AI-Mediated Learning and the Restructuring of Interpretive Cognition: A Developmental-Critical Model for Social Sciences and Humanities Education

This study examines how generative artificial intelligence (AI) reshapes interpretive cognition in Social Sciences and Humanities (SSH) education. Moving beyond instrumental perspectives, the paper conceptualizes AI as an epistemic mediator that reconfigures meaning-making processes, authorship, and learner agency. The study adopts a qualitative, conceptually driven design combining systematic interdisciplinary synthesis with structured classroom observation (N = 42 interactional episodes, 27 students, 6 instructional sessions). An abductive analytical approach was used to identify patterns of learner – AI interaction and to construct a developmental-critical model of interpretive learning. Findings reveal three interconnected transformations: the externalization of interpretive cognition, the hybridization of authorship, and the emergence of distributed epistemic agency. These correspond to three developmental trajectories – AI-dependent, AI-enhanced, and AI-critical interpretation – reflecting increasing levels of interpretive autonomy. The study argues that AI does not merely support learning but fundamentally restructures the cognitive and epistemic architecture of interpretation. In response, a developmental-critical pedagogical model is proposed, operationalized through six checkpoints designed to scaffold critical AI literacy, epistemic responsibility, and interpretive autonomy. The article contributes to ongoing debates on AI in education by providing a theoretically grounded and empirically informed framework for integrating AI into SSH pedagogy without compromising interpretive depth.