The Double-Edged Sword Effect of AI Application Among Clinical Nurses Based on the Job Demands-Resources Model: Qualitative Study

Abstract Background AI is rapidly transforming clinical nursing, promising administrative relief and decision support. However, the frontline reality presents a double-edged sword effect, where technological empowerment is frequently offset by novel occupational burdens and technostress. Objective This study aims to theoretically deconstruct the bidirectional impacts of AI application among clinical nurses and identify buffering conditions, using the Job Demands-Resources (JD-R) theoretical framework. Methods A descriptive qualitative study was conducted across multiple general hospitals in mainland China. Using maximum variation and purposive sampling, semistructured in-depth interviews were conducted with registered nurses who actively use clinical AI systems. Data were analyzed using directed qualitative content analysis guided by predefined JD-R constructs. Results The analysis revealed 3 overarching domains comprising 9 main themes and 24 subthemes. On the gain path (job resources), AI empowered nurses through a workflow efficiency leap, clinical cognitive empowerment, and professional capital appreciation. Conversely, along the drain path (job demands), hidden costs were exposed, conceptualized as cognitive impediment, relational attrition, and digital involution driven by performance inflation and competitive perfectionism. The interplay between these pathways was perceived to be buffered by contextual mechanisms, specifically nurses’ proactive coping strategies, professional boundary demarcation, and the provision of a cohesive organizational support architecture. Conclusions The impact of AI integration in nursing is not technologically deterministic. While AI provides valuable cognitive and operational support, it concurrently generates novel digital demands. To prevent AI from devolving into an occupational hazard, health care administrators must establish multidimensional life cycle AI governance, cultivate comprehensive AI literacy, and safeguard the irreplaceable humanistic core of clinical care.