AI-Assisted Translation and Human Revision: A Study of Quality, Error Types, and Translator Agency

Abstract

This paper will discuss the ways in which AI-assisted translation transforms the quality of translation, error dynamics, and translator agency in the modern translation practice. Its primary inquiry is whether neural machine translation has decreased the place of the human translator or has simply reallocated it to new technological procedures. The purpose of the study is to compare raw machine translations, post-edited machine translations and fully human translations in order to find out their strengths, weaknesses and implications to the professional translation. The study is based on a qualitative comparative approach and focused error analysis to measure the translation outputs in terms of adequacy, fluency, lexical accuracy, stylistic appropriateness, discourse coherence, and human intervention. The results indicate that raw machine translation is quite effective in speed and surface fluency but is susceptible to semantic error, contextual error, and flattening of style. Post-editing enhances legibility and minimizes blatant mistakes, but tends to maintain the structural effect of the machine text. Human translation is slower but with greater interpretive flexibility, and with more control of tone and sensitivity to communicative intent. The research concludes that the human judgment is still at the center of AI-mediated translation particularly when the meaning, context, and rhetorical effect are paramount. It suggests that critical AI literacy, post-editing competence, and agency-centered training should be incorporated into the training of translators, and that more research should be conducted with broader corpora, genres, and process-based approaches.