A Big Data Analysis of Staged Learning in Chinese Textbook Compilation Using AI Agent

By leveraging artificial intelligence (AI) agents, this study conducts a data-driven analysis of classical Chinese poetry and prose selections across the entire K-12 curriculum. We design multiple AI agents to identify, extract, and translate classical Chinese texts, enabling a quantitative examination of the evolution of key textual features — including length, character frequency, semantic content, and dynastic distribution — across primary school, junior middle school, and senior high school levels. Our findings reveal a clear developmental trajectory: a formal progression from short poetry to lengthy prose; a thematic shift from concrete, sensory imagery toward abstract, philosophical discourse; and the formation of a cultural canon centered on Tang and Song poetry while maintaining historical continuity. These results suggest that textbook compilation is a sophisticated process informed by cognitive load theory, pedagogical sequencing, and cultural transmission, extending well beyond the goal of language skill acquisition.