This study compares AI PengTalk’s assessments of Korean children’s pronunciation with the assessments of Korean teachers. Sixty Korean sixth-graders participated as assessees, and four Korean elementary teachers participated as assessors. Both PengTalk and the teachers rated the children’s production of 10 English sentences on a five-point scale. They focused on segmentals, stress-rhythm, intonation, and speech rate. The findings were as follows: Firstly, PengTalk evaluated the children’s pronunciation in the four elements significantly lower than the teachers across all English proficiency levels. Secondly, teachers’ ratings of the students aligned more closely with their pre-evaluated English proficiency levels than the AI PengTalk’s assessments. The teachers rated students at the upper level significantly higher than those at the intermediate level, who were, in turn, assessed significantly higher than those at the lower level in all four elements. Furthermore, AI PengTalk and the teachers differed in the mean order of the four elements, particularly in segmentals. Based on the results of this study, suggestions were made for the development and implementation of AI-based English programs.
This study aims to investigate the effects of using AI chatbots in Korean English education from a macro perspective. For this purpose, 19 experimental studies are selected to conduct a meta-analysis, synthesizing the results of 51 individual study cases. The results of this study are as follows: First, it is found that the overall effect size of using chatbots is more than medium size meaning that a chatbot is an effective tool to learn English. Second, in the aspects of linguistic competence and affective categories, each shows over medium sizes like the overall effect size. In details of the dependent variables, vocabulary and speaking in linguistic competence and motivation in affective categories, large effect sizes are shown. Third, the effect sizes are getting larger, as the younger the students are, the longer the experiment period lasts, and the more purpose-built the chatbot is. But the differences in the effect sizes in terms of these moderators (e.g., school level, experiment period, and chatbot type) are not significant. Lastly, it is suggested that follow-up studies are needed to collect a sufficient number of experimental study cases and subdivide the variables for performing a more detailed meta-anlysis.
The current study presents a methodology of teaching the connotations and nuances of the word Han (恨) to English-speaking Korean language learners. Since Han (恨) expresses unique and traditional aspects of Korean sensibilities, the goal would be for native English speakers to be able to understand the complexities of its meaning, in spite of the differences in culture background. To this end, research was done into how Han (恨) is translated into English, as there is no term in English with the same meaning. From these findings, a list of Korean vocabulary related to the meaning of Han (恨) was made to show its definitions in English. In addition, a survey was conducted on 42 Americans to find out how English speakers understand Han (恨). As a result of this research, the current study demonstrates the need to teach the meaning of Han (恨) to Korean language learners from English-speaking countries, and suggests a teaching methodology which follows the order of Pre-Class, In-Class and After-Class study based on the Flip-Learning model.