Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Nursing Students’ Attitudes Toward Artificial Intelligence: Palestinian Perspectives
14
Zitationen
9
Autoren
2025
Jahr
Abstract
Background Artificial intelligence (AI) is significantly transforming the nursing profession, enhancing patient care, and shaping future nursing practice. Understanding nursing students’ attitudes toward AI applications is crucial for its effective integration into clinical practice and education. Aim To evaluate nursing students’ attitudes toward AI in Palestine. Methods A cross-sectional design was conducted among 325 nursing students. Due to logistical constraints, data were gathered via online surveys using the AI attitude scale. The research was conducted between February and March 2024 at Arab American University in Palestine. Results The results showed that the average attitudes toward using AI in nursing practice scores (M = 61.81; SD = 9.74) were significantly greater than the neutral score ( p = .001). Nursing students have a positive attitude toward AI in terms of benefits and willingness to use it in nursing practice. However, nursing students have a neutral attitude toward the practical advantages of AI and exhibit a negative attitude toward the dangers of AI in nursing. Furthermore, gender, academic year, and purpose of AI had statistically significant differences in nursing students’ attitudes toward AI ( p = .034, .039, and 0.042 respectively). Female students showed higher levels of attitudes toward AI usage, while participants with master's degree participants had the lowest level of attitudes toward AI. Conclusion Our findings demonstrate that nursing students have a positive attitude toward the integration of AI into nursing and healthcare practice, along with significant intentions to utilize the technology. The results highlight the need for AI-focused training within nursing curricula.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.697 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.602 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.127 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.872 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.