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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Nursing Students; Decision-Making; Diagnostic Reasoning
• Intervention / Treatment: Other: AI-VRS; Other: Control

Detailed Description

This study will evaluate the effect of an artificial intelligence-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is designed as a pre-post, parallel-group, randomised controlled trial involving 80 fourth-year nursing students (40 in the experimental group and 40 in the control group). Eligible participants will complete a demographic form and the Melbourne Decision-Making Scale at the outset. Participants will be stratified by overall academic grade point average and prior VR experience, and randomly assigned to groups by an independent statistician.

Whilst the intervention group receives AI-supported VR simulation training, the control group will receive traditional case-based training using the same case scenario to ensure comparability. Both the training case and the assessment case, along with the assessment criteria, will be developed based on expert consensus.

Two weeks after the intervention, both groups will complete a case study assessment. Data collection will include the Melbourne Decision-Making Scale, nursing diagnosis and symptom identification results, and satisfaction measures. The statistician will be blinded, and appropriate statistical tests will be applied based on the data distribution.

• Study Type: Interventional
• Enrollment (Estimated): 80
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Nurcan Çalışkan, Prof., PhD; Phone Number: +90 312 216 2653; Email: nurcany@gazi.edu.tr
• Study Contact Backup: Name: Özlem Tikit, Research Assistant, PhD student; Phone Number: +90 0554 756 58 04; Email: ozlemtikit@gazi.edu.tr

Study Locations

• Turkey (Türkiye)
  • Ankara, Turkey (Türkiye), 06500
    • Gazi University Nursing Faculty
    • Contact: Özlem Tikit, Research Assistant, PhD student; Phone Number: +90 0554 756 58 04; Email: ozlemtikit@gazi.edu.tr
    • Contact: Nurcan Çalışkan, Prof., PhD; Phone Number: +0312 216 26 53; Email: nurcany@gazi.edu.tr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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.,

Exclusion Criteria:

• 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,

Criteria for Exclusion from the Study:

• 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,

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-VRS; 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; 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.
Other: Control; 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Melbourne Decision-Making Scale	Used to assess changes in decision-making skills. Higher scores indicate better decision-making performance.The Melbourne Decision-Making Scale consists of two parts. MKVÖ I: This scale is designed to assess self-esteem (self-confidence) in decision-making. The maximum score on the scale is 12. High scores indicate high self-esteem in decision-making. MKVÖ II: The scale measures decision-making styles. In the scoring of MKVÖ II, the following score ranges are used: cautious (0-12), avoidant (0-12), procrastinating (0-10), and panicked (0-10).	Baseline (pre-intervention) and 2 weeks post-intervention (assessment case study)
Simulation Design Scale	Assessed using the Simulation Design Scale to evaluate participants' perceptions of the simulation design. Higher scores indicate more positive evaluations. The scale consists of five subscales-"Goals and Knowledge," "Support," "Problem Solving," "Feedback/Guided Reflection," and "Authenticity"-and twenty items. The number of items in each subscale is as follows: the "Goals and Knowledge" subscale has 5 items, the "Support" subscale has 4 items, the "Problem Solving" subscale has 5 items, the "Feedback/Guided Reflection" subscale has 4 items, and the "Degree of Authenticity" subscale has 2 items. The scale, administered in two sections, measures students' opinions on whether the best simulation design elements were implemented in the simulation application in the first section and on the extent to which the simulation application is important to students in the second section.	2 weeks post-intervention
Satisfaction Survey Regarding Teaching Methods	Student satisfaction will be assessed immediately after completion of the VR training to capture participants' perceptions of the training experience. Higher scores indicate greater satisfaction.The survey is scored on a scale of 16 to 80 points.	2 weeks post-intervention

Collaborators and Investigators

• Sponsor: Gazi University

Publications and helpful links

General Publications

• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): April 1, 2026
• Primary Completion (Estimated): April 1, 2026
• Study Completion (Estimated): May 1, 2026

Study Registration Dates

• First Submitted: March 30, 2026
• First Submitted That Met QC Criteria: April 4, 2026
• First Posted (Actual): April 8, 2026

Study Record Updates

• Last Update Posted (Actual): April 8, 2026
• Last Update Submitted That Met QC Criteria: April 4, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Educational Technology; Nursing Education; Simulation Training; Immersive Virtual Reality; Holistic Care; Nursing Diagnoses
• Other Study ID Numbers: 2025-1069; 2025-10282 (Other Grant/Funding Number: GAZİ UNİVERSİTY SCIENTIFIC RESEARCH PROJECTS COORDINATION UNIT (BAP))

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No