A Study on the Role Transformation of Local Chinese Language Teachers in Thai Secondary Schools Empowered by Generative AI
生成式AI赋能下泰国本土中学中文教师角色转变研究
DOI:
https://doi.org/10.20961/mandarinable.v5i2.4079Keywords:
Generative AI, International Chinese Education, SAMR Model, Teacher Role Transformation, Thai Local Secondary SchoolsAbstract
Driven by digital educational transformation, generative AI is profoundly reshaping traditional classrooms. Grounded in Thailand’s local educational reality, this study investigates the role transformation and practical challenges of local Chinese language teachers in Thai secondary schools under generative AI empowerment. By integrating the SAMR model with Bloom's Taxonomy, this study constructed a specialized behavioral analysis framework, based on which in-depth interviews and behavioral analyses were conducted with 15 in-service Chinese language teachers. The findings indicate that generative AI does not completely replace traditional teaching roles but instead facilitates a pragmatic and selective adaptation. Specifically, pre-class, teachers transition from content producers to human-AI co-creators, using AI to reduce workload while actively serving as quality controllers for instructional content. In-class, they shift from knowledge transmitters to smart teaching assistants, leveraging AI to enhance instructional efficiency while maintaining absolute classroom authority and order. Post-class, they evolve from homework graders to evaluators and reflective practitioners, establishing an efficient AI-screening and teacher-vetting workflow to reallocate the time saved toward deeper pedagogical reflection and humanistic care for students. Finally, targeted recommendations are proposed, ranging from government policy formulation on digital equity and ethical guidelines, through school environment upgrades involving infrastructure and the procurement licensed tools, to teacher training focused on prompt engineering and localized development capabilities, aiming to achieve an organic integration of technological efficiency and humanistic warmth.
References
Blundell, C. N., Mukherjee, M., & Nykvist, S. (2022). A scoping review of the application of the SAMR model in research. Computers and Education Open, 3, Article 100093. https://doi.org/10.1016/j.caeo.2022.100093
Common Sense Education. (2016, July 12). SAMR and Bloom's taxonomy: Assembling the puzzle.
Department for Education. (2024). Generative AI in education: Educator and expert views. UK Government.
Equitable Education Fund. (2026, January 17). The crisis of small schools: "Marathon teaching" exceeds the standard by 37.6%, urgent proposal to hire "specialized professionals" to reduce teacher burden.
Gou, D. (2025). The potential, challenges, and pathways of generative artificial intelligence in empowering the professional development of international Chinese language teachers. Journal of Current Social Issues Studies, 2(2), 104–115. https://doi.org/10.71113/JCSIS.2025v2i2.104-115
Kaewbut, P. (2021). Conditions and problems of teaching and learning Chinese in public schools at secondary level in the southern region. Journal of Humanities and Social Sciences Suratthani Rajabhat University, 13(1), 58–83.
Ketusiri, A., Ketusiri, N., Senket, S., et al. (2025). The artificial intelligence (AI) for teaching and learning management in the 21st century. Journal of Research Innovation for Society, 1(1), 44–51.
Matichon Weekly. (2026, January 26). The crisis of teacher overwork: An endless epic problem.
Nyaaba, M., & Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1–13. https://doi.org/10.61969/jai.1385915
Putkorn, N. (2025). The role of Artificial Intelligence (AI) in Chinese language learning management. Journal of Humanities and Social Sciences, Rajapruk University, 11(3), 21–39.
Thavirath, J. (2025). AI and education: The ultimate intelligent assistant for enhancing learning. Journal of Social Studies Perspectives, 1(2), 98–108. https://doi.org/10.64186/jsp1310
Yamrung, R., Suthasinobol, K., & Xu, J. (2024). Problems and suggestions for the teaching Chinese language at the Mattayomsuksa level in Thailand. Journal of Home Economics, 6(71), 181–195.
陈思迪, 沈薇薇. (2025). 生成式人工智能时代高校教师的角色转变. 高教论坛, (09), 6–9.
耿媛娜, 祖菲娅·吐尔地. (2025). 人工智能时代体育教师角色转变与能力提升路径研究. 中国体育科学学会. 第十四届全国体育科学大会学术成果汇编——墙报交流(体育信息分会), 155-157.
何保兴. (2025). 人工智能环境下大学英语教师角色转变与教学策略优化研究. 现代英语, (08), 25–27.
侯奕扬, 张丽丽. (2026). 人工智能背景下思政课教师角色转变三维探讨. 中学政治教学参考, (03), 88–91.
黄超. (2025). AI赋能教师专业发展:数字化转型中的角色重塑与能力提升. 学园, 18(18), 68–70.
李晓玫, 王其寓. (2025). 国际中文教师对生成式人工智能的接纳意愿及其影响因素探究. 广西社会科学, (06), 212–221.
刘秀琴, 冯洁, 张建婷. (2026). 数智时代成人英语教师角色重塑:从授业者到生态构建者. 山西开放大学学报, 31(01), 58–62.
马宁, 范本超, 唐千惠. (2025). 人机协同视角下的教师角色转变与能力提升. 教师发展研究, 9(02), 28–35.
王焕, 徐晓燕. (2026). 生成式人工智能时代教师角色的转变逻辑、特征与重塑路径探析. 成都航空职业技术大学学报, 42(01), 46–50.
王晓旭. (2025, April 21). 人工智能时代高校教师角色转变与能力提升策略. 中国工业报, 20.
吴和林, 杨会云. (2025). 智能时代教师角色转变与发展的重点方向. 建设教育强国, (07), 2–5.
肖锐, 赵婉莹, 施浩然, 杨蓉. (2025). 人工智能赋能国际中文教育研究热点可视化分析. 普洱学院学报, 41(03), 108–118.
曾梦凡. (2024). 智慧教育赋能教师角色转变. 创新教育研究, 12(2), 392–401.
朱红, 迟锐泽. (2025). 人工智能赋能国际中文教育研究现状及未来发展路径探索. 辽宁工业大学学报(社会科学版), 27(05), 73–78.
邹翠英, 龚苇. (2023). 基于SAMR模型的移动技术辅助大学英语教学模式探索与实践. 武汉商学院学报, 37(5), 82–86.
Published
Issue
Section
License
Copyright (c) 2026 Jiraya Ho Wongyai

This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open-access journal in accordance with the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This permits users to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
for any purpose, even commercially.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.






