Integrating GenAI into Flipped Learning via Bloom’s Taxonomy in Postgraduate Nursing Education: A Qualitative Study

Background/Objectives: Generative artificial intelligence (GenAI) presents immense potential to advance flipped learning approach. Yet, it is under-explored. This study presents the integration of GenAI into a nursing leadership course, guided by Bloom’s taxonomy, and explores the learning experiences of postgraduate nursing students in this GenAI-integrated flipped learning approach. Methods: A descriptive qualitative design was employed, and individual semi-structured interviews were conducted with 9 postgraduate nursing students who had completed a nursing leadership course delivered via a GenAI-integrated flipped-learning approach. Results: Three interconnected themes emerged: (1) GenAI supplements in-person learning, (2) Direct and Socratic questioning, and (3) Factors contributing to GenAI learning experience. Students appreciated that GenAI helped them summarize and understand the learning material, thereby promoting meaningful in-class activities. Students highlighted direct and Socratic questioning as the major approaches in using GenAI. Notably, Socratic questioning, AI-driven thought-provoking dialogue, helped develop higher-order thinking. However, suboptimal prompting techniques was the barrier, while channels for learning prompting and integrating it into the curriculum were facilitators. Conclusions: This study provides evidence on integrating GenAI into higher education. It also provides insights into the potential use of Socratic questioning to develop higher-order thinking in postgraduate nursing education.