This article is published and under copyright: Teaching and Learning in Nursing, © 2025 Organization for Associate Degree Nursing, published by Elsevier, all rights reserved. Reproducing the text, tables or figures on this site would not be permitted, so this page gives the citation, a short summary in our own words, and a link to the published version.
This site publishes the statistical analysis and the code, which are the parts of the work it can properly share.
Citation
Barker, N., Yocom, D., & DaSilva, M. (2026). Measuring undergraduate nursing students' acceptance of AI enabled virtual reality using AI generated simulations. Teaching and Learning in Nursing, 21, e109-e113. https://doi.org/10.1016/j.teln.2025.08.020
What the study did
Nursing faculty built virtual reality simulations using a software platform that generates scenarios with AI, and ran them with baccalaureate nursing students. The question was not whether the simulations taught anything, but whether students would accept the technology at all, which is the prior question for any tool a curriculum is considering adopting.
Acceptance was measured with UTAUT, the Unified Theory of Acceptance and Use of Technology. It asks about four distinct reasons someone takes up a technology: whether it helps them perform (Performance Expectancy), whether people around them encourage it (Social Influence), whether it feels easy (Effort Expectancy), and whether support exists when it goes wrong (Facilitating Conditions). Students completed the 18 items after their simulation, each scored from 1, strongly disagree, to 5, strongly agree.
19 students responded. Institutional review board approval was obtained before the study ran.
What it reported
Students reported intermediate to advanced acceptance of the technology. The article gives a UTAUT total and the four construct means, and concludes that baccalaureate nursing students had high acceptability of AI-generated VR simulations.
Our recomputation from the response file reproduces all four construct means exactly, and the total's standard deviation and range. It differs on the total mean, which is documented in section 5 of the analysis. The construct-level findings, which carry the article's conclusions, are unaffected.
The analysis page also adds what the article does not report: confidence intervals, effect sizes, reliability with intervals, assumption checks and a multiplicity correction. The most substantive addition is that Social Influence is not distinguishable from neutral, which a single overall acceptance figure obscures.
Authors
Nancy Barker, EdD, MSN, RN, CHSE (corresponding author) · Danielle Yocom, DNP, RN, FNP-BC · Michelle DaSilva, EdD, RN, FNP-BC. West Chester University, West Chester, PA, USA.
Correspondence should go through the journal or the authors' institutional pages. Their email addresses appear in the published PDF and are deliberately not repeated on this page.
John Fisher performed the statistical analysis published on this site and is not an author of the article.