ArticleBMC medical education2025
Individual innovativeness levels and levels of medical artificial intelligence readiness among nursing students: a cross-sectional and correlational study.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimThis study, aimed to determine the individual innovativeness levels of nursing students and their readiness levels for medical artificial intelligence and the relationship between these two variables.
backgroundA healthcare team with innovative personality traits is essential for the effective use of artificial intelligence in healthcare. It is important to determine the perspectives of nursing students, who are among the most crowded members of the team and whom we define as the nurses of the future, in this direction.
designThe research was designed as descriptive and correlational.
methodThe research data were collected by the researcher between March 1 and May 1, 2023. The study included 1st, 2nd, 3rd and 4th year nursing students studying at two universities in Anatolia. Data were collected with a reliable online survey method. The sample selection procedure was based on the sampling of the known population method. The study was conducted with 781 nursing students using a cross-sectional and correlational study method. The data were collected using the Personal Information Form, Individual Innovativeness Scale and Medical Artificial Intelligence Readiness Scale. The conformity of the data to the normal distribution was made by using skewness and kurtosis values and the range of + 2 and - 2 was taken as reference. Significance level p < .05 was accepted in the tests.
resultsThe mean total score of the IIS, nursing students was 55.09 ± 9.22, and the mean total score of the MARS nursing students was 67.63 ± 12.83, and there was a weak positive correlation between the scales (r = .172, p < .001).
conclusionIn our study, it was determined that nursing students' individual innovativeness levels positively affected their readiness for medical artificial intelligence. It was concluded in this study that the individual innovativeness level of the nursing students was low; in other words, they adopted a traditionalist attitude and approached artificial intelligence cautiously. It is recommended that the existing curricula be restructured to strengthen the innovative perspective of nursing students and to understand, use, and develop artificial intelligence technologies in nursing, and nursing educators should upskill themselves in this field.
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