Implementing Immersive Virtual Reality Simulation in a Distributed Undergraduate Nursing Program: An Educational Design Research Study
Bibliographic record
Abstract
INTRODUCTION: Immersive virtual reality simulation (IVRS) is an increasingly popular tool in health education. This qualitative study explores the integration of IVRS into a distributed practical nurse-to-registered nurse program. The intervention included hardware and software deployment, faculty and student orientation, and the integration of IVRS scenarios into existing courses. This study aimed to (1) describe the IVRS intervention and its implementation; (2) explore the perceptions and experiences of students, faculty, and information technology staff involved in the first iteration of the IVRS intervention; and (3) offer reflections and recommendations for optimizing student learning. METHODS: Using an educational design framework and generic qualitative methodology, we collected data through demographic surveys and postintervention focus groups and interviews. RESULTS: Thematic analysis revealed 3 key themes: user experience, IVRS impact on learning, and achieving consensus. Although faculty responses were positive, student responses were mixed. Findings suggest that successful implementation of IVRS in distributed nursing education requires deliberate planning, comprehensive orientation, and ongoing support. CONCLUSIONS: IVRS has the potential to enhance nursing education. Success hinges on thorough preparation; ongoing student, faculty, and institutional support; and clear communication. Recommendations for future interventions include enhancing student preparation, explicitly communicating the purpose and expectations of IVRS integration to strengthen alignment between faculty and students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".