A Systematic Literature Review on the Application of Artificial Intelligence in Translation: Challenges, Innovations, and Impact Across Diverse Fields

Abstract

With the ongoing expansion of digital technology and global communication, the need for rapid and accurate language translation has become increasingly important. Artificial intelligence has led to significant progress in translation, especially through innovations such as Neural Machine Translation (NMT) and Large Language Models (LLMs). Despite ongoing advancements, these translation systems often fall short in areas such as gender fairness, maintaining accuracy from the source language, and recognizing cultural subtleties. This paper systematically analyzes 29 selected research works to highlight the challenges, novel contributions, and potential implications of AI-assisted translation. The approach used was a systematic review. Results show that Artificial intelligence has improved translation efficiency and identified new opportunities in global trade, research, healthcare, and cross-cultural communication. Yet it needs to tackle several issues, such as algorithmic bias, challenges and nuances in cultural retention, and ethical challenges in the human translation industry. The results of this analysis underscore the need for more comprehensive, precise, and ethical AI models, as well as the importance of AI-human collaboration to achieve the highest-quality translations. This study helps to understand the development of AI-based translation and develop methods for its long-term usage.