logic of a real laboratory device and to link user actions directly to plausible experimental outcomes.

This implies that academic lab simulations must feel as natural as any professionally developed computer game; otherwise, students will likely refuse the offer, seeing no benefit in learning how to interact with virtual structures. Studies indicate that students desire interactions within the virtual environment to be “game-like” (Hao et al., 2021)—a stark contrast to the anxiety of educators who fear that the significance and seriousness of experiments would suffer from gamification. However, this concern seems to be mostly inherent to educators, as students report that their virtual experience could benefit from incorporating professional game design into higher education (Hao et al., 2021).

In our pilot study, students in the vLab group reported that preparation using the vLab required more time than reading the manual. We interpret this as a crucial insight for engineering educators: (i) vLabs can demand greater commitment from students during the preparation phase, and it becomes our responsibility as educators to ensure that this additional investment leads to clear learning benefits; (ii) if a vLab is to be used as a primary preparation tool, it must be designed with high-quality content, efficient guidance, and meaningful feedback to avoid being perceived as a “waste of time.” Thus, the reported increased time investment should be viewed not only as a constraint but also as an appeal to our responsibility to develop well-crafted, educationally rich virtual environments.

Therefore, our vLab implementation strongly focused on meeting new standards and expectations of students and was explicitly influenced by modern game design [this influence was restricted to elements of gameplay, as suggested by Pellas et al. (2021)]. We not only created an identical copy of the lab devices, but also included intuitive navigation/interaction, adaptive feedback, natural animations, and a workflow presented in a quest-like fashion (delivered, e.g., by a virtual tutor). Our initial evaluation was successful, as students reported an overall positive experience with the vLab. We also observed that participants who used the vLab worked more independently and efficiently in the traditional lab.

At the same time, the wow effect of entering the physical lab for the first time appeared to be reduced in the vLab group. From an engineering education perspective, this raises an interesting trade-off: is it preferable to preserve a strong first-time impression, or to prioritize prior familiarization that promotes safer, more efficient, and more analytically focused lab work? Our findings suggest that, at least in this advanced biomechatronics context, students valued the improved preparedness despite the weaker novelty impression.

According to requirements for digital learning environments (Dalgarno and Lee, 2010; Thomas and Schneider, 2018) and student feedback, the next iteration of our implementation will include a task log, enriched adaptive feedback, a non-linear storyline with self-evident user guidance, and the possibility to collaborate—features that are currently a work in progress. We consider these enhancements promising directions for further aligning serious-game-inspired vLabs with the needs of engineering curricula.

4.1 Methodological limitations

This work reports an implementation-focused pilot study and should be interpreted accordingly. First, the cohort size was small (N = 8) and derived from a single course run, which precludes statistical analysis, limits robustness, and reduces generalizability. Second, the evaluation relied on self-reported perceptions captured via open-ended questions. While data are valuable for early-stage design feedback and for identifying usability or acceptance barriers, they are susceptible to expectancy effects, social desirability, and recall bias, and they do not directly measure learning gains or laboratory performance. Third, participants were not randomly sampled beyond the course cohort, and preparation modalities could not be fully isolated because manual access was technically available to the vLab group. Fourth, we did not collect objective performance indicators in the physical laboratory (e.g., task completion time, error frequency, or instructor interventions), and, therefore, cannot claim measurable improvements in competence or performance. Finally, the manual thematic clustering of responses was intentionally pragmatic for this early-stage iteration; while multiple evaluators reviewed responses, we did not compute inter-rater reliability or conduct formal qualitative validation (e.g., saturation analysis).

Consequently, the present findings should be read as context-bound indicators that inform design implications and motivate more structured mixed-method evaluations in future cohorts. In particular, we plan to combine qualitative student feedback with objective indicators such as task completion time, procedural error counts, frequency of instructor interventions, and quantitative assessments of conceptual understanding. Such approaches will allow future studies to more systematically evaluate how virtual laboratory preparation influences both perceived preparedness and measurable laboratory performance.

4.2 Development effort, resources, and transferability

The vLab was developed by a small three-person team: a course coordinator, one Ph.D. student (primary implementation of the operational simulation), and one Master’s student who developed the interactive anatomy model as part of a curricular practical project.

Importing the MyoRobot CAD model into Unity required minimal additional effort because the device already existed as a detailed Autodesk Inventor model and could be transferred via a compatible CAD importer. The most substantial effort for the anatomy model was the functional segmentation and interaction logic (e.g., toggling component visibility, highlighting, and animating device motions), which was implemented over approximately 2 months to achieve a fully functional interactive model.

The operational simulation (3D lab scene, navigation and interaction conventions, pipetting logic, and the initial feedback system) evolved iteratively over approximately 2 years, implemented part-time by a single developer alongside Ph.D. responsibilities. Based on this experience, we estimate that a comparable prototype could be implemented within several months, provided there are dedicated staff resources and modest funding, depending on prior familiarity with Unity/Unreal and the complexity of the simulated workflow. The most time-intensive component was the pipetting logic because GUI actions needed to update and persist backend state variables; however, this follows conventional input–output programming patterns and becomes more scalable once the core architecture is implemented.

Importantly, many technical elements are reusable across laboratory scenarios, including interaction conventions, dialog/text prompts, and template logic for feedback triggers (e.g., state comparisons and context-specific messages). Using an established engine also reduces low-level development because common features (UI elements, input handling, physics, and navigation scaffolds) are already available and extensible.

5 Conclusions

To date, the vLab has been used entirely as a complementary tool. As such, we could only investigate how vLab participation affected practical class performance, but not whether it eventually improved exam performance. Evaluating this would require reasonable constructive alignment within the course; without such alignment, studies suggest that students are likely to ignore optional offers and choose traditionally reinforced approaches, even when complementary tools may have clear potential learning benefits (Biggs and Moore, 1993; Diederen et al., 2005). Our pilot study underlines the importance of embedding vLabs more systematically into assessment structures if their benefits are to be fully realized in engineering education.

For this reason, it is clear that traditional learning approaches cannot be replaced by digital teaching. However, vLabs can complement and enrich study courses by providing students with a different range of learning opportunities. This allows students to choose the learning activities and resources that best suit their learning habits (Dalgarno et al., 2009; Dalgarno and Lee, 2010). The opportunity to turn the virtual space into a “canvas” on which educational stories can be designed, unrolled, and recorded (Checa-Romero and Pascual Gómez, 2018) offers unique benefits over traditional lab courses, from which students can benefit (see Figure 8). For engineering students, such environments can foster the integration of conceptual knowledge, system-level understanding, and procedural confidence.

Figure 8

We believe that realistic digital lab courses indeed promote higher-order cognitive skills in the academic development of STEM and engineering students and help internalize the link between theory and application. As such, we will continue to develop our MyoRobot vLab to include elements of storytelling, further improve exploratory learning through decision-making processes, and enrich the user experience and understanding with more sophisticated adaptive feedback. We also see potential to transfer our design principles—digital twins, serious-game-inspired interaction, and adaptive feedback—to other engineering domains such as robotics, control systems, or optics laboratories.

References

• 1

  BiggsJ. B.MooreP. J. (1993). The Process of Learning, 3rd Edn.New York: Prentice Hall.

  • Google Scholar

• 2

  Checa-RomeroM.Pascual GómezI. (2018). Minecraft and machinima in action: development of creativity in the classroom. Technol. Pedagogy Educ.27(5), 625–637. 10.1080/1475939X.2018.1537933

  • CrossRef
  • Google Scholar

• 3

  ChristopoulosA.MystakidisS.CachafeiroE.LaaksoM.-J. (2023). Escaping the cell: virtual reality escape rooms in biology education. Behav. Inf. Technol.42, 1434–1451. 10.1080/0144929X.2022.2079560

  • CrossRef
  • Google Scholar

• 4

  DalgarnoB.BishopA. G.AdlongW.BedgoodD. R. (2009). Effectiveness of a virtual laboratory as a preparatory resource for distance education chemistry students. Comput. Educ.53(3), 853–865. 10.1016/j.compedu.2009.05.005

  • CrossRef
  • Google Scholar

• 5

  DalgarnoB.LeeM. J. W. (2010). What are the learning affordances of 3-D virtual environments?Br. J. Educ. Technol.41(1), 10–32. 10.1111/j.1467-8535.2009.01038.x

  • CrossRef
  • Google Scholar

• 6

  DiederenJ.GruppenH.HartogR.VoragenA. G. J. (2005). Evaluation of computer-based learning material for food chemistry education. Chem. Educ. Res. Pract.6(2), 64–82. 10.1039/B4RP90004A

  • CrossRef
  • Google Scholar

• 7

  GuppyN.BoudD.HeapT.VerpoortenD.MatzatU.TaiJ., et al. (2022). Teaching and learning under COVID-19 public health edicts: the role of household lockdowns and prior technology usage. Higher Educ.84(3), 487–504. 10.1007/s10734-021-00781-w

  • CrossRef
  • Google Scholar

• 8

  HaoC.ZhengA.WangY.JiangB. (2021). Experiment information system based on an online virtual laboratory. Future Internet13(2), 27. 10.3390/fi13020027

  • CrossRef
  • Google Scholar

• 9

  HatchardT.Al-AminM.RihawiZ.AlsebaeA.AzmatF., eds (2019). Design and Development of Virtual Engineering Lab. Coventry: University of Warwick.

  • Google Scholar

• 10

  HaugM.MeyerC.ReischlB.PrölßG.NüblerS.SchürmannS., et al. (2019). MyoRobot 2.0: an advanced biomechatronics platform for automated, environmentally controlled skeletal muscle single fiber biomechanics assessment employing inbuilt real-time optical imaging. Biosens. Bioelectron.138, 111284. 10.1016/j.bios.2019.04.052

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  HaugM.ReischlB.PrölßG.PollmannC.BuckertT.KeidelC., et al. (2018). The MyoRobot: a novel automated biomechatronics system to assess voltage/Ca2+ biosensors and active/passive biomechanics in muscle and biomaterials. Biosens. Bioelectron.102, 589–599. 10.1016/j.bios.2017.12.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  JonesS. M.KatyalP.XieX.NicolasM. P.LeungE. M.NolandD. M., et al. (2019). A ‘KAHOOT!’ approach: the effectiveness of game-based learning for an advanced placement biology class. Simul. Gaming50(6), 832–847. 10.1177/1046878119882048

  • CrossRef
  • Google Scholar

• 13

  PellasN.DengelA.ChristopoulosA. (2020). A scoping review of immersive virtual reality in STEM education. IEEE Trans. Learn. Technol.13(4), 748–761. 10.1109/TLT.2020.3019405

  • CrossRef
  • Google Scholar

• 14

  PellasN.MystakidisS.ChristopoulosA. (2021). A systematic literature review on the user experience design for game-based interventions via 3D virtual worlds in K-12 education. Multimodal Technol. Interact.5(6), 28. 10.3390/mti5060028

  • CrossRef
  • Google Scholar

• 15

  PerezR. P.KeleşÖ. (2025). Immersive virtual reality environments for embodied learning of engineering students. arXiv [Preprint]. arXiv:2503.16519. Available online at: https://arxiv.org/abs/2503.16519(Accessed March 27, 2026).

  • Google Scholar

• 16

  PollyP.MarcusN.MaguireD.BelinsonZ.VelanG. M. (2014). Evaluation of an adaptive virtual laboratory environment using western blotting for diagnosis of disease. BMC Med. Educ.14, 222. 10.1186/1472-6920-14-222

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  RadhamaniR.KumarD.NizarN.AchuthanK.NairB.DiwakarS. (2021). What virtual laboratory usage tells us about laboratory skill education pre- and post-COVID-19: focus on usage, behavior, intention and adoption. Educ. Inf. Technol.26(6), 7477–7495. 10.1007/s10639-021-10583-3

  • CrossRef
  • Google Scholar

• 18

  RavyseW. S.Seugnet BlignautA.LeendertzV.WoolnerA. (2017). Success factors for serious games to enhance learning: a systematic review. Virtual Real.21(1), 31–58. 10.1007/s10055-016-0298-4

  • CrossRef
  • Google Scholar

• 19

  StrazzeriI.NotebaertA.BarrosC.QuintenJ.DemarbaixA. (2024). Virtual reality integration for enhanced engineering education and experimentation: a focus on active thermography. Computers13(8), 199. 10.3390/computers13080199

  • CrossRef
  • Google Scholar

• 20

  ThomasM.SchneiderC. (2018). Language teaching in 3D virtual worlds with machinima. Int. J. Comput.-Assist. Lang. Learn. Teach.8(2), 20–38. 10.4018/IJCALLT.2018040102

  • CrossRef
  • Google Scholar

• 21

  VahdatikhakiF.Friso-van den BosI.MowlaeiS.KollöffelB. (2024). Application of gamified virtual laboratories as a preparation tool for civil engineering students. Eur. J. Eng. Educ.49(1), 164–191. 10.1080/03043797.2023.2265306

  • CrossRef
  • Google Scholar

• 22

  WolfT. (2010). Assessing student learning in a virtual laboratory environment. IEEE Trans. Educ.53(2), 216–222. 10.1109/TE.2008.2012114

  • CrossRef
  • Google Scholar

• 23

  WoodfieldB. F.AndrusM. B.AndersenT.MillerJ.SimmonsB.StangerR., et al. (2005). The virtual chemlab project: a realistic and sophisticated simulation of organic synthesis and organic qualitative analysis. J. Chem. Educ.82(11), 1728. 10.1021/ed082p1728

  • CrossRef
  • Google Scholar