Development and Psychometric Evaluation of the Attitudes Toward the Use of Artificial Intelligence in Nursing Care Scale.

Background and Purpose: The rapid expansion of artificial intelligence in health care is reshaping clinical workflows and decision-making, making it important to understand how future nurses perceive its use in nursing care. However, there is limited availability of nursing-focused instruments that specifically measure students' attitudes toward the use of artificial intelligence in the nursing care process. This study aimed to develop and psychometrically evaluate a scale that assesses nursing students' attitudes toward the use of artificial intelligence in nursing care. Methods: This scale development study, which included expert review, exploratory factor analysis, confirmatory factor analysis, and reliability analyses, was conducted with undergraduate nursing students using an online survey. Scale development followed standard steps, including item generation based on the literature, expert review to establish content relevance and clarity, pilot testing for comprehensibility, and psychometric testing to evaluate the scale's dimensional structure and internal consistency. Construct validity was examined through factor analytic approaches, and reliability was evaluated using internal consistency and split-half methods. Results: The final instrument consisted of a single-dimension set of items that collectively reflects nursing students' attitudes toward the use of artificial intelligence in nursing care. Analyses supported a coherent structure with strong item performance and high internal consistency, indicating that the scale measures the targeted construct reliably. Conclusions: The developed scale demonstrates sound validity and reliability for assessing nursing students' attitudes toward the use of artificial intelligence in nursing care. The scale can be used to guide curriculum development, evaluate educational initiatives, and monitor changes in students' perspectives over time. The results obtained using this scale can inform institutional and national strategies for digital health competency development and workforce preparation aligned with safe, ethical, and patient-centered care.