This study focuses on the development of a learning management s ystem (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 effici ent feedback system. Through the use of external APIs (Application P rogramming Interface), the tool implements automatic detection of di sfluency elements such as fillers, pauses, and repetitions in STT-gen erated interpreting outputs, which are visualized using a TextAE (Tex t Annotation Editor) program. Additionally, the system enhances obj ectivity in evaluation by providing visualized assessment results and supports autonomous practice for learners through a self-study featu re. The significance of this study lies in its effective application of AI technologies to interpreter training, enabling an integrated system th at encompasses task creation, execution, and evaluation in one platf orm. It is anticipated that this research will contribute to the digital tr ansition of interpreter training and improve educational efficiency.