Factors Influencing Nursing Internship Students' Readiness to Use AI: Cross-Sectional Study Using Neural Network Analysis.

BACKGROUND: Enhancing nursing students' awareness, attitudes, beliefs, and preparedness toward AI may help improve their health care knowledge and practice. OBJECTIVE: This study aimed to assess nursing students' attitudes, perceptions, self-efficacy, barriers, and anxiety, which influence their readiness to adopt AI in nursing practice. METHODS: This study used a cross-sectional, correlational design. Data were collected from 307 nursing internship students using an 8-part, self-administered questionnaire. RESULTS: Increased self-efficacy with computers was correlated with decreased barriers to accessing AI technology, lower computer anxiety scale scores (r=-0.27, P<.001 and r=-0.57, P<.001, respectively), and higher perceptions of using AI (r=0.27, P<.001). Meanwhile, nursing students' readiness to adopt AI in nursing practice was negatively associated with barriers to accessing AI technology (r=-0.20, P<.001) and positively associated with attitudes toward and perceptions of using AI (r=0.32, P<.001 and r=0.14, P=.01, respectively). Increased barriers to accessing AI technology were associated with negative attitudes toward AI and nursing students' perceptions of using AI (r=-0.34, P<.001 and r=-0.39, P<.001, respectively). A multilayer neural network model identified barriers (relative importance=0.27), attitudes (relative importance=0.16), and perceptions (relative importance=0.15) as the most significant predictors, while self-efficacy (relative importance=0.11) and anxiety (relative importance=0.07) showed smaller contributions, despite nonsignificant bivariate associations with nursing students' AI readiness. The model demonstrated strong predictive performance, achieving a low relative error of 0.62 in the training set. The stability and generalization ability of the model were supported by the training and testing set results, which yielded a training sum of squares error of 65.93 and a testing sum of squares error of 35.49, showing no signs of overfitting. CONCLUSIONS: Several contributing factors influenced nursing students' readiness to embrace AI, with barriers, attitudes, and perceptions emerging as the most consistent, whereas self-efficacy and anxiety may play indirect roles. To improve the adoption of AI among nursing students, such factors should be dealt with in such educational programs; an interrelated adoption of AI in nursing practice is expounded as a more favorable environment.