The Effect of an Artificial Intelligence-Supported Virtual Reality Simulation on Nursing Students' Holistic Care Skills

NCT07518199 | Not Yet Recruiting

• 2 other identifiers: 2025-10692025-10282
• Study Type: interventional
• Target: 80
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to evaluate the effect of artificial intelligence (AI)-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is a randomised controlled trial involving fourth-year nursing students, divided into an experimental and a control group. Whilst the experimental group will receive AI-supported VR simulation training, the control group will receive traditional case-based training. Outcomes to be assessed include decision-making, symptom identification, nursing diagnosis, simulation design and satisfaction with the training methods.

Conditions

Nursing Students; Diagnostic Reasoning; Decision-Making

Keywords

Artificial Intelligence; Simulation Training; Immersive Virtual Reality; Nursing Education; Holistic Care; Nursing Diagnoses; Educational Technology

Outcome Measures

Primary Outcomes (1)

• Nursing Diagnosis and Symptom Identification within a Holistic Care Framework

  Participants' ability to correctly identify patient symptoms and formulate appropriate nursing diagnoses will be evaluated using a structured assessment form. In addition to overall accuracy, performance will be assessed based on the inclusion of multiple dimensions of holistic care (physical, psychological, social, and spiritual). Higher scores will indicate greater diagnostic accuracy in nursing and more comprehensive holistic care assessment.

  2 weeks post-intervention (assessment case study)

Secondary Outcomes (3)

• Melbourne Decision-Making Scale

  Baseline (pre-intervention) and 2 weeks post-intervention (assessment case study)

• Simulation Design Scale

  2 weeks post-intervention

• Satisfaction Survey Regarding Teaching Methods

  2 weeks post-intervention

Study Arms (2)

AI-VRS

EXPERIMENTAL

At the start of the study, participants will be asked to complete a demographic questionnaire and the Melbourne Decision-Making Scale. They will then be assigned to research groups based on their overall academic grade point average and their experience with virtual reality (VR) headsets. Before the intervention, participants will be provided with a pre-intervention information guide. The experimental group will undergo training using an AI-supported VR simulation designed to facilitate taking a patient history, identifying symptoms, and determining a nursing diagnosis. Two weeks after the training, concurrently with the control group, they will undertake an assessment case study in which they must analyse the case individually, without instructor support, to identify nursing diagnoses and symptoms. They will then complete the Melbourne Decision-Making Scale, the Simulation Design Scale, and a satisfaction questionnaire regarding the training methods.

Other: AI-VRS

Control

OTHER

At baseline, participants will complete a demographic questionnaire and the Melbourne Decision-Making Scale. Participants will then be allocated to study groups using a stratified randomisation approach based on academic grade point average and prior experience with VR headsets. The control group will receive traditional case-based training using written clinical scenarios. Following the training, a two-week interval will be observed. After this period, both the control and experimental groups will complete an assessment case simultaneously. During this assessment, participants will be required to independently analyse the case, identify the patient's symptoms, and formulate appropriate nursing diagnoses without instructor support. After completing the assessment, participants will again complete the Melbourne Decision-Making Scale as a post-test measure.

Other: Control

Interventions

AI-VRS OTHER

This intervention consists of an AI-supported virtual reality (VR) simulation designed to improve nursing students' holistic care skills. Participants interact with a virtual patient to perform patient history-taking, identify symptoms, and formulate nursing diagnoses across the dimensions of holistic care (physical, psychological, social, and spiritual). The simulation is delivered using Meta Quest 3 VR headsets and incorporates artificial intelligence to provide dynamic, responsive patient interactions. The intervention includes structured simulation scenarios with high-fidelity graphics and interactive decision-making processes to support skill acquisition.

AI-VRS

Control OTHER

This intervention consists of traditional case-based training delivered through presentations and question-and-answer discussions. Participants will analyse case scenarios and receive feedback from instructors. This approach provides a practical learning experience without using VR technology.

Control

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Voluntary participation in the study,
• Having enrolled for the first time in the courses HEM 402 Professional Practice I and HEM 404 Professional Practice II in the Department of Nursing, Faculty of Health Sciences,
• Absence of eye conditions affecting depth perception, such as amblyopia (lazy eye), anisometropia(different refractive errors in each eye), and strabismus (squint). (Self- report is accepted.),
• Academic performance score between 2.00 and 4.00.,

You may not qualify if:

• Having received training in holistic care skills in addition to their undergraduate nursing degree,
• Having experience with virtual simulation exercises focused on holistic care skills,
• Holding a high school, foundation year or undergraduate degree in a health-related field,
• Having difficulty understanding and speaking Turkish,
• The participant has not completed or has incompletely completed the required forms and scales,
• The participant in the experimental group had not taken part in or completed the AI-supported virtual reality simulation,
• Students in the control group did not take part in the educational case study,
• Students in the experimental and control groups did not take part in the assessment case study,
• The participant wishes to withdraw from the study,

Sponsors & Collaborators

• Gazi University: lead

Study Sites (1)

Gazi University Nursing Faculty

Ankara, 06500, Turkey (Türkiye)

Location

Related Publications (8)

• Hopewell S, Chan A, Collins G S, Hróbjartsson A, Moher D, Schulz K F et al. CONSORT 2025 statement: updated guideline for reporting randomised trials BMJ 2025; 389 :e081123 doi:10.1136/bmj-2024-081123

  BACKGROUND

• INACSL Standards Committee, Decker, S., Sapp, A., Bibin, L., Chidume, T., Crawford, S. B., Fayyaz, J., Johnson, B. K., & Szydlowski, J. (2025d). Healthcare Simulation Standards of Best Practice®: The Debriefing Process. Clinical Simulation in Nursing, 105, 101775-101775. https://doi.org/10.1016/j.ecns.2025.101775

  BACKGROUND

• INACSL Standards Committee, DiGregorio, H., Todd, A., Blackwell, B., Brennan, B. A., Repsha, C., Shelton, C. M., Vaughn, J., Wands, L., Wruble, E., & Yeager, C. (2025c). Healthcare Simulation Standards of Best PracticeⓇ Facilitation. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101776

  BACKGROUND

• INACSL Standards Committee, Watts, P.I, McDermott, D.S., Alinier, G., Charnetski, M., Ludlow, J., Horsley, E., Meakim, C., & Nawathe, P. (2021b). Healthcare Simulation Standards of Best Practice® Simulation Design. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2021.08.009.

  BACKGROUND

• INACSL Standards Committee. (2021a). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing Persico, Lori et al. Clinical Simulation in Nursing, Volume 105, 101777. https://doi.org/10.1016/j.ecns.2025.101777 1876-1399

  BACKGROUND

• INACSL Standards Committee, Persico, L., Wilson-Keates, B., DiGregorio, H., Decker, S., & Xavier, N. (2025a). Preamble: Grounded in Excellence: The Cornerstone Healthcare Simulation Standards of Best Practice®. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101774

  BACKGROUND

• INACSL Standards Committee, Persico, L., Ramakrishnan, S., Wilson-Keates, B., Catena, R., Charnetski, M., Fogg, N., Jones, M. C., Ludlow, J., MacLean, H., Simmons, V. C., Smeltzer, S., & Wilk, A. (2025b). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing. Clinical Simulation in Nursing https://doi.org/10.1016/j.ecns.2025.101777

  BACKGROUND

• Ackley, B. J., & Ladwig, G. B. (2024). Hemşirelik tanıları el kitabı: Bakım planlamasında kanıta dayalı rehber (Z. Göçmen Baykara, N. Çalışkan, E. Gülnar, E. Sarıtaş, & G. Eyüboğlu, Ed. ve çev., 13. baskı). Ankara: Nobel Tıp Kitabevleri. ISBN:978-625-6448-92-6.

  BACKGROUND

Central Study Contacts

Nurcan Çalışkan, Prof., PhD

CONTACT

+90 312 216 2653; nurcany@gazi.edu.tr

Özlem Tikit, Research Assistant, PhD student

CONTACT

+90 0554 756 58 04; ozlemtikit@gazi.edu.tr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Nursing

Model Details: Parallel-group, pre-test/post-test randomized controlled trial.

Study Record Dates

• First Submitted: March 30, 2026
• First Posted: April 8, 2026
• Study Start: April 1, 2026
• Primary Completion: April 1, 2026
• Study Completion: May 1, 2026
• Last Updated: April 8, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)