Free ticketed event
As libraries navigate the integration of generative AI into their services and operations, one of the most promising applications is its use with data-driven evaluation and insight. This workshop offers an accessible, hands-on introduction to using prompt engineering with generative AI models to create Python code for analyzing library data. While many professionals in galleries, libraries, archives, and museums are already experimenting with ChatGPT or similar tools, fewer have had the opportunity to work with generative models as coding assistants. This workshop bridges that gap by demonstrating how AI can support Python-based text analysis, without requiring any deep programming experience. Using the freely accessible Google Colab platform, participants will learn how to generate, evaluate, and run code that performs tasks such as data cleaning, keyword extraction, time-based analysis, and basic natural language processing (e.g., sentiment analysis or named entity recognition). The hands-on portion of the session will guide participants through working with a pre-prepared, de-identified chat transcript dataset. The workshop concludes with a guided discussion on interpreting results and applying insights in context.
Eric brings experience supervising a team of STEM librarians, conducting applied research on library chat data, and leading AI training initiatives for academic library staff. Along with collaborators, he has developed AI personas for staff development and regularly designs instruction at the intersection of data science, user services, and responsible innovation.
Jason brings experience leading AI curriculum initiatives, teaching entrepreneurship and statistics, and supporting student success as Associate Director for Academic Excellence at The Polytechnic School at Arizona State University. He regularly teaches at the intersection of humanities, engineering, and ethics, and has launched programs in online education, regional entrepreneurship, and interdisciplinary AI literacy.