IMMERSIVE VIRTUAL REALITY AND ARTIFICIAL INTELLIGENCE FOR ENHANCING STUDENT PREPAREDNESS FOR CLINICAL EXAMS
Notice bibliographique
Résumé
Introduction: Test anxiety is a common issue among post-secondary students, leading to negative consequences such as the increased risk of dropout, lower grades, and limited employment opportunities.Students unfamiliar with the test-taking environment are more likely to have test anxiety.This study aimed to explore virtual reality (VR) and artificial intelligence (AI) as potential solutions to reduce test anxiety in health science students preparing for clinical exams.By utilizing an AI-powered virtual testing environment with interactive virtual patients, students acted as medical professionals in a simulated clinical setting, allowing them to familiarize themselves with the environment and potentially reduce their anxiety levels.The study utilized AI in the form of a generative pre-trained transformer (GPT) to generate responses from virtual patients.System was evaluated on its ability to reduce test anxiety.Objective: To assess the efficacy of a VR simulation of a clinical setting in reducing student anxiety for a clinical exam and gather student perspectives on their VR simulation and coursework experiences to better understand their learning environment.Methods: First-year health science students were invited to participate in a VR session that took place three-days before their clinical exam.Students exposed to VR (YesVR) and those who opted out (NoVR) had their anxiety levels compared to one another using the State Trait Anxiety Inventory (STAI) and Test Anxiety Inventory (TAI).Immersive VR simulation included history-taking and cognitive assessment modules, allowing students to communicate with virtual patients in natural language in a virtual clinic.Virtual patient responses were generated by GPT, fine-tuned with transfer learning techniques based on real-world student and standardized patient video recordings.After completing their clinical exams, students were invited to participate in semi-structured interviews and focus groups.Results: A total of 108 students participated in the quantitative aspects of the study (mean aged 24.53 years, SD 2.64): 61 for the NoVR group (mean aged 24.52 years, SD 2.42) and 47 for the YesVR group (mean aged 24.54 years, SD 2.93).There was a significant difference in state anxiety scores between groups, with NoVR showing greater anxiety scores (mean 51.69, SD 11.87) than YesVR (mean 39.79, SD 12.21) (t106=5.10,P=<.001, Cohen d = 0.99).The mean difference was 11.90 units (95% CI 7.28-16.53).A total of 25 students participated in the interviews and focus groups -16 from interviews and 9 from focus groups.The major themes emerging from focus groups and interviews were overall student background, exam feedback, fear of the unknown, self-consciousness, and the exam environment.Conclusion: This study highlights the potential of AI-enhanced VR as an effective tool for reducing test anxiety and increasing student familiarity with clinical exam environments.The results suggest that VR may reduce ambiguity and uncertainty, which are key contributors to test anxiety.The findings provide valuable insights into the potential of VR and AI in addressing test anxiety.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 source (Gemma direct ou Codex distillé), 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 ».