Construction of Culture Sensitive Obstetrics and Gynecology Nursing Teaching Model Based on Humanoid Robots

Journal: Advanced Journal of Nursing DOI: 10.32629/ajn.v6i3.4397

Pengying Yue, Kailing Wang, Huimin Li, Wenyao Yan, Jimei Li

Xi'an Innovation College of Yan'an University, Xi'an 710100, Shaanxi, China

Abstract

Objective: To develop a humanoid robot-based teaching model for culturally sensitive obstetric and gynecological nursing, thereby providing a theoretical framework for innovating nursing education methodologies. Methods: A systematic search and thematic synthesis of literature published between January 2015 and May 2025 was conducted across major databases, including PubMed, Web of Science, CINAHL, CNKI, Wanfang, and VIP. Results: Leveraging their capabilities for multilingual interaction, emotion recognition, and behavioral simulation, humanoid robots can create highly simulated cross-cultural nursing scenarios. This study integrated Leininger's Culture Care Theory and constructivist learning theory to construct a three-dimensional integrated teaching model encompassing "Culture-Technology-Pedagogy." This model includes a spiral curriculum design, a human-robot collaborative teaching mechanism, and specific technological implementation pathways. It demonstrates potential for effectively enhancing nursing students' cultural awareness, cross-cultural communication skills, and empathy. However, its application faces challenges such as high technical costs, ethical risks, and the standardization of cultural scripts. Conclusion: The humanoid robot-based teaching model for culturally sensitive obstetric and gynecological nursing shows significant application potential. Future efforts should focus on strengthening interdisciplinary collaboration, optimizing algorithm design, and conducting empirical research to promote its deeper integration into nursing education.

Keywords

Humanoid Robot; Cultural Sensitivity; Obstetric and Gynecological Nursing; Nursing Education; Teaching Model

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Copyright © 2025 Pengying Yue, Kailing Wang, Huimin Li, Wenyao Yan, Jimei Li

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