THE RELATIONSHIP BETWEEN KNOWLEDGE, ATTITUDES, AND PRACTICES REGARDING ARTIFICIAL INTELLIGENCE APPLICATION AMONG NURSING STUDENTS
Main Article Content
Abstract
Background: In the context of digital health transformation, elucidating the correlation among knowledge, attitude, and practice (KAP) regarding artificial intelligence (AI) application in learning among nursing students is essential for designing effective educational interventions. Objectives: To evaluate the correlation among KAP of AI application in learning among nursing students. Materials and methods: A cross-sectional descriptive study was conducted among 428 nursing students at the University of Medicine and Pharmacy at Ho Chi Minh City, using stratified random sampling by academic year. The data collection instrument was the "Exploring Knowledge, Attitudes, and Practices toward Artificial Intelligence" scale developed by Walid Al-Qerem (2023), translated into Vietnamese, with adequate content validity (S-VI/UA=0.97) and reliability (Cronbach's Alpha ranging from 0.71 to 0.84), comprising four parts: demographics, knowledge, attitude, and practice. Spearman correlation and multivariate regression were used to determine the relationships among KAP components, with statistical significance set at p < 0.05. Results: Spearman analysis indicated statistically significant positive correlations among KAP components (p < 0.05). Knowledge showed weak correlations with both attitude and practice, whereas attitude demonstrated a stronger correlation with practice than knowledge did (r = 0.304; p < 0.001). Conclusions: Attitude was a stronger statistically significant predictor of AI application practice than knowledge among nursing students. Training programs should transcend purely theoretical instruction, focusing instead on shifting perceptions, fostering trust, and building readiness to adapt to technology.
Keywords
Artificial intelligence, knowledge, attitudes, practices
Article Details
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