DEVELOPMENTAL STAGES OF ARTIFICIAL INTELLIGENCE-BASED TRANSLATION SYSTEMS AND THEIR IMPACT ON CONTEMPORARY TRANSLATION STUDIES

The rapid advancement of artificial intelligence (AI) has rapidly changed the field of translation processes in recent decades. Machine Translation (MT), once a rule-based computational experiment, has evolved into highly sophisticated neural systems capable of producing near-human translations. This study examines the developmental stages of AI-based translation systems—Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), and Neural Machine Translation (NMT)—and analyzes their influence on contemporary translation studies. Using a qualitative systematic literature review approach, the research evaluates technological progress and its theoretical, practical, ethical, and pedagogical implications. The findings indicate that neural architectures and transformer models have dramatically improved translation fluency and contextual accuracy. However, AI integration has also redefined translator roles, challenged traditional translation theories, and introduced new ethical concerns. The study concludes that AI does not replace translation studies but reshapes its frameworks and methodologies, promoting a hybrid human–machine paradigm.