翻译
术语
心理学
探索性研究
主题分析
定性研究
定性性质
词汇
高等教育
文化多样性
数学教育
计算机科学
描述性统计
记笔记
教育学
语言能力
多元方法论
应用心理学
口译(哲学)
教育技术
计算机辅助教学
医学教育
教学方法
数据收集
生成语法
半结构化面试
生成模型
测量数据收集
作者
Linping Zhong,Xingcheng Ma
摘要
Abstract As generative artificial intelligence (GenAI) transforms language educational practices, understanding its pedagogical role in specialised domains like interpreter training has become increasingly important. Still, little is known about how translation and interpreting students, also advanced second‐language learners, perceive and engage with this emerging technology. This study explores how Master of Translation and Interpreting (MTI) students in China experience and evaluate this technology in interpreting learning, as well as their expectations for its integration into training programmes. An exploratory mixed‐methods approach was adopted, drawing on both qualitative and quantitative data from a survey of 244 MTI students with diverse demographics, training backgrounds and usage experience. Quantitative data were analysed using descriptive statistics and inferential analysis, while qualitative data underwent thematic analysis. Findings revealed relatively low levels of GenAI usage in interpreting tasks. However, students with more intensive GenAI use in daily life reported higher usage frequency in interpreting contexts. Technology instruction and moderate self‐learning time could lower barriers to GenAI adoption, making it easier for students to begin using these tools. Overall, students held favourable views of GenAI, particularly for preparation activities such as terminology extraction, information prediction and the real‐world interpreting scenario simulation. Nonetheless, students raised concerns regarding GenAI's limitations in processing cultural and contextual nuances, algorithmic bias and GenAI translationese. Concerning integration into interpreter education, students expressed a strong desire for more systematic and practice‐oriented training, especially in prompt engineering and enhanced feedback functions tailored to interpreting performance. These findings may inform the pedagogical design and technological integration of GenAI in interpreter education.
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