Towards Socially Legitimized Trust in AI Learning Systems
Paper Title: Towards Socially Legitimized Trust in AI Learning Systems Authors: Sanket Ramchandra Patole 1 , Rob E. Carpenter 1 , Reed M. Milewicz 2 , Rose M. Baker 3 , and John R. Turner 4, 1University of Texas, USA, 2USA, 3University of North Texas, USA, 4Texas A&M University, USA Abstract: AI-enabled learning technologies are rapidly becoming institutional infrastructure. In this context, “trust” cannot be treated as a simple user attitude or a byproduct of technical performance. We argue that durable trust in AI-enabled learning systems is fundamentally a legitimacy outcome: stakeholders must judge AI mediated practices as appropriate, credible, and defensible within the normative and governance structures of learning environments. Building on socio-technical systems scholarship and institutional legitimacy theory, we introduce Socially Legitimized Trust (SLT), a framework that helps us understand adoption stability and contestation through alignment among thre...
