Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions
233
Zitationen
3
Autoren
2022
Jahr
Abstract
Abstract Background Artificial intelligence (AI) technology has recently seen rapid advancement, with an expanding role and scope in nursing education and healthcare. This study identifies the influence of AI ethics awareness, attitude toward AI, anxiety, and self-efficacy on nursing students’ behavioral intentions to use AI-based healthcare technology. Methods The participants included 189 nursing students in Gyeonggi-do, with data collected from November to December 2021 using self-reported questionnaires. We analyzed the data using the SPSS/WIN 26.0 program, including a t-test, Pearson’s correlation coefficient, and hierarchical multiple linear regression. Results The results revealed that AI ethical awareness (t = − 4.32, p < .001), positive attitude toward AI (t = − 2.60, p = .010), and self-efficacy (t = − 2.65, p = .009) scores of the third and fourth-year nursing students were higher, while their anxiety scores were lower (t = 2.30, p = .022) compared to the scores of the first and second-year nursing students. The factors influencing behavioral intention included a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22). The adjusted R 2 was 0.42. Conclusion It is necessary to inculcate a positive attitude toward AI and self-efficacy by providing educational programs on AI-based technology in healthcare settings.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.