<p>A foundational question in social theory concerns the mechanisms of institutional stability and change, that is, how field-wide orders are reproduced and transformed through individual-level practices. For the sociology of science, this translates to a specific question: how do the persuasive practices of researchers aggregate to shape the epistemic values of a field? Yet, systematically connecting individual-level persuasive claims to field-wide value change has remained a methodological and theoretical challenge. This paper addresses this gap by introducing the Persuasive Positioning Model (PPM), a framework that classifies the logics of persuasion into four distinct types: Empirical Superiority, Niche Construction, Methodological Virtue, and Structural Contribution. The PPM is operationalized through a case study of Artificial Intelligence (AI), using a large-scale computational analysis of 17,756 conference papers from the pre-revolution (2010–2012) and mature deep learning (2022–2024) eras. The findings document a fundamental value reconfiguration in AI, revealing a significant decline in the logic of Methodological Virtue and a corresponding rise in the logics of Niche Construction and Structural Contribution, while the logic of Empirical Superiority persisted as the field’s dominant organizing principle. These results are synthesized into a conceptual model of value reconfiguration defined by three interlocking dynamics: the Persistence of the dominant logic, the Emergence of competing counter-logics, and the resulting Pluralization of the field’s epistemic values. By connecting individual-level persuasive claims to field-wide shifts in epistemic values, the framework and the computational pipeline developed to operationalize it offer a replicable approach for studying how the values of scientific fields are constructed and reconfigured over time.</p>