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

Martin Qiang Zhao; Rajwol Chapagain; Andy Digh

doi:doi:10.11648/j.ijecs.20261104.16

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

Martin Qiang Zhao^*, Rajwol Chapagain, Andy Digh

Published in International Journal of Education, Culture and Society (Volume 11, Issue 4)

Received: 22 June 2026 Accepted: 27 July 2026 Published: 26 August 2026

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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.

Published in	International Journal of Education, Culture and Society (Volume 11, Issue 4)
DOI	10.11648/j.ijecs.20261104.16
Page(s)	164-171
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Chinese the WRITE Way, Meaning Annotation for Chinese Characters, Knowledge Infusion, Prompt Engineering, AI in Education

References

[1]	Wikipedia, CJK Unified Ideographs. Retrieved from https://en.wikipedia.org/wiki/CJK_Unified_Ideographs [Accessed May 2023]
[2]	Kuo L-J, Ku Y-M, Chen Z, Shih C-Y. Acquisition of Chinese characters: the impact of character properties and the contribution of individual differences. Applied Psycholinguistics. 2024; 45(6): 1114-1146. https://doi.org/10.1017/S0142716424000420
[3]	Chua, N. A., Tajudddin, A., Yingsoon, G., Zaid, C. Perceived Difficulties in Learning of Mandarin Among Foreign-Language Learners and Strategies to Mitigate Them. Journal of Business and Social Development 8(2): 43-52. https://doi.org/10.46754/jbsd.2020.09.005
[4]	Hsiang-Yu Hsiung, Yu-Lin Chang, Hsueh-Chih Chen, Yao-Ting Sung, Effect of stroke-order learning and handwriting exercises on recognizing and writing Chinese characters by Chinese as a foreign language learners, Computers in Human Behavior, 2017; 74: 303-310, ISSN 0747-5632, https://doi.org/10.1016/j.chb.2017.04.022
[5]	Li, X., Cutting, J. (2011). Rote Learning in Chinese Culture: Reflecting Active Confucian-Based Memory Strategies. In: Jin, L., Cortazzi, M. (eds) Researching Chinese Learners. Palgrave Macmillan, London. https://doi.org/10.1057/9780230299481_2
[6]	Zhao, M. Q., Digh, A. Chinese the WRITE Way: An Innovative Approach to Teaching Chinese Characters Through Stories Behind the Scripts. In: INTED2025 Proceedings, Proceedings of the 19th Annual International Technology, Education and Development Conference, Valencia, Spain (2025). https://doi.org/10.21125/inted.2025
[7]	Xu, S.: Shuowen Jiezi, Yunnan People's Publishing House (2019).
[8]	Tse, S. K., Marton, F., Ki, W. W., Loh, E, An integrative perceptual approach for teaching Chinese characters. Instructional Science 2007; 35(5): 375-406.
[9]	Zhao, M.Q., Chen, J. L., Digh, A.D. (2026). Codification, Annotation and Rule-Based Inferencing for CharActER: A Proposed Application for Teaching Chinese the WRITE Way. In: Arabnia, H.R., Deligiannidis, L., Amirian, S., Ghareh Mohammadi, F., Shenavarmasouleh, F. (eds) AI Revolution: Research, Ethics and Society. AIR-RES 2025. Communications in Computer and Information Science, vol 2723. Springer, Cham. https://doi.org/10.1007/978-3-032-13056-3_42
[10]	Zhao, M. Q.: “Using a Chinese Learning Application for Case Studies in OO Programming, Database Systems, and Software Engineering Courses”. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24). ICR 2024. Lecture Notes in Networks and Systems, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-031-65522-7_67 (2024).
[11]	DaoDe Jing (道德经, also known as Tao Teh Ching or the Book of Tao). Available from https://ctext.org/dao-de-jing [Accessed 26 March 2026]
[12]	Make Me a Hanzi – Free, open-source Chinese character data: https://www.skishore.me/makemeahanzi/ [Accessed May 2, 2025].
[13]	Vasisht, K., Ganesan B., Kumar, V., Bhatnagar, V. (2024). Infusing Knowledge into Large Language Models with Contextual Prompts. https://arxiv.org/html/2403.01481v1

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APA Style

Zhao, M. Q., Chapagain, R., Digh, A. (2026). Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. International Journal of Education, Culture and Society, 11(4), 164-171. https://doi.org/10.11648/j.ijecs.20261104.16

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ACS Style

Zhao, M. Q.; Chapagain, R.; Digh, A. Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. Int. J. Educ. Cult. Soc. 2026, 11(4), 164-171. doi: 10.11648/j.ijecs.20261104.16

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AMA Style

Zhao MQ, Chapagain R, Digh A. Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. Int J Educ Cult Soc. 2026;11(4):164-171. doi: 10.11648/j.ijecs.20261104.16

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@article{10.11648/j.ijecs.20261104.16,
  author = {Martin Qiang Zhao and Rajwol Chapagain and Andy Digh},
  title = {Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach},
  journal = {International Journal of Education, Culture and Society},
  volume = {11},
  number = {4},
  pages = {164-171},
  doi = {10.11648/j.ijecs.20261104.16},
  url = {https://doi.org/10.11648/j.ijecs.20261104.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20261104.16},
  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.},
 year = {2026}
}

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TY  - JOUR
T1  - Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach
AU  - Martin Qiang Zhao
AU  - Rajwol Chapagain
AU  - Andy Digh
Y1  - 2026/08/26
PY  - 2026
N1  - https://doi.org/10.11648/j.ijecs.20261104.16
DO  - 10.11648/j.ijecs.20261104.16
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 164
EP  - 171
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20261104.16
AB  - 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.
VL  - 11
IS  - 4
ER  -

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Author Information

Martin Qiang Zhao

Computer Science Department, College of Liberal Arts & Sciences, Mercer University, Macon, the United States

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Rajwol Chapagain

Computer Science Department, College of Liberal Arts & Sciences, Mercer University, Macon, the United States
Andy Digh

Computer Science Department, College of Liberal Arts & Sciences, Mercer University, Macon, the United States

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Plain Text BibTeX RIS

APA Style

Zhao, M. Q., Chapagain, R., Digh, A. (2026). Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. International Journal of Education, Culture and Society, 11(4), 164-171. https://doi.org/10.11648/j.ijecs.20261104.16

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ACS Style

Zhao, M. Q.; Chapagain, R.; Digh, A. Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. Int. J. Educ. Cult. Soc. 2026, 11(4), 164-171. doi: 10.11648/j.ijecs.20261104.16

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AMA Style

Zhao MQ, Chapagain R, Digh A. Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach. Int J Educ Cult Soc. 2026;11(4):164-171. doi: 10.11648/j.ijecs.20261104.16

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@article{10.11648/j.ijecs.20261104.16,
  author = {Martin Qiang Zhao and Rajwol Chapagain and Andy Digh},
  title = {Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach},
  journal = {International Journal of Education, Culture and Society},
  volume = {11},
  number = {4},
  pages = {164-171},
  doi = {10.11648/j.ijecs.20261104.16},
  url = {https://doi.org/10.11648/j.ijecs.20261104.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20261104.16},
  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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Knowledge Annotation and Infusion for a Chinese Teaching AI: An Expert System Approach
AU  - Martin Qiang Zhao
AU  - Rajwol Chapagain
AU  - Andy Digh
Y1  - 2026/08/26
PY  - 2026
N1  - https://doi.org/10.11648/j.ijecs.20261104.16
DO  - 10.11648/j.ijecs.20261104.16
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 164
EP  - 171
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20261104.16
AB  - 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.
VL  - 11
IS  - 4
ER  -

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