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Conference Paper Design of Interactive Reading Comprehension Competence Assessment System using AI
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Authors
HongYeon Yu, Eunkyoung Jeon, Seunghun Oh, Donghoon Son, Seihyoung Lee, Kwon-Seob Lim
Issue Date
2022-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.2062-2064
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952613
Abstract
This paper describes the design of an interactive reading ability assessment system using AI. The proposed system consists of a natural language processing module, a reading comprehension competence assessment module, and a visualization module, and is serviced through a web server and a client. The natural language processing module provides an interactive interface between the user and the assessment system through speech-to-text (STT) and text-to-speech (TTS) functions. The reading comprehension competence assessment module evaluates the data responded by the user by measuring the similarity in units of sentences and words, and displays the final assessment result through the diagram of the visualization module. The purpose of the system proposed in this paper is to improve the reading comprehension competence of Korean students by assessing the objective reading competence.