The Architecture of AI-Mediated Learning: A Three-Layer Framework

Artificial intelligence (or AI) is rapidly transforming digital learning environments, reshaping how educational processes are organized, how knowledge is produced, and how learning is evaluated. Despite a growing body of research on AI in education, existing studies often examine technological, pedagogical, and ethical dimensions in isolation, leaving a lack of integrative frameworks capable of explaining how AI restructures learning environments as a whole. This study addresses this gap by proposing a three-layer conceptual framework that models AI-mediated learning environments through the interaction of efficiency, pedagogy, and ideology. The framework conceptualizes AI integration as a system of interdependent processes: the efficiency layer captures the optimization of educational activities through automation and data-driven personalization; the pedagogical layer explains how AI reshapes learning processes, feedback cycles, and learner strategies; and the ideological layer examines the normative assumptions embedded within AI systems, including issues of epistemic authority, linguistic norms, and algorithmic bias. Drawing on a structured synthesis of recent empirical research across domains such as generative AI tools, automated feedback systems, intelligent tutoring systems, and AI-supported assessment, the study demonstrates how these dimensions interact to structure contemporary digital learning environments and generate both affordances and tensions. The main theoretical contribution lies in advancing a system-level analytical framework that moves beyond tool-specific approaches and enables a more integrated understanding of AI in education. In practical terms, the framework provides educators and policymakers with a lens to critically evaluate AI integration, supporting more informed decisions on assessment design, sustainable learning practices, and inclusive digital education.