AI AS A DIGITAL SCAFFOLD: AN INTEGRATIVE REVIEW OF VYGOTSKY'S ZONE OF PROXIMAL DEVELOPMENT IN MODERN EDUCATION

Abstract

This paper conducts an integrative review to synthesize current research on the intersection of Artificial Intelligence (AI), adaptive learning systems, and Vygotsky's Zone of Proximal Development (ZPD). It aims to clarify how AI functions as a "digital scaffold" and evaluate the extent to which current technologies align with the core principles of ZPD. A systematic search of academic databases was conducted for peer-reviewed literature published between 2020 and 2025. The review analyzes and synthesizes findings from these sources to identify key themes, practical applications, and research gaps. The analysis reveals that AI-powered systems operationalize ZPD primarily through three mechanisms: (1) personalized learning paths that adapt content difficulty in real-time; (2) immediate, targeted feedback that corrects misconceptions; and (3) the facilitation of self-regulated learning. Recent literature indicates a shift towards using generative AI to also support collaborative and social learning, moving beyond purely individualized instruction. The paper highlights a gap between the theoretical potential of AI in education and its practical implementation, particularly concerning equity, teacher preparedness, and ethical data use. It concludes by proposing a framework for designing and evaluating AI tools based on Vygotskian principles and calls for more empirical research on the effectiveness of AI as a digital scaffold in real-world classroom settings.