AI-Driven Visual Scaffolding in Education: A Comprehensive Literature Review

Artificial Intelligence (AI) has revolutionized educational scaffolding, providing personalized, real-time learning support through machine learning (ML), natural language processing (NLP), reinforcement learning (RL), and computer vision (CV). AI-driven visual scaffolding enhances STEM education, language learning, and special education by offering dynamic feedback and adaptive instruction. However, significant challenges remain, including static scaffolding mechanisms, inadequate calibrated fading, over-reliance on AI assistance, limited metacognitive development, and concerns such as algorithmic bias and data privacy risks. This comprehensive literature review examines the evolution of scaffolding from human-led to AI-driven approaches, evaluates current AI-based implementations, and identifies critical research gaps—particularly in dysgraphia interventions. The study highlights the need for structured fading mechanisms and adaptive feedback to enhance AI-driven scaffolding’s effectiveness and inclusivity. By addressing these limitations, AI-powered scaffolding can transition from rigid, rule-based interventions to truly adaptive learning support systems, fostering cognitive development, independent problem-solving, and long-term knowledge retention. Future research should focus on integrating AI-driven adaptive scaffolding, implementing structured fading strategies, and conducting longitudinal studies to assess AI’s sustained impact on learning outcomes.