Mengdi Shao*, Juntao Deng
School of Foreign Languages, Wuhan Institute of Technology, Wuhan 430205, Hubei, China.
*Corresponding author: Mengdi Shao
Published: November 30, 2025
Article history: Updated on June 26, 2026
Abstract
This paper is based on the shortcomings of the traditional methods, and it suggests an artificial intelligence-assisted knowledge management framework to help in interpreting education. There are three important issues with specialized knowledge management. These are the challenge of finding terms of high frequency more systematically, the challenge of processing dense informational material more efficiently, and the lack of support of predictive pre-interpreting preparation. In order to deal with these problems, word clouds derive the main terminology of domain-specific texts. AI briefing systems such as Quillbot condense background specialized knowledge so that it can be quickly understood. The use of AI-based speaker profile analysis determines discourse styles and the topic development pattern, thus, providing the predictive preparation. The results indicate that these tools can greatly enhance the efficiency of pre-interpreting, the ability to process information, and strategic use. AI is not to replace human professional expertise but to be an intelligent help. This brings out the crucial role of synergistic partnership between human cognition and technological aids in interpreter training.
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How to cite this paper
Integrating Artificial Intelligence in Interpreting Education: Innovations in Specialized Knowledge Management and Teaching Practice
How to cite this paper: Mengdi Shao, Juntao Deng. (2025). Integrating Artificial Intelligence in Interpreting Education: Innovations in Specialized Knowledge Management and Teaching Practice. Translation and Foreign Language Learning, 1(4), 611-617.
DOI: http://dx.doi.org/10.26855/tfll.2025.11.003