Adaptation of Translator Roles and Competence Development in AI-Assisted Translation Environments

Abstract

The breakthrough advancements in artificial intelligence technology, particularly the maturation of neural machine translation, are driving fundamental transformations within the translation field. This paper aims to systematically examine the systemic reshaping of the translation ecology driven by this technology, along with the consequent need for role adaptation and competence development among translators. The study first analyzes the structural impact of artificial intelligence on translation production models, workflow processes, and quality assessment paradigms, revealing the transition mechanism from linear manual operations to networked human-machine collaboration. Subsequently, the paper discusses the shifting role of translators within the human-machine symbiosis system, transforming from direct text producers into process managers, post-editing decision-makers, and integrated quality control nodes for multimodal projects, with their core functions increasingly focusing on high-level cognitive judgment and system management. Based on this analysis, the paper constructs a core competency framework for translators tailored to the intelligent translation ecosystem. This framework encompasses critical technical application literacy, deep linguistic cognition and strategic intervention capabilities, as well as an adaptive development path supported by metacognition and lifelong learning. It aims to provide a theoretical reference for translators to achieve sustained professional development in an era of deeply embedded technology.