Decision-Making, Diagnostic Reasoning, Nursing Students
Conditions
Keywords
Artificial Intelligence, Simulation Training, Immersive Virtual Reality, Nursing Education, Holistic Care, Nursing Diagnoses, Educational Technology
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.
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.
Interventions
OTHERAI-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.
OTHERControl
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.
Sponsors
Gazi University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE
Intervention model description
Parallel-group, pre-test/post-test randomized controlled trial.
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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,
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Nursing Diagnosis and Symptom Identification within a Holistic Care Framework | 2 weeks post-intervention (assessment case study) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Melbourne Decision-Making Scale | Baseline (pre-intervention) and 2 weeks post-intervention (assessment case study) | 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). |
| Simulation Design Scale | 2 weeks post-intervention | 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. |
| Satisfaction Survey Regarding Teaching Methods | 2 weeks post-intervention | 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. |
Countries
Turkey (Türkiye)
Contacts
CONTACTNurcan Çalışkan, Prof., PhD
CONTACTÖzlem Tikit, Research Assistant, PhD student
Outcome results
None listed