Enriching Anatomy Learning with Virtual Reality Clinical Scenarios
Bibliographic record
Abstract
Introduction The use of virtual reality (VR) to simply display anatomical specimens typically fails to exploit the potential of VR to enhance the learning environment. For example, anatomy education normally occurs within the confines of a laboratory which is devoid of relevant context, while VR is uniquely capable of providing a clinically‐relevant space that may enhance the anatomy learning experience. However, it is also possible that an enriched VR environment may inhibit learning, perhaps as a result of increased cognitive load. In general, well‐designed educational materials create schemas and organize the acquired knowledge in a way that promotes deeper learning, retention, and easy retrieval in other settings. Efforts must be made, in instructional design, to reduce extraneous and increase germane cognitive load, but how exactly this is done in VR remains an open question. Objective The purpose of this study is to evaluate the educational efficacy of a VR module which includes a clinical scenario presented in an immersive, contextually‐relevant virtual environment. Hypothesis Although learning in a VR enriched environment with a clinical scenario can increase extraneous cognitive load, we hypothesize that it will ultimately facilitate memorization of anatomical structures, when compared with using 3D‐printed physical models or an interactive, 2D environment. Methods Participants with no prior knowledge of pelvic anatomy will be randomly assigned to one of three groups to learn human pelvic anatomy in an identical module presented in VR, an interactive 2D computer‐based module, or physical environment consisting of 3D‐printed models. Prior to the learning phase, participants will complete the Mental Rotation Test (MRT), as well as the Titmus Fly and Titmus Circles tests to assess spatial visualization ability and stereoscopic vision, respectively. They will then complete a pre‐test assessment where they will be asked to identify anatomical structures labeled on a 3D‐printed model. Participants will then be given 10 minutes to learn and memorize pelvic anatomy using their assigned anatomy modality. This will be followed by a post‐test assessment with another set of labeled structures to identify. Finally, participants will complete a survey to share feedback on the learning experience and will complete the Simulation Task Load Index (SIM‐TLX) questionnaire to assess cognitive load. Conclusion The impact of contextually‐relevant enriched virtual environments and clinical scenarios for VR‐based anatomy education have yet to be explored. The findings from this study will provide valuable insight to inform the design of future VR learning tools that not only reduce or limit factors which can impair learning, but also suggest factors within the learning environment which have the potential to improve anatomy learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".