This workshop brings together leading researchers from across the social sciences and humanities to explore the practical realities of integrating AI into qualitative and historical research. Experts from a variety of fields, including sociology, history, geography, and political science, will provide candid, nuts-and-bolts accounts of how they use AI tools in their work, from leveraging large language models to process archival texts and spatial data, to using generative AI for coding and classification of qualitative data, to grappling with document digitization and data linkage. They will address the practical challenges that arise at every stage of the research process, including crafting effective prompts, interpreting and validating AI-generated output, detecting bias, and knowing when AI helps and when it gets in the way.
The workshop is co-sponsored by the Centre for the Study of the United States and the Data Sciences Institute
| May 13, 2026 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9:00-9:10 |
Introductions |
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9:10-9:55 |
Talk title TBD S. Wright Kennedy, Assistant Professor, Department of History, University of South Carolina |
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10:00-10:45 |
Talk title TBD Sergio Gabiel Petralia, Assistant Professor, Utrecht University |
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10:50-11:35 |
Talk title TBD Ian Miligan, Associate Vice-President, Research Oversight and Analysis, Professor, Department of History, University of Waterloo |
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11:35-11:45 |
Break
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11:45-12:30 |
Panel Discussion
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12:30-1:30 |
Lunch
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1:30-2:15 |
Talk title TBD Alina Arseniev-Koehler, Assistant Professor, Department of Sociology, Purdue University |
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2:20-3:15 |
Talk title TBD Daniel Karell, Assistant Professor, Department of Sociology, Yale University |
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3:15-3:25 |
Break |
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3:25-4:10 |
Panel Discussion |
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4:10-4:30 |
Concluding Thoughts |
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FAlina Arseniev-Kohler
Department of Sociology, Purdue University
Professor Alina Arseniev-Koehler is a computational and cultural sociologist with substantive interests in language, health, and social categories. Alina strives to clarify core concepts and debates about cultural meaning in sociology. For example, how do individuals learn and deploy stereotypes? Empirically, Alina focuses on cases where meaning is linked to inequality and health, such as the moral meanings attached to body weight, the stigmatizing meanings of disease, and gender stereotypes. To investigate these topics, Alina uses computational methods and machine learning, especially computational text analysis.
Daniel Karell
Department of Sociology, Yale University
Professor Daniel Karell’s research uses computational and quantitative methods to examine the intersection of social movements, culture, and technology. For example, some projects investigate how people’s interactions with AI can influence their understanding of the social world, including their perceptions of history and behavior towards people in different social groups. Another project analyzes the social and cultural dynamics of backlash, with a focus on the Blue Lives Matter movement during 2020. Daniel’s research has appeared in several academic journals, including American Sociological Review, Sociological Methods & Research, Sociological Methodology, and PNAS Nexus. His work has won awards from the American Sociological Association’s section on Collective Behavior and Social Movements and the Journal of Peace Research. In recent years, Daniel has been a Weatherhead Scholar at Harvard University and a Fung Global Fellow at Princeton University. At Yale, Daniel is a faculty affiliate of the Institute for Foundations of Data Science and the Institution for Social and Policy Studies. He is also a co-organizer of the Computational Social Science Workshop.(Link is external) Daniel teaches courses on integrating AI into social science research methods, computational approaches to studying culture, and the sociology of backlash.
S. Wright Kennedy, University of South Carolina
Department of History, University of South Carolina
Professor S. Wright Kennedy specializes in public-facing spatial history projects, and he uses geographic information systems (GIS) and spatial analysis to study past and present health, environmental, and socioeconomic issues. Professor Kennedy has investigated a wide range of spatial history topics with GIS, including epidemics, streetcar corruption, hurricane recovery, residential segregation, and environmental injustices. Previously, he led the Mapping Historical New York (mappinghny.com) project for four years as a postdoctoral scholar at Columbia University and served as project manager for three years on the imagineRio project (imaginerio.org) at Rice University. He has a PhD in History, an MA in Geography, and is a certified GIS Professional (GISP). Professor Kennedy’s teaching interests include spatial history methods, public history, and the history of public health. He is working on his first monograph, tentatively titled Separate but Dead, which examines the rise of residential segregation in New Orleans at the end of the 19th Century and the unequal burdens of disease that segregation created.
Ian Milligan
Associate Vice-President, Research Oversight and Analysis,
Professor, Department of History, University of Waterloo
Professor Ian Milligan has authored or co-authored six books, most recently Averting the Digital Dark Age (Johns Hopkins, 2024). As Associate Vice-President, Research Oversight & Integrity, he provides campus-wide leadership across research ethics, compliance, safeguarding, and research data management, co-leading the university’s RDM strategy through to implementation. As PI of the Archives Unleashed project (2017–2023), he worked with and helped lead an interdisciplinary, multi-institution research team whose work is now offered as a service by the Internet Archive.
Sergio Petralia
Economy Geography, Utrecht University
Professor Sergio Petralia is currently working on issues related to technological change and innovation. His most recent research projects study the emergence and spatial concentration of new technologies using historical data on patent activity, the identification of the challenges and opportunities for technological development in developing economies, and the impact of disruptive technological change on income and wages.
