Artificial Intelligence-Based Simulation Training in Midwifery Education: A Descriptive Cross-Sectional Study on Chatbot-Supported Medical History Taking

(1) Background: Midwifery students require practical training experience to learn how to perform a medical history. Simulation-based training, such as chatbot exercises using large language models like GPT, provide structured practice but require ongoing evaluation. This study explores German midwifery students’ views on using an AI chatbot simulating a pregnant woman, regarding usability, realism, and educational value. (2) Methods: Twenty-six students participated in a descriptive, quantitative cross-sectional survey, using a literature-based, self-developed questionnaire after interacting with the AI generative chatbot. Data were analyzed via SPSS 30.0, with results shown in a stacked horizontal bar chart. (3) Results: The findings indicate that students experienced no difficulties when interacting with the chatbot. Both the quality and realism of the conversations were evaluated positively. Chatbot training was perceived as helpful in supporting structured medical history interviews and the collection of relevant data but was not considered a substitute for practice with actors or real-life situations. (4) Conclusions: The findings suggest that the medical history chatbot offers midwifery students an innovative, flexible simulation for training. Students responded positively, and it may help develop structured history-taking skills. Further study is needed to determine if repeated chatbot use improves medical history collection skills.