Training Future <scp>LIS</scp> Faculty in <scp>AI</scp> and Data Science through a Library Rotation Education Model

ABSTRACT As Library and Information Science (LIS) programs expand their curricula to include artificial intelligence (AI) and data science, there is a growing need to ensure that related technical skills are taught within the context of library practice. However, many AI and data science instructors have limited backgrounds in librarianship, leading to a disconnect for students applying these skills in library environments. To address this gap, our research team developed a Library Rotation Education Model, an innovative approach that provides PhD students with hands‐on experience applying AI and data science in real‐world library settings. The three types of libraries—academic, public, and school—served as living laboratories where students and librarians collaborated on research projects. In this panel, participating PhD students will share their experiences and key takeaways from conducting AI‐related research projects in diverse library contexts. The audience will be invited to share other examples of successful doctoral‐level experiential learning programs, fostering a broader discussion on how LIS doctoral education can better integrate academic research with the practical needs of information professionals in the era of AI and data science.