Integrating Generative Artificial Intelligence in Midwifery Education: Balancing Innovation, Ethics, and Academic Integrity

Abstract

Applications driven by large language models (LLMs) are reshaping higher education by offering innovative tools that enhance learning, streamline administrative tasks, and support scholarly work. However, their integration into education institutions raises ethical concerns related to bias, misinformation, and academic integrity, necessitating thoughtful institutional responses. This article explores the evolving role of LLMs in midwifery higher education, providing historical context, key capabilities, and ethical considerations. Using insights from a US-based midwifery program, it highlights strategies for responsible LLM integration, including faculty development, classroom applications, and policy updates. The discussion addresses challenges such as mitigating bias, preventing plagiarism, and fostering critical thinking while ensuring that LLM-fueled applications remain a tool rather than a substitute for student learning. Practical approaches, including faculty training and student guidance, offer a replicable framework for leveraging LLM tools in professional health education while maintaining academic standards and equity.