MEASURING FEAR OF ARTIFICIAL INTELLIGENCE AMONG NURSING STUDENTS: SCALE DEVELOPMENT AND VALIDATION

Abstract

Background: The integration of Artificial Intelligence (AI) in healthcare is transforming clinical practice by enhancing accuracy, efficiency, and patient care. Despite these advancements, AI adoption has triggered concerns among nursing students regarding job security, reliance on technology, and the erosion of human-centered values such as empathy and compassion. To date, no standardized tool exists for measuring fear of AI among nursing students.

Materials and Methods: A cross-sectional study was conducted among 140 fourth-year BS Nursing students from Shalamar Nursing College, Rashid Latif Nursing College (RLNC), Saeeda Waheed FMH College of Nursing (SWCON), Doctors Hospital College of Nursing, and Inspire College of Nursing and Health Sciences, Lahore. Scale development followed a systematic process guided by a literature review. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were employed to evaluate construct validity, while content validity was ensured through expert panel review. Reliability was assessed using Cronbach’s alpha.

Results: Exploratory and confirmatory factor analyses supported a stable two-factor structure of the FtAIS, with factor loadings ranging from 0.62 to 0.85 and fit indices (CFI = 0.90, RMSEA = 0.075, SRMR = 0.057) demonstrating acceptable model adequacy. All items showed strong corrected item-to-total correlations (0.66–0.83) and good internal consistency, confirming the reliability and construct validity of the scale among nursing students with limited AI exposure.

Conclusion: The study confirms that the FtAIS is a valid and reliable tool for measuring nursing students’ fear of artificial intelligence, even in groups with limited prior exposure to AI technologies