本研究聚焦大语言模型在翻译教学中的应用,系统考察其在学生译文质量评估中的效能与局限。研究采用人工翻译质量评价标准,构建量化为主、质性为辅的双层分析框架,整合了人工评分、模型评分及评语数据。量化结果表明,大语言模型在英译汉任务的结构化维度表现稳健,但在汉译英的语义与文化维度一致性显著下降,暴露其在深层语义理解与文化适配方面的不足。质性分析进一步揭示模型生成的评语存在模板化、错误归因与创造性排斥等问题,与量化结果相互印证。基于此,研究提出人机协同的教学实践路径,强调大语言模型应作为结构化校验的辅助工具,由教师主导语义与文化评估。研究为智能化翻译评估的教育应用提供了实证支持,推动大语言模型从工具性辅助向认知协作的角色转型。

This study investigates the application of large language models (LLMs) in translation teaching, focusing on their effectiveness and limitations in assessing student translations. Using established standards for human translation quality evaluation, a two-tier analytical framework combining quantitative and qualitative analyses was developed, incorporating human scores, LLM-generated scores, and evaluative comments. Quantitative results indicated that the LLM performed reliably in structural dimensions of Chinese-to-English tasks, but a marked decline was observed in semantic and cultural dimensions in English-to-Chinese tasks, exposing weaknesses in deep semantic understanding and cultural adaptation. Qualitative analysis further revealed issues such as templated feedback, misattribution of errors, and rejection of creative translation, thereby corroborating the quantitative findings. Based on these results, the study proposes a human-AI collaborative pathway for teaching practice, highlighting the role of LLMs as auxiliary tools for structural checking while reserving semantic and cultural evaluation for teachers. The findings provide theoretical grounding and pedagogical implications for the integration of intelligent assessment in translation education, and suggest a shift in LLMs’ role from instrumental support to cognitive collaboration.