Large Language Models for Beginner-level Spoken Chinese Instruction in Thailand: A Conceptual Framework Informed by Teaching Practice
Miao Sun *
School of Foreign Languages, Tianjin Chengjian University, Tianjin 300384, China.
*Author to whom correspondence should be addressed.
Abstract
Large language models (LLMs) offer opportunities to expand conversational practice and to develop contextualised materials for international Chinese language education. In Thai secondary-school settings, however, the instructional value of these systems depends on the suitability of generated language, the quality of speech-processing components, teacher supervision, and equitable access. This conceptual article, informed by professional experience teaching Chinese at a Thai public secondary school, examines challenges that may arise in beginner-level spoken Mandarin instruction. It draws selectively on scholarship concerning comprehensible input, learner output, interaction, foreign-language anxiety, Mandarin tone production, and AI-assisted language learning. Four pedagogical possibilities are considered: additional opportunities for dialogue, structured pronunciation feedback, locally meaningful scenarios, and practice beyond scheduled lessons. The article distinguishes text-generating LLMs from automatic speech recognition and pronunciation-assessment tools and identifies risks involving inaccurate bilingual feedback, learner dependence, cultural mismatch, access barriers, and the protection of school-age learners. A teacher-led sequence of pre-class preparation, in-class interaction, and after-class consolidation is proposed, together with feasible assessment and safeguarding practices. The framework constitutes a pedagogical proposal rather than an evaluated intervention; its effects on speaking proficiency and learner experience require empirical testing.
Keywords: Large language models, Mandarin Chinese, Thailand, beginner learners, oral communication, second language acquisition, AI-assisted language learning