The The Role of AI in Translator Training: Assessing AI’s Influence on Translation Education and Professional Training

Abstract

The growing integration of Artificial Intelligence (AI) into translation workflows is reshaping the demands placed on translator education. Neural Machine Translation (NMT) systems such as DeepL and Google Translate offer increased efficiency but challenge conventional teaching practices by shifting emphasis from direct translation to post-editing, evaluation, and decision-making. Despite this evolution, translator training programs often lack structured guidance on integrating AI, leading to a potential gap in preparing students for contemporary professional environments. This qualitative case study explores the evolving role of AI in translator education by analyzing student preparedness and attitudes toward AI-assisted translation tasks at the King Fahd School of Translation (KFST), Morocco’s only institution combining theoretical and practical translation approaches. The study involved 17 Master’s students enrolled in the “PE: English & Arabic” core module, all of whom had prior experience using AI tools such as Google Translate, ChatGPT, DeepL, or Reverso. Data were gathered through a Google Forms questionnaire comprising 16 items, including multiple-choice, Likert-scale, and open-ended questions designed to elicit both quantitative and qualitative insights. The questionnaire remained open for two weeks and was distributed via institutional email and messaging platforms to ensure full participation. Responses were analyzed through descriptive statistics for closed-ended questions and thematic analysis (Braun & Clarke, 2006) for open-ended feedback, focusing on key themes such as AI familiarity, post-editing competence, curriculum reform, and ethical concerns. Thematic triangulation allowed for a nuanced understanding of student perceptions regarding the pedagogical integration of AI in translation education.Findings indicate that while this cohort widely uses AI tools for speed and efficiency, it expresses uncertainty about the reliability of machine outputs and reports a lack of formal training in post-editing and AI evaluation. All participants unanimously supported the introduction of dedicated modules on AI literacy and post-editing. The study concludes that AI integration can enhance translator training if supported by updated curricula emphasizing technological competence, critical engagement, and ethical awareness. It advocates for an andragogical paradigm that positions AI as a complementary tool, reinforcing human expertise and underscores the enduring value of human judgment in producing contextually nuanced translations.