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Contrasting Nursing Students' and Working Staff's Conceptions and Motivation of Using Smart Technologies in Medical Contexts: A Draw-a-Picture Technique and Epistemic Network Analysis

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Title: Contrasting Nursing Students' and Working Staff's Conceptions and Motivation of Using Smart Technologies in Medical Contexts: A Draw-a-Picture Technique and Epistemic Network Analysis
Language: English
Authors: Hsin Huang, Yun-Fang Tu, Gwo-Jen Hwang (ORCID 0000-0001-5155-276X), Hui-Chen Lin, Dongpin Hu
Source: Education and Information Technologies. 2025 30(17):24057-24083.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 27
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Nursing Education, Nursing Students, Student Attitudes, Learning Motivation, Artificial Intelligence, Technology Uses in Education, Medical Services, Disease Control, Freehand Drawing, Allied Health Personnel, Network Analysis, Epistemology
DOI: 10.1007/s10639-025-13746-8
ISSN: 1360-2357
1573-7608
Abstract: 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 Epistemic Network Analysis.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501276
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 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 Epistemic Network Analysis.
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