AI辅助翻译下学生能力短板与教学转向——基于MQM框架的实证研究-科学发展研究-CSCIED科技核心评价数据库-手机版

AI辅助翻译下学生能力短板与教学转向——基于MQM框架的实证研究

Students' competency gaps in ai-assisted translation and the transformation of translation teaching — an empirical study based on the MQM framework

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DOI	10.12208/j.sdr.20250262
刊名	科学发展研究 Scientific Development Research
年，卷(期)	2025, 5(7)
作者	秦雨,
作者单位	大连大学辽宁大连
摘要	本研究以某高校翻译专业58名三年级学生为研究对象，采用有无AI辅助交叉实验设计，通过基于多维质量评估模型（MQM）的深度文本对比分析与半结构式访谈，系统探究AI辅助翻译下学生能力短板和AI使用问题。研究结果显示：两种实验条件下，学生在语境识别、句式调整及语用风格处理等方面均存在明显局限；学生对AI存在不同程度的过度依赖，缺乏对AI译文的批判性审校意识与能力。翻译教师应据此调整教学重点，聚焦高阶翻译能力，提升学生人文素养，最终实现人机协同翻译的最优效果。
Abstract	This study recruited 58 third-year translation majors and adopted a crossover experimental design. Using in-depth textual comparison based on the Multidimensional Quality Metrics (MQM) model and semi-structured interviews, it systematically explored students' competency gaps and AI usage issues in AI-assisted translation. Results indicate that under both conditions, students exhibit obvious limitations in context recognition, syntactic adjustment, and pragmatic style handling. They also show varying degrees of over-reliance on AI, lacking the awareness and ability to critically review AI-generated translations. Translation teachers should thus adjust teaching priorities, focus on high-order translation competencies, and enhance students' humanistic literacy to achieve optimal human-AI collaborative translation.
关键词	AI翻译；翻译教学；质性分析；文本对比分析；MQM框架
KeyWord	AI translation; Translation teaching; Qualitative analysis; Textual comparative analysis; MQM framework
基金项目
页码	20-24

参考文献
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[1] 覃军,马冬梅.GenAI时代翻译教学的创新:求变与应变
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[5] Konttinen K, Salmi L & Koponen M. Revision and Post-editing Competences in Translator Education
[A] in Translation Revision and Post-editing Industry Practices and Cognitive Processes
[C]. London: Routledge, 2021. 187-202.

[6] Guerberof Arenas A & Moorkens J. Machine translation and Post-editing Training as Part of a Master’s Programme
[J]. The Journal of Specialised Translation, 2019, 31: 217-238.

[7] Massey G, Kiraly D & Ehrensberger-Dow M. Training Translator Trainers: An Introduction
[J]. The Interpreter and Translator Trainer, 2019, 13(3): 211-215.

[8] 成爽,张玉双.翻译教学质量评估新视角：行业评估模型
[J].当代外语研究,2024,(05):45-56+79.

引用本文

秦雨. AI辅助翻译下学生能力短板与教学转向——基于MQM框架的实证研究 [J]. 科学发展研究. 2025; 5; (7). 20 - 24.

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