Reframing Information Seeking in the Age of Generative AI: A Critical and Humanistic Approach

Information-seeking has long been the subject of theoretical modeling, often drawing from cognitive, behavioral, computational, and even evolutionary perspectives to explain how individuals navigate, filter, and utilize information. Several dominant frameworks—Carol Kuhlthau’s Information Search Process, Marcia Bates’ Berrypicking Model, Peter Pirolli & Stuart Card’s Information Foraging Theory, Kiyohiko Nakamura’s Information Criteria framework, and Ian Ruthven’s Information Shaping Theory —have provided structured ways of understanding how people interact with information environments. However, while these frameworks offer valuable insights, they often operate within mechanistic or efficiency-driven paradigms, which risk overlooking the complex, embodied, and socioculturally situated nature of human information behaviors. These efficiency-oriented models, while useful in certain contexts, do not sufficiently account for the deeply affective, iterative, and socially embedded nature of information-seeking. A more humanistic and queer approach to AI literacy resists the assumption that information retrieval is purely rational or goal-directed, instead emphasizing the ways in which knowledge production is relational, contingent, and shaped by power. Queer theory provides a productive lens for interrogating the normative assumptions embedded in traditional information-seeking frameworks, particularly their optimizationist paradigms and their tendency to privilege dominant epistemologies. By conceptualizing AI literacy through a queer lens, this paper advocates for an approach that foregrounds uncertainty, performativity, and embodied subjectivity—principles that are vital in an era where AI systems increasingly mediate access to and interpretation of knowledge.