Enhancing Immersion in Virtual Reality–Based Advanced Life Support Training: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Serious game-based training modules are pivotal for simulation-based health care training. With advancements in artificial intelligence (AI) and natural language processing, voice command interfaces offer an intuitive alternative to traditional virtual reality (VR) controllers in VR applications. OBJECTIVE: This study aims to compare AI-supported voice command interfaces and traditional VR controllers in terms of user performance, exam scores, presence, and confidence in advanced cardiac life support (ACLS) training. METHODS: A total of 62 volunteer students from Acibadem Mehmet Ali Aydinlar University Vocational School for Anesthesiology, aged 20-22 years, participated in the study. All the participants completed a pretest consisting of 10 multiple-choice questions about ACLS. Following the pretest, participants were randomly divided into 2 groups: the voice command group (n=31) and the VR controller group (n=31). The voice command group members completed the VR-based ACLS serious game in training mode twice, using an AI-supported voice command as the game interface. The VR controller group members also completed the VR-based ACLS serious game in training mode twice, but they used VR controllers as the game interface. The participants completed a survey to assess their level of presence and confidence during gameplay. Following the survey, participants completed the exam module of the VR-based serious gaming module. At the final stage of the study, participants completed a posttest, which had the same content as the pretest. VR-based exam scores of the voice command and VR controller groups were compared using a 2-tailed, independent-samples t test, and linear regression analysis was conducted to examine the effect of presence and confidence rating. RESULTS: Both groups showed an improvement in performance from pretest to posttest, with no significant difference in the magnitude of improvement between the 2 groups (P=.83). When comparing presence ratings, there was no significant difference between the voice command group (mean 5.18, SD 0.83) and VR controller group (mean 5.42, SD 0.75; P=.25). However, when comparing VR-based exam scores, the VR controller group (mean 80.47, SD 13.12) significantly outperformed the voice command group (mean 66.70, SD 21.65; P=.005), despite both groups having similar time allocations for the exam (voice command group: mean 18.59, SD 5.28 minutes and VR controller group: mean 17.3, SD 4.83 minutes). Confidence levels were similar between the groups (voice command group: mean 3.79, SD 0.77 and VR controller group: mean 3.60, SD 0.72), but the voice command group displayed a significant overconfidence bias (voice command group: mean 0.09, SD 0.24 and VR controller group: mean -0.09, SD 0.18; P=.002). CONCLUSIONS: VR-based ACLS training demonstrated effectiveness; however, the use of voice commands did not result in improved performance. Further research should explore ways to optimize AI's role in education through VR. TRIAL REGISTRATION: ClinicalTrials.gov NCT06458452; https://clinicaltrials.gov/ct2/show/NCT06458452.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».