Exploring the Application of Artificial Intelligence in Basic Education Through the Lens of Human Learning Mechanisms

To explore the value positioning of artificial intelligence (AI) in basic education, this paper reconstructs an educational framework centered on developing higher-order thinking based on the intrinsic logic of human learning. Through theoretical deliberation and literature analysis, it elucidates three fundamental characteristics of human learning: the understanding of the essence of tacit knowledge, the attention-driven mechanism of deep learning, and the inherent contextuality and embodied nature of cognition. It further argues that AI-driven transformations in knowledge production necessitate shifting foundational education goals from knowledge acquisition to cultivating higher-order competencies. Analysis reveals that current AI applications often deviate from the intrinsic demands of deep learning. Consequently, this paper redefines AI-supported foundational education pathways: constructing embodied, contextualized intelligent environments; emphasizing the foundational value of basic knowledge in supporting higher-order thinking; establishing learning mechanisms where knowledge and skills mutually construct and coexist; and designing human-centered auxiliary systems with information filtering capabilities.