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<p>The rapid advancement of artificial intelligence (AI) has transformed translation practice and training, yet there remains limited agreement on which competencies should be prioritized for translators working in AI-mediated environments. This study aimed to identify and prioritize the core competencies required of translators in the age of AI. To achieve this objective, a two-round Delphi method was conducted with 29 experts from translator education, research, and professional practice, combining qualitative thematic analysis in Round 1 with ranking-based consensus analysis in Round 2. The first round identified seven core competency dimensions comprising 31 specific themes. In the second round, experts ranked these competencies, with Linguistic and Cultural Competence receiving the highest priority, followed by Adaptability and Lifelong Learning, Cognitive and Emotional Abilities, Professionalism and Ethical Standards, Technological Proficiency, Project Management and Interpersonal Skills, and Domain-Specific Knowledge and Specialization. Kendall’s coefficient of concordance indicated a statistically significant level of agreement among experts (<i>W</i> = 0.22, <i>p</i> &lt; 0.001). These findings provide an expert-informed basis for revising translator competence models, such as the PACTE model. Pedagogical implications are discussed in relation to integrated, scenario-based, and AI-responsive translator training.</p>

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Prioritizing translator competencies in the era of artificial intelligence: a Delphi method study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Chunwen Yang,
  • Shuai Hou,
  • Jing Chen,
  • Walton Wider

摘要

The rapid advancement of artificial intelligence (AI) has transformed translation practice and training, yet there remains limited agreement on which competencies should be prioritized for translators working in AI-mediated environments. This study aimed to identify and prioritize the core competencies required of translators in the age of AI. To achieve this objective, a two-round Delphi method was conducted with 29 experts from translator education, research, and professional practice, combining qualitative thematic analysis in Round 1 with ranking-based consensus analysis in Round 2. The first round identified seven core competency dimensions comprising 31 specific themes. In the second round, experts ranked these competencies, with Linguistic and Cultural Competence receiving the highest priority, followed by Adaptability and Lifelong Learning, Cognitive and Emotional Abilities, Professionalism and Ethical Standards, Technological Proficiency, Project Management and Interpersonal Skills, and Domain-Specific Knowledge and Specialization. Kendall’s coefficient of concordance indicated a statistically significant level of agreement among experts (W = 0.22, p < 0.001). These findings provide an expert-informed basis for revising translator competence models, such as the PACTE model. Pedagogical implications are discussed in relation to integrated, scenario-based, and AI-responsive translator training.