摘要
Machine learning (ML), a core component of artificial intelligence (AI), is increasingly being used to assess children’s emotions and attention, with potential applications in developmental monitoring and early identification of neurodevelopmental conditions such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). This narrative review synthesizes studies published between 2012 and 2025 from PubMed, IEEE Xplore, and Web of Science. We examine multimodal data sources (including facial, speech, physiological, eye movement, and behavioral features) and computational approaches such as convolutional neural networks (CNNs), support vector machines (SVMs), and long short-term memory (LSTM) networks. These methods can capture behavioral and physiological signals and provide complementary information for assessing children’s emotional and attentional states, particularly in controlled settings. However, the current evidence remains heterogeneous, with many studies relying on limited or laboratory-based datasets, which may constrain real-world applicability. Key challenges include data bias, cross-cultural variability, ethical concerns, and the need for robust privacy protection and external validation. Recent work has explored integrating AI with virtual reality (VR), augmented reality (AR), and Internet of Things (IoT) technologies to support more adaptive monitoring systems. Nevertheless, these applications remain largely exploratory. Future research should prioritize real-world validation, pediatric-specific datasets, and interdisciplinary collaboration to better define the role of AI in children’s mental health and education.