Influence of virtual reality simulation (excluding augmented reality) on endodontic learning experiences of undergraduate dental students: a systematic review
Bibliographic record
Abstract
BACKGROUND: Virtual Reality (VR) technology has demonstrated a promising prospect for enhancing endodontic learning in undergraduate dental students by boosting their procedural skills, accuracy, and confidence. AIM: To systematically evaluate the effectiveness of virtual reality (VR) simulation in endodontic education among undergraduate dental students, with a specific focus on four key outcomes: procedural accuracy, enhancement of student confidence, reduction in procedural errors, and overall learner satisfaction. METHODS: An exhaustive literature search was carried out in December 2024 in PubMed, Cochrane Library, Embase, Scopus, and ClinicalTrials.gov. Randomized controlled trials (RCTs), quasi-experimental studies, and cross-sectional studies published between 2010 and 2024 were included in the review. Risk of bias was appraised as follows: Cochrane Risk of Bias 2.0 (RoB2) tool for RCTs; Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies; National Institute of Health (NIH) Quality Assessment Tool for before-and-after studies; and the Methodological Index for Non-Randomized Studies (MINORS) tool for non-randomized studies without a comparator group. RESULTS: Fifteen studies were included in the final analysis. VR-based training showed statistically significant differences between the pre and post-test scores regarding procedural accuracy and efficiency for tasks at the end of endodontics. These results showed that VR training leads to greater confidence and skill levels in students than traditional approaches, improved retention of knowledge, and a reduction in errors. Advantages notwithstanding, limitations around cost and accessibility were observed. CONCLUSION: VR simulation is an effective, valuable tool in the endodontic education toolbox. Further studies should assess cost-effectiveness and long-term clinical performance effects.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".