Future Translators’ Linguistic and Non-linguistic Competencies and Skills in The Age of Neural Machine Translation and Artificial Intelligence: A Content Analysis

Abstract

Artificial Intelligence (AI) and machine learning (ML) are disrupting the way millions of people work. According to the Future of Jobs Report published by the World Economic Forum (WEF 2022), the Fourth Industrial Revolution will result in the disappearance of more than 75 million jobs by 2025. While this is considered bad news to many, the report also predicts the creation of 133 million jobs for the same reason. Among the at-risk jobs are translation and language services jobs which have witnessed a sharp drop in demand due to the adoption of AI and ML technologies in machine translation systems making them capable of producing translations that match the translations produced by expert human translators. Using content analysis method of online translation job advertisements in Saudi Arabia, this study sheds light on the current in-demand translator linguistic and non-linguistic competences and skills from the perspective of employers and matches those with the well-established translator competence models in literature. Through a content analysis of 213 online job advertisements, the study identifies five key competency domains: Language and Culture, Personal and Interpersonal, Service Provision, Technology, and Translation Knowledge. While language proficiency remains crucial, there is a growing emphasis on "soft skills," project management, technological adaptability, and ethical practice. The findings reveal a partial alignment with existing competency models but also highlight potential gaps, particularly regarding strategic planning and document analysis skills. The study underscores the need for translator education to adapt to the evolving demands of the industry, integrating traditional skills with emerging technologies and fostering a culture of lifelong learning. By bridging the gap between academic training and industry expectations, we can better equip future translators to navigate the complexities of the profession and thrive in the age of Neural Machine Translation and AI.