Abstract

Objects: The integration of interdisciplinary approaches in nursing education is crucial for developing well-rounded healthcare professionals. However, traditional teaching methods often fall short in fostering the necessary competencies for effective interdisciplinary instruction. This study aims to explore how generative artificial intelligence (AI) can be leveraged to enhance the interdisciplinary teaching capabilities of nursing educators, addressing challenges such as over-reliance on technology, inadequate information security awareness, and imperfect human-AI collaboration.

Methods: A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews with nursing educators to assess their current interdisciplinary teaching practices and attitudes towards AI integration. The study involved 30 participants from various educational institutions, representing diverse backgrounds in terms of age, gender, ethnicity, teaching experience, and prior exposure to AI-based training. Data were analyzed using thematic analysis for qualitative responses and descriptive statistics for quantitative data. Additionally, a pilot program was implemented, where participants engaged with AI tools designed to support interdisciplinary course development, teaching delivery, and student assessment.

Results: The findings revealed that while nursing educators recognize the potential benefits of AI in enhancing their teaching practices, they also express concerns about over-dependence on technology and the need for robust information security measures. The pilot program demonstrated significant improvements in participants' abilities to design and implement interdisciplinary curricula, with AI facilitating the creation of personalized learning materials, dynamic case scenarios, and adaptive assessments. Educators reported increased confidence in integrating diverse disciplines and utilizing AI-generated insights to refine their teaching strategies. However, challenges related to human-AI collaboration, such as ensuring seamless integration of AI-generated content with existing teaching frameworks, were also identified.

Conclusion: Generative AI holds substantial promise for transforming nursing education by augmenting educators' interdisciplinary teaching competencies. By providing tools for course design, real-time feedback, and personalized learning, AI can empower educators to create more engaging and effective learning experiences. However, successful implementation requires careful consideration of educators' needs, ongoing support for professional development, and the establishment of clear guidelines for responsible AI use in educational settings. Future research should focus on refining AI algorithms to better align with pedagogical objectives and exploring long-term impacts on student outcomes and educator satisfaction. This study underscores the importance of a balanced approach, where AI serves as a valuable tool to complement, rather than replace, the expertise and human touch of nursing educators.