Optimists, Realists, and Traditionalists: Profiling Nursing Students' Engagement with Artificial Intelligence (AI) in Pediatric Nursing Education

Abstract

Background: Artificial intelligence (AI) is increasingly integrated into nursing education; however, its use in pediatric nursing courses remains underexplored. Aim: This study aimed to examine the use of artificial intelligence tools among nursing students enrolled in a pediatric nursing course and to identify homogenous student profiles based on their usage characteristics and theoretical adoption patterns. Methods: This cross-sectional study included 241 nursing students enrolled in a pediatric nursing course. Data were collected using the AI in Pediatric Nursing Course Questionnaire and the General Attitude Toward Artificial Intelligence Scale (GAAIS). K-means cluster analysis was conducted, guided by Rogers’ Diffusion of Innovations Theory, to classify students into distinct profiles based on their GAAIS subscale scores, which were considered to reflect their fundamental attitudinal orientations toward AI. Results: Cluster analysis identified three distinct student profiles: Optimists (n=95), Realists (n=102), and Traditionalists (n=44). Optimists demonstrated the highest levels of AI use, whereas Traditionalists demonstrated the lowest levels in both clinical practice (p=0.002) and preparation for clinical visits (p=0.039). Additionally, Optimists and Realists reported more positive perceptions of AI’s impact on examination success (p=0.002) and learning (p=0.001), whereas Traditionalists expressed significantly more negative attitudes toward AI overall (p<0.001) and reported the lowest levels of trust in the technology (p=0.040). Conclusion: Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes. These findings highlight the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.