Artificial intelligence readiness and its influencing factors among newly qualified nurses: a cross-sectional study

Introduction: This study investigated the artificial intelligence (AI) readiness of newly qualified nurses and identified potential influencing factors. The technology acceptance model was extended by including perceived barriers to provide a comprehensive understanding of AI adoption in clinical practice. Methods: This cross-sectional study was conducted across four tertiary grade A hospitals in Shandong Province in August and September 2022. Using convenience sampling, 329 newly qualified nurses with 1-3 years of clinical experience were surveyed. Data were collected using several instruments: a demographic characteristics questionnaire, the Readiness to Adopt AI in Nursing Practice Scale, the Perceived Usefulness in Nursing Practice Scale, the Perceived Ease of Use in Nursing Practice Scale, and the Perceived Barriers to Accessing AI Technology Scale. Data analysis, including descriptive statistics, correlation analysis, and multiple linear regression, was performed using SPSS 27.0. Results: = 0.018). Although perceived barriers did not significantly predict readiness in regression analysis, they were widely prevalent in clinical practice, with a lack of AI knowledge and limited computer skills reported as common obstacles. Discussion: The readiness of newly qualified nurses for AI is influenced by multiple factors. Awareness of AI plays a crucial role, in addition to perceived ease of use and prior AI training. Although perceived barriers did not show a significant relationship with readiness, practical challenges, such as knowledge gaps and limited computer skills, require attention. Enhancing AI training, improving system usability, and ensuring adequate time and resource support are essential to strengthen AI application capabilities among newly qualified nurses.