Scholarlink

<p>AI-driven Machine Translation (MT) technology has raised doubts about whether it can replace Human Translation (HT), particularly in the academic English field. This research contrasts AI-based MT and HT of academic English on accuracy, stylistic quality, errors, contextual appropriateness, and overall suitability. A quantitative comparative design was employed for the translation of eight Chinese texts with various genres. Using purposive sampling, 4 certified human translators were selected to produce the HT, and also used AI-based MT systems such as DeepL, ChatGPT, and Google Translate to translate the text. 69 experts evaluated the translated versions, and this data was examined using SPSS software. HT produced better performance than MT through mean accuracy, stylistic quality, contextual appropriateness, and overall suitability (Human: 4.76, AI: 3.73), revealed statistically significant differences (<i>p</i> &lt; 0.001), and effect sizes (Cohen’s d = 1.53 to 2.81). Compared to HT, the MT showed higher grammatical and semantic errors (Cohen’s d = 2.81). The fluency, tone, and register of stylistic quality were evaluated at higher levels for HT (<i>p</i> &lt; 0.001). Among AI-MT tools, DeepL shows higher performance in translation (M = 4.16, SD = 0.37). The genre-wide comparison of MT and professionally certified HT, and the comparison among multiple AI tools, towards various translation dimensions, provides insights into the differences in translating quality. The research indicates a continued reliance on human professionals for important scholarly translations while encouraging continued development in AI translation software for better quality.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Statistical Analysis of Human and AI Machine Translation Accuracy, Style, and Context in Academic English

  • 期刊论文

  • 发表日期：

    2026年6月20日

查看全文
  • Shi Beibei

摘要

AI-driven Machine Translation (MT) technology has raised doubts about whether it can replace Human Translation (HT), particularly in the academic English field. This research contrasts AI-based MT and HT of academic English on accuracy, stylistic quality, errors, contextual appropriateness, and overall suitability. A quantitative comparative design was employed for the translation of eight Chinese texts with various genres. Using purposive sampling, 4 certified human translators were selected to produce the HT, and also used AI-based MT systems such as DeepL, ChatGPT, and Google Translate to translate the text. 69 experts evaluated the translated versions, and this data was examined using SPSS software. HT produced better performance than MT through mean accuracy, stylistic quality, contextual appropriateness, and overall suitability (Human: 4.76, AI: 3.73), revealed statistically significant differences (p < 0.001), and effect sizes (Cohen’s d = 1.53 to 2.81). Compared to HT, the MT showed higher grammatical and semantic errors (Cohen’s d = 2.81). The fluency, tone, and register of stylistic quality were evaluated at higher levels for HT (p < 0.001). Among AI-MT tools, DeepL shows higher performance in translation (M = 4.16, SD = 0.37). The genre-wide comparison of MT and professionally certified HT, and the comparison among multiple AI tools, towards various translation dimensions, provides insights into the differences in translating quality. The research indicates a continued reliance on human professionals for important scholarly translations while encouraging continued development in AI translation software for better quality.