Earticle

The study analyzes the semantic differences between the English verbs of near-synonyms extend and expand through corpus linguistics and a survey. Using COCA, COHA, and the Merriam-Webster dictionary, the study compares authentic language data and dictionary definitions. The results show that there are differences in the frequency and usage trends of extend and expand in the corpora. Students had difficulty distinguishing their meanings, which was reflected in low comprehension and accuracy rates in the survey. The analysis of noun collocations also reveals differences, with some collocations appearing in unexpected ways. This suggests limitations in vocabulary learning and understanding of these words in the context of learning English as a foreign language for Korean college students, where exposure to natural English language environments is limited. The study highlights the importance of providing students with authentic language experiences, diverse contexts, and the use of dictionaries to improve their understanding and use of extend and expand.

The purpose of this study is to describe the project implementation process to build an AI-based English speaking evaluation system. The project was carried out for approximately four months from September to December 2022 with support from the Korea Intelligence and Information Society Promotion Agency. Approximately 1,000 hours of English speaking evaluation data sets were collected, purified, processed, and artificial intelligence modeled. In this paper, the organizations formed to build data are introduced, and data collection is explained in detail. In addition, the process of refining the collected data so that it can be used for speaking evaluation and artificial intelligence modeling is described, and the evaluation method for constructing speaking evaluation data is also described.

This study aims to identify and address the inherent political bias in ChatGPT by utilizing a sentiment analysis task. We set up representative figures from each political faction, asked chatgpt to write about the politicians using various prompts, and then sentimentally analyzed their outputs to determine the bias of ChatGPT. We found that ChatGPT is more positively biased toward liberal politicians in South Korea. We also found that ChatGPT's bias can be reduced by combining general narratives that encourage neutral writing or by refining the prompts with variables such as tone and writing style. This study provides important insights into the responsible use of AI and how to improve its bias.