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Attitudes toward artificial intelligence among CRNA students: A multi-institutional cross-sectional study
Joshua Olson1, Ava Travo2, Phillip Olla1
1University of Detroit Mercy, 4001 W McNichols Rd, Detroit, MI, 48221, United States of America.
Background:
As artificial intelligence continues to transform healthcare and education, nurse anesthesia students must be prepared to use artificial intelligence tools effectively in both academic and clinical environments. Little is known about these students' current exposure to artificial intelligence or their attitudes toward integrating it into their learning.
Aim:
To examine nurse anesthesia students' perspectives on artificial intelligence in anesthesia education, focusing on their familiarity, perceptions, and acceptance of artificial-intelligence-enhanced learning.
Design:
A cross-sectional survey design was used, conducted as a secondary analysis of a previously reported cohort.
Settings:
Six nurse anesthesia programs at universities across the United States.
Participants:
Four hundred one student registered nurse anesthetists.
Methods:
A survey using the six-item Attitudes Toward Artificial Intelligence scale and demographic questions assessed familiarity, perceptions, and acceptance. Descriptive statistics summarized sample characteristics; non-parametric tests and Welch's analysis of variance compared demographic subgroups, with Benjamini-Hochberg correction for multiple comparisons. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.
Results:
The mean scale score was 2.81 (standard deviation 0.81), which falls within the neutral range but below the scale's neutral midpoint of 3.0. Higher item-level means were observed for positive feelings toward artificial intelligence (3.14), perceived usefulness in coursework (3.11), and agreement that instructors should teach students how to integrate it (3.08). Subgroup analyses by sex, race/ethnicity, age group, and study site revealed no differences that remained significant after correction for multiple comparisons.
Conclusions:
Nurse anesthesia students demonstrate limited familiarity with artificial intelligence tools and hold neutral perceptions and modest acceptance of its integration. These findings highlight the need for structured artificial intelligence literacy education within nurse anesthesia programs to better prepare students for increasingly artificial-intelligence-enhanced clinical and academic environments.
