Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach

Abstract

Chinese the WRITE Way (CtWW) is a pedagogical approach proposed by the authors to address the limitations of traditional Chinese character instruction, which primarily emphasizes repetitive stroke-order practice and rote memorization. Although such methods are effective for native Chinese-speaking children who are simultaneously developing language proficiency and literacy skills, they are often less engaging for non-heritage learners at the high school and college levels, who seek a more meaningful understanding of the writing system. As the only surviving independently invented writing system, Chinese preserves pictorial and conceptual features that reveal the original design logic of many characters, providing valuable learning cues for students accustomed to alphabetic languages. Building upon our previous work on knowledge annotation, this study investigates how structured character knowledge can be integrated with generative artificial intelligence to create personalized instructional materials. The proposed methodology represents stroke-level, radical-level, and etymological knowledge using JSON-based annotations within an expert-system architecture named CharActER. These structured knowledge representations are infused into prompts for large language models to generate contextualized, story-driven explanations that follow the Chinese the WRITE Way methodology. Two proof-of-concept case studies demonstrate the feasibility of the proposed framework, including explanations for the character 不 and a lesson involving the related characters 人、大、天. The results indicate that combining structured knowledge with generative AI produces coherent, personalized teaching materials that move beyond rote memorization, providing a scalable foundation for adaptive Chinese language instruction and demonstrating the value of integrating expert-system knowledge representation with large language models in language education.