Reconfiguring Authority and Trust: A Secondary Data Analysis of Student-Teacher Relationships in the Era of Generative AI

Abstract

The rapid integration of generative artificial intelligence (GenAI) into higher education has precipitated a fundamental reconfiguration of student-teacher relationships, challenging long-established patterns of pedagogical authority, mutual trust, and institutional legitimacy. This article presents a secondary data analysis of existing empirical research, systematic reviews, and policy documents to examine how GenAI is reshaping the relational dynamics between students and educators in academic settings. Drawing on a sociological framework that integrates Actor-Network Theory, Bourdieusian concepts of capital and habitus, and Foucauldian notions of surveillance, the analysis synthesizes findings from thirty peer-reviewed studies published between 2022 and 2025, alongside institutional policy documents and large-scale survey datasets. The findings reveal three interrelated mechanisms of relational reconfiguration: the erosion of interpersonal trust stemming from asymmetrical transparency requirements and cheating concerns; the redistribution of pedagogical authority as AI systems emerge as non-human actants mediating classroom interactions; and the emergence of new surveillance practices that intensify top-down monitoring while failing to address underlying structural inequalities. The analysis further demonstrates that GenAI does not affect all student-teacher relationships uniformly; rather, its impact is mediated by existing disparities in cultural, social, and economic capital, thereby exacerbating rather than alleviating educational inequalities. The article concludes by proposing a sociological research agenda for understanding AI-mediated pedagogy as a site of contested power relations, with specific policy implications for developing higher education ecosystems such as Pakistan.