The Effectiveness of Virtual Reality in Medical Education: A Meta-Analysis of Knowledge, Skills, and Motivation Outcomes
Bibliographic record
Abstract
Virtual reality (VR) technology has emerged as a promising educational tool in medical training, offering immersive learning experiences that address traditional limitations in medical education. However, comprehensive evidence regarding VR's overall effectiveness across diverse medical education contexts remains fragmented. To systematically evaluate the effectiveness of virtual reality interventions compared to traditional educational methods in medical education through meta-analysis of randomized controlled trials and quasi-experimental studies. A systematic literature search was conducted across Google Scholar, PubMed, Scopus, and ERIC databases from January 2015 to December 2024. Studies involving medical students, residents, or healthcare professionals using VR-based educational interventions with traditional comparison groups were included. Primary outcomes included knowledge acquisition, skills development, and motivation measures. Standardized mean differences (Cohen's d) were calculated using fixed-effects models, with subgroup analyses by outcome type. Twenty-one studies encompassing 1,527 participants met the inclusion criteria. The overall meta-analysis revealed a moderate to significant positive effect favoring VR interventions (d = 0.510, 95% CI: 0.408 to 0.612, p < .001). Subgroup analysis demonstrated differential effectiveness: skills outcomes showed the most significant effect (d = 0.692, p < .001), knowledge outcomes showed moderate effects (d = 0.346, p < .001), while motivation outcomes showed no significant difference (d = 0.054, p = 0.685). Substantial heterogeneity was observed (I² = 87.06%). This meta-analysis provides robust evidence supporting VR's effectiveness in medical education, particularly for skills development and knowledge acquisition. The differential effects suggest VR's strength in enhancing practical competencies. However, substantial heterogeneity highlights the importance of implementation quality and contextual factors. These findings support the strategic integration of VR technology in medical curricula, especially for procedural training and clinical skills development.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".