AI 赋能口译员自我提升的闭环框架研究

The AI-Supported Interpreter Self-Improvement Framework (AI-SIF): A Closed-Loop Approach to Empowering Interpreters

摘要: 本文以生成式人工智能时代口译员自我赋能为导向，设计构建AI 赋能口译员自我提升框架(AI-SIF)，进行模块化功能实现口译闭环，译前利用大语言模型抽取术语并预测难点等，译中实现实时转写和翻译，译后借助大语言模型生成报告写进术语库形成闭环反馈学习、提升学习效率。以“气候变化风险加剧”主题演讲为测试实例进行对比实验，译前准备效率、术语覆盖率和理解度提升，译中漏译误译明显减少，译后复盘效率提高，证明AI-SIF对于口译工作质量和学习效率提升有巨大帮助，为口译员学习和实践提供可操作可验证的支持。

Abstract: This study introduces a novel closed-loop framework, the AI-Supported Interpreter Self-Improvement Framework (AI-SIF), which integrates AI tools throughout the interpreting workflow. The system employs large language models to automate terminology extraction and challenge prediction during pre-task preparation, provides real-time assistance through speech recognition and machine translation during interpretation, and generates performance reports with updated terminology databases post-task. This creates a self-reinforcing cycle for continuous skill development. A case study on climate change risks confirmed AI-SIF’s effectiveness: it boosted preparation efficiency and terminology coverage, reduced interpretation errors, and accelerated post-task review. This demonstrates AI-SIF’s significant value in enhancing both interpreting quality and learning efficiency, providing operational and verifiable support for interpreters’ learning and practice.