Large Language Models for Beginner-level Spoken Chinese Instruction in Thailand: A Conceptual Framework Informed by Teaching Practice

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.