Artificial Intelligence in Research Translation in Higher Education: Applied Potentials for University Students
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Artificial Intelligence, Translation, Analysis, Education, Content, University
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in research translation within higher education, shifting the paradigm from basic automation to intelligent systems capable of semantic understanding and contextual adaptation. This study explores the applied potentials of AI-driven tools, such as natural language processing (NLP) and deep learning, in facilitating research translation for university students. Unlike traditional machine translation, contemporary AI models understand cognitive structures and cultural nuances, generating outputs consistent with the target language's linguistic context. This paper analyzes how these technologies enhance cross-border academic communication and facilitate access to global knowledge by accelerating multilingual content production and reducing costs. Furthermore, it examines the collaborative human-machine review mechanism as a key factor in improving translation quality. The findings suggest that integrating AI into higher education research practices not only optimizes technical efficiency but also broadens the horizons for studying translation as a complex cognitive process. This study provides insights into how university students can leverage these advancements to bridge linguistic gaps in global academic discourse.
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