<p>As emerging technologies gradually integrate into healthcare, smart technologies have profoundly impacted clinical practice and educational demands. Nursing staff, as frontline caregivers in healthcare, and nursing students, as key members of future healthcare teams, play crucial roles in the medical field. In this study, a draw-a-picture technique and epistemic network analysis were used to investigate whether significant differences existed between 52 nursing students’ and 42 nursing staff’s learning motivation and conceptions of smart technologies in medical contexts. The findings showed that nursing staff had significantly higher learning motivation than nursing students. Both groups held similar viewpoints regarding the participants and the nursing activities involving smart technologies in medical contexts, particularly in terms of disease prevention and control. However, nursing staff associated the locations and objects of the contexts with actual work environments and procedures, demonstrating their clinical experience. On the other hand, nursing students paid attention to“personal computers” and “pharmaceuticals,” indicating their idealized conceptions of smart technologies in medical contexts. The ENA results revealed that intelligent systems played a central role in assessment and evaluation as well as prevention and control in the models of nursing staff. In contrast, analytics and prediction exhibited a relatively stronger connection with other nursing activities in the models of nursing students. Contrary to common assumptions, nursing students demonstrated more idealized and less contextually grounded conceptions of smart technologies, while nursing staff articulated richly contextualized and practice-informed understandings. This suggests that conceptual differences, beyond motivational variations, are shaped by professional experience, as revealed through our novel combination of the draw-a-picture technique and EpistemicNetwork Analysis.</p>