The impact of artificial intelligence on translation quality: An Analytical Study Comparing Human Translators and Modern Translation Systems

Abstract

This research is based on evaluating the quality of AI-supported machine translation compared to human translation, in light of the rapid development of modern linguistic technologies. The significance of this study stems from the increasing role of machine translation across various media, cultural, and educational institutions, which makes it necessary to analyze its ability to produce linguistically and semantically accurate texts, the research problem revolves around the question: To what extent can artificial intelligence achieve translation quality comparable to that of human translators? And what are its limits in handling the diverse cultural and semantic contexts of texts? The study adopts a descriptive–analytical method, using a systematic comparison between human translations and machine translations. The sample included media, scientific, and literary texts, which were analyzed based on linguistic accuracy, contextual consistency, style, and the ability to translate specialized content The results indicate that modern machine translation systems based on neural networks exhibit high linguistic accuracy and good ability to maintain textual coherence, in addition to a notable advantage in speed and output volume compared to human translators. However, the study notes limitations in handling metaphors, dialects, precise technical terminology, and rhetorical structures with deep cultural Nuance the findings also confirm that human revision remains essential to ensure final quality, especially in sensitive fields. The study further shows that integrating artificial intelligence with human translators constitutes the most effective model for producing accurate and professional.