AI-Driven Development of Authentic Chu Culture Texts in College English Teaching
Authentic texts have long been valued in language teaching, yet materials representing regional Chinese traditions remain scarce in college English courses. This paper examines how artificial intelligence can support the selection, adaptation, and classroom use of authentic texts drawn from Chu culture, the distinctive tradition of the middle Yangtze region during the Zhou period. Drawing on research in second language acquisition, intercultural competence, and educational technology, it proposes a workflow that combines machine assistance with systematic teacher review, and then considers task design, scaffolding, and assessment. Risks such as factual fabrication, cultural reduction, and lexical overload are also discussed. The paper concludes that artificial intelligence can enlarge the cultural range of college English reading only when its outputs are treated as drafts to be judged rather than as authoritative accounts, and when teachers retain responsibility for cultural fidelity.
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