Introduction to the IMLS-funded Project ALiT
Updated: Sep 14
Program Introduction
The University of North Texas (UNT) College of Information, in partnership with the Amigos Library Services, requests $382,268 for a three-year workforce development project titled “ALiT: AI Platform for Librarian Lifelong Training.” This project directly supports the Laura Bush 21st Century Librarian Program (LB21) Goal 2 (Train and retain current library and archives professionals, faculty, and staff) & Objective 2.1 (Develop or enhance professional development and training programs to enable the library and archival workforce to meet the needs of their communities) by creating an innovative framework for the training and professional development of the library
workforce.
By leveraging generative AI to simulate complex service encounters, ALiT provides a scalable solution to enhance the skills, confidence, and resilience of public librarians. Rather than replacing professional judgment, ALiT positions generative AI as a simulation and reflection tool that supports knowledge sharing, experiential learning, ethical decision-making, and reflective practice. ALiT is designed as an on-demand, flexible training environment, enabling public librarians to practice professional service skills anytime and anywhere, without constraints of schedule, location, or the availability of trainers or patrons. The project team also built a prototype to demonstrate the feasibility of using generative AI to simulate library service interactions and provide structured feedback to support professional training.
Overall, ALiT is guided by the following research questions (RQs):
RQ1: How can generative AI simulate realistic public library service interactions that support professional training and reflective practice?
RQ2: How does scenario-based simulation training affect librarians’ confidence, communication skills, and preparedness for complex service encounters?
RQ3: How can knowledge and experiences from different libraries and librarians be integrated into the AI system to facilitate inter-institutional and inter-generational knowledge sharing?
RQ4: What best practices support the ethical, human-centered use of generative AI for librarian training and workforce development?
Project Team
Faculty members
Dr. Haihua Chen is Assistant Professor of Data Science and Director of the Intelligent Data Engineering and Analytics Lab at UNT. His research focuses on artificial intelligence, LLMs, information retrieval, question answering, and summarization, and he has published more than 60 peer-reviewed articles in leading venues such as EMNLP, WWW, ICDM, JCDL, ASIS&T, and IP&M. Dr. Chen will lead the project’s AI platform design, simulation architecture, feedback analytics, and overall technical integration.
Co-PI Dr. Katherine E. Klein
Dr. Katherine E. Klein is Assistant Professor of Library Science at UNT. Her research focuses on public library staff, workplace technology, professional identity, routines, and autonomy. She also brings direct professional library experience as a librarian, library media specialist, and research and teaching librarian. Dr. Klein will lead efforts in competency mapping, scenario authenticity, practitioner-centered evaluation, and the study of how librarians experience AI-supported training in practice.
Co-PI Dr. Junhua Ding
Dr. Junhua Ding is the inaugural Dean of the University of Wyoming School of Computing. He served as Reinburg Endowed Professor and Department Chair of Data Science at UNT with more than two decades of experience spanning software engineering, data quality, and AI evaluation. With prior industry experience as a software engineer, along with active NSF-funded research, Dr. Ding brings the technical depth needed to ensure ALiT’s platform robustness, data quality, model evaluation, and responsible system performance. He will lead efforts in system reliability, evaluation design, and AI quality assurance.
Student Researcher
Miss Yuhan Zhou is a Ph.D. student in Information Science with a Data Science concentration at the University of North Texas (UNT). She earned her B.S. in Information Management from Zhejiang University in 2022, and her M.S. in Business Analytics from the University of Hong Kong in 2023. Her research focuses on data quality, including developing evaluation frameworks, dimensions, and metrics. Related work has been presented in the South Central Chapter of the Medical Library Association (SCC/MLA) Conference, IEEE Internet Computing, and so on.