Computational Foundations and Educational Impacts of AI in Child-Centered Learning

Abstract

Artificial intelligence (AI) technology is gradually expanding from experimental prototypes to large-scale deployment, profoundly impacting how children learn, interact, and apply knowledge. Existing educational research focuses on the impact on teaching, but the technical foundations of AI systems - including Transformer-based natural language models and reinforcement learning engines for personalized content distribution - also fundamentally shape educational outcomes. This paper systematically explores the role of AI in enhancing children's learning from a computational perspective, focusing on the algorithmic architecture, system design, and data processing used in intelligent tutoring systems, adaptive learning platforms, and multimodal feedback agents. We propose a four-layer computational impact model covering: the algorithmic layer, involving natural language processing, knowledge tracking, and reinforcement learning methods; the system layer, focusing on the integrated architecture of multi-component AI education platforms; the data layer, including learning analysis under privacy protection, labeling challenges, and fairness modeling; and the application layer, exploring the implementation and interaction of related technologies in real classrooms and child-centered scenarios. This study systematically identifies key opportunities in AI education applications from a computer science perspective such as fine-grained personalization, multimodal feedback, and automated scaling, while also warning of associated risks including algorithmic bias, lack of adversarial robustness, and opaque decision-making). This analysis aims to provide a theoretical framework and practical guidance for subsequent research at the intersection of AI and education, promoting the construction of computationally sound, ethically compliant, and pedagogically effective intelligent education systems.