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Record W4410019369 · doi:10.2196/69428

Immersive Virtual Reality and AI (Generative Pretrained Transformer) to Enhance Student Preparedness for Objective Structured Clinical Examinations: Mixed Methods Study

2025· article· en· W4410019369 on OpenAlexaffvenue
Shaniff Esmail, Brendan Concannon

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnxietyPreparednessCourseworkObjective structured clinical examinationPsychologyTest anxietyExposure therapyVirtual Reality Exposure TherapyVirtual realityTest (biology)Medical educationClinical psychologyMedicineApplied psychologyArtificial intelligenceComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Immersive virtual reality (VR) and artificial intelligence have been used to determine whether a simulated clinical exam setting can reduce anxiety in first-year occupational therapy students preparing for objective structured clinical examinations (OSCEs). Test anxiety is common among postsecondary students, leading to negative outcomes such as increased dropout risk, lower grades, and limited employment opportunities. Students unfamiliar with specific testing environments are particularly prone to anxiety. VR simulations of OSCEs may allow students to become familiar with the exam setting and reduce anxiety. OBJECTIVE: This study aimed to assess the efficacy of a VR simulation depicting clinical settings to reduce student anxiety about a clinical exam while gathering perspectives on their first-year coursework experiences to better understand their learning environment. METHODS: An experimental, nonrandomized controlled trial compared state anxiety, trait test anxiety, and OSCE grades in 2 groups of first-year occupational therapy students analyzed using independent t tests (2-tailed). Group 1 (NoVR) was not exposed to the VR simulation and acted as a control group for group 2 (YesVR), who were exposed to the VR simulation. The VR used artificial intelligence in the form of a generative pretrained transformer to generate responses from virtual patients as students interacted with them in natural language. Self-reported psychometric scales measured anxiety levels 3 days before the OSCE. YesVR students completed perceived preparation surveys at 2 time points-3 weeks and 3 days before the OSCE-analyzed using dependent t tests. Semistructured interviews and focus groups were conducted within 1 week after the OSCE. Student perspectives on their classes and VR experiences were summarized using interpretative thematic analysis. RESULTS: =3.96; P<.001; Cohen d=1.02). The mean difference was 11.96 units (95% CI 5.92-18.01). Trait test anxiety and OSCE scores remained static between groups. There was an increase in all perceived preparedness variables in the YesVR group. In total, 42% (25/60) of the participants took part in interviews and focus groups, providing major themes regarding factors that affect OSCE performance, including student experience and background, feedback and support, fear of unknown, self-consciousness, and knowledge of the exam environment. CONCLUSIONS: Intolerance of uncertainty may lead students to interpret ambiguous exam situations as overly precarious. Findings suggest that VR simulation was associated with reduced state anxiety, although results from this small, nonrandomized sample should be interpreted cautiously. Qualitative data indicated that VR helped students gain familiarity with clinical exam settings, potentially decreasing uncertainty-based anxiety. Future research with larger or randomized samples is needed to confirm these findings and explore advanced VR tools offering feedback to enhance learning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.037
GPT teacher head0.535
Teacher spread0.498 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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