컴퓨터보조통역교육(CAIT)을 위한 LMS 기반 자동 어노테이션 저작 도구 개발

Abstract

This study focuses on the development of a learning management system (LMS)-based automatic annotation authoring tool for computer-assisted interpreter training (CAIT). The primary goal of the research is to establish a specialized LMS that integrates AI-powered speech-to-text (STT) and automatic annotation functionalities to create an efficient feedback system. Through the use of external APIs (Application Programming Interface), the tool implements automatic detection of disfluency elements such as fillers, pauses, and repetitions in STT-generated interpreting outputs, which are visualized using a TextAE (Text Annotation Editor) program. Additionally, the system enhances objectivity in evaluation by providing visualized assessment results and supports autonomous practice for learners through a self-study feature. The significance of this study lies in its effective application of AI technologies to interpreter training, enabling an integrated system that encompasses task creation, execution, and evaluation in one platform. It is anticipated that this research will contribute to the digital transition of interpreter training and improve educational efficiency.