AI-empowered multidimensional evaluation of TCM English speech courses
AI赋能中医英语演讲课程多元评价

摘要

本研究聚焦大语言模型在中医英语演讲课程评价中的应用，探索其对提升教学质量、推动中医文化国际传播的重要意义。研究通过构建 “同伴互评辅助者”、“演讲逻辑分析师”、“语言准确性校验者”、“跨文化表达评估师”、“个性化反馈生成器”等五大核心角色，将大语言模型深度融入课程评价体系。研究结果表明，大语言模型可通过生成标准化互评框架提升同伴互评客观性；利用语义分析优化演讲逻辑结构；依托权威语料库保障中医英语术语准确性；基于跨文化语料学习增强文化表达适配性；并通过整合多维度数据实现个性化反馈。该研究为中医英语教学评价的智能化转型提供理论支撑与实践路径，有效推动中医文化国际传播人才培养的专业化发展。

Abstract

This study focuses on the application of large language models (LLMs) in the evaluation of TCM English speech courses, exploring their significance in enhancing teaching quality and promoting the international dissemination of traditional Chinese medicine (TCM) culture. By establishing five core roles—“Peer Assessment Facilitator,” “Speech Logic Analyst,” “Language Accuracy Checker,” “Cross-Cultural Expression Evaluator,”and “Personalized Feedback Generator”—the research deeply integrates LLMs into the course evaluation system. Findings indicate that LLMs can enhance the objectivity of peer assessment by generating standardized evaluation frameworks; optimize speech logic structures through semantic analysis; ensure the accuracy of TCM English terminology by relying on authoritative corpora; improve the adaptability of cultural expression based on cross-cultural corpus learning; and deliver personalized feedback by integrating multi-dimensional data. This research provides both theoretical support and practical pathways for the intelligent transformation of TCM English teaching evaluation, effectively advancing the professional development of talent cultivation for international TCM cultural dissemination.