AI in Children's Education: Applications of Machine Learning and Large Language Models, Benefits and Challenges

Abstract

Artificial Intelligence (AI) in education is transforming the way children learn by offering personalized learning experiences. In this paper, we survey AI-based educational tools and research published between 2020 and 2025, focusing on four core methods: supervised learning, unsupervised learning, reinforcement learning, and natural language processing through large language models. The review highlights the methods' contribution to early intervention, individualized instruction, and engagement through interactive technologies. A separate discussion section is provided for the interpretation of major findings. The paper highlights the virtues of engagement and accessibility, as well as challenges, particularly data privacy, bias, and cost. In this review, we consider the potential impact of AI on learning, as well as the risks it may introduce. Our nuanced review demonstrates that AI can safely accelerate the educational development of our youngest learners.

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