Comparing artificial intelligence-enhanced virtual reality and simulated patient simulations in undergraduate nursing education
Bibliographic record
Abstract
Introduction Simulation-based learning (SBL) is foundational in nursing education, traditionally employing simulated patients (SPs) to create realistic clinical scenarios. Recent technological advances have introduced Artificial Intelligence-Enhanced Virtual Reality (AI-VR) as a scalable, cost-effective alternative. Aim and Methods This qualitative descriptive study explored undergraduate nursing students' experiences with both AI-VR and SP simulations, focusing on perceptions of realism, psychological safety, and skill development. Findings While 240 students participated overall (120 in each simulation modality), in-depth qualitative data were collected from 20 third-year nursing students who experienced either approach through four focus groups. Findings revealed that SP simulations excel in fostering emotional engagement and hands-on learning, whereas AI-VR provides an intuitive, judgment-free environment conducive to rapid decision-making and iterative practice. Conclusions The study indicates implications for curriculum design, faculty development, and future research, advocating for an integrated approach that leverages the strengths of both modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".