Conference session

Universities entrust faculty-led admissions committees with decisions that ultimately shape who gains access to educational opportunity. Yet the governance processes through which these committees are constructed, and the underlying logics that guide this work, remain largely under investigated. How, then, might researchers responsibly study these opaque institutional processes? This presentation explores how generative artificial intelligence can help clarify these dynamics while expanding the methodological toolkit available to humanities and social sciences (HSS) researchers. Drawing on a study of faculty leaders at two Canadian universities, the research used committee-member biographies, generated with the support of generative AI, as standardized research stimuli in semi-structured interviews examining how admissions committees are assembled. The resulting data were analyzed using Iterative Thematic Inquiry and multidimensional (dual) scaling, revealing patterns of implicit homophily, identity-based clustering, and divergent rationales across disciplinary and demographic groups, often reflecting participants’ social backgrounds and lived experiences. Beyond the empirical findings, the session engages a broader methodological question: how can AI support, not supplant, human interpretation while enabling new and more dynamic forms of inquiry into complex and politically sensitive organizational processes? By generating controlled simulation environments, AI can enable researchers to examine decision-making dynamics that are otherwise difficult to study due to confidentiality and access constraints. Participants will leave with a transferable methodological framework for using AI-assisted simulation to study governance and evaluation processes across contexts, including peer review processes (e.g., hiring and promotion) as well as scholarship and fellowship adjudication.