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Immersive virtual reality simulation: engagement and clinical judgment

  • Amanda Savage EMAIL logo and Mary Tedesco-Schneck
Published/Copyright: October 13, 2025

Abstract

Objectives

Immersive Virtual Reality Simulation (IVRS) has been reported to improve clinical judgment, communication, and competency in nursing psychomotor skills. The purpose of this study was to determine if IVRS implemented in a senior-level undergraduate medical-surgical didactic course improved student engagement and clinical judgment.

Methods

IVRS was executed in three class sessions for one cohort of students. Clinical judgment was compared in two cohorts of students [IVRS, n=39 Fall 2023; no IVRS, n=36 Spring 2024]. Engagement theory served as the framework for this study. Only students in the IVRS cohort completed the Student Course Engagement Questionnaire (SCEQ) at the end of the course to measure course engagement. Clinical judgement was measured using the six cognitive domains on the Kaplan® Med/Surg Comprehensive NGN Integrated Test for both cohorts of students (IVRS and no IVRS).

Results

There was no statistical difference in clinical judgment scores between the two cohorts. IVRS as measured by the SCEQ increased engagement by motivating students to take good notes (47 %) and review notes between class (44 %), listen carefully, apply content to clinical, and ask questions on unclear concepts (47 %). Students reported that IVRS was fun (53 %).

Conclusions

IVRS is a novel educational tool that contributes to student engagement with medical/surgical content and can easily be accommodated in a classroom setting. IVRS is an engaging active learning strategy that can be implemented internationally by schools of nursing.


Corresponding author: Amanda Savage, Department of Nursing, University of Maine System, 5724 Dunn Hall, Orono, ME 04469-5724, USA, E-mail:

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: Authors state no conflict of interest.

  6. Research funding: None declared.

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Received: 2024-08-13
Accepted: 2025-07-08
Published Online: 2025-10-13

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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