<![CDATA[Despite remarkable advances in artificial intelligence that have produced large language models (LLMs) capable of mimicking human linguistic behavior with high levels of sophistication, these systems still fall significantly short of the abilities of children in acquiring language rapidly, flexibly, and contextually. This article presents a systematic review from the perspective of cognitive and developmental neuroscience to address the question of why children remain superior in language learning compared to artificial intelligence systems. By examining mechanisms such as neuroplasticity, the involvement of social affective circuits, reward prediction systems, and multimodal sensorimotor functions in child language development, the article argues that human language acquisition is a phenomenon deeply embedded in the body, social relationships, and biological drives-dimensions that artificial systems inherently lack. The implications of this review extend to clinical interventions, education, and the development of AI systems that more closely approximate human neurocognitive architecture.]]>
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