Augmented and Virtual Reality in Anatomical Education and its Evaluation: Quality Matters
Bibliographic record
Abstract
Introduction and Objective Many studies have described augmented and virtual reality (AR/VR) teaching in anatomy as having great potential. However, for this potential to be realized, deeper exploration of the offered evidence in AR/VR tool evaluation is required. While the body of evidence may be increasing to support the use of AR/VR, the purpose of this review is to examine the gaps in implementing and evaluating AR/VR anatomical education. The focus of many reviews is on the technology with less emphasis on instructional design. This review aims to pave the way for future researchers to provide evidence based, rigorously evaluated and validated anatomical educational tools that will truly be helpful. Materials and Methods A literature search was conducted using search terms related to AR/VR anatomical educational tool evaluation. Included studies were evaluated for quality of research. Results Gaps in AR/VR anatomy education evaluation studies included lack of user‐focused design process, minimal to no re‐iterations of AR/VR anatomy educational methods, high risk of bias, low validity, and lack of standardized protocols. Conclusion In order to successfully and sustainably incorporate AR/VR into institutional educational curricula, high quality, comprehensive, standardized protocols of evaluation and user‐centred design approaches to meet instructional and curricular goals are required. Significance/Implication AR/VR is becoming increasingly available as a tool in anatomy education. It is a promising but relatively new frontier, and as such there is an increased pressure to seek out high quality evidence regarding its efficacy. These protocols must reflect a user‐centered, iterative process that begets high quality anatomical educational tools which would reliably and reproducibly lead to higher quality educational outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.441 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".