• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.973-985
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• 2021

The purpose of this study is to suggest the direction and implications of learning with AI in the future by analyzing the trends of research learning with AI in the field of education. For doing this, the final 78 papers published in domestic journals over the past three years from 2019 to July 2021 were selected for analysis through review. The analysis results are as follows. First of all, papers in 2020 among the three years were most published, and the most utilized research method was the qualitative research. In addition, according to the analysis by study subject, studies on elementary school students were the most common, followed by studies on college and graduate students. In the analysis by subject, research related to foreign language education was most utilized and chatbot was most used in the AI technology type. Finally, the research learning with AI accounted for the majority, and student support accounted for the majority as the type of education system learning with AI at the implementation stage among the areas of teaching and learning and evaluation. Based on these results, the direction and implications of learning with AI in the future were presented. This study is meaningful in that it grasped research trends of learning with AI in domestic from an overall perspective, and examined learning with AI focusing on the instructor-learner and the teaching and learning design process.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.279-306
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• 2022

Public institutions and governments develop discussions on the premise that they can facilitate smooth public communication with the socially vulnerable by promoting citizens' welfare by providing voice-based service chatbots to citizens. The purpose of the study is to propose a plan for intelligent governments to provide quick and efficient administrative services by efficiently managing knowledge and information within and outside government organizations based on ICT and facilitating access and use of information for citizens, especially vulnerable groups. This paper confirms that citizens' attitudes, perceptions, and expectations for public institutions ahead of voice-based service provision are positive through small surveys and interviews with experts with knowledge of artificial intelligence, discuss the technical aspects of voice-based services, the significance and necessity of public institutions. In addition, the government and public institutions are considering the implications of using and providing voice-based services. As a result, chatbot's voice-based service is of great significance in providing an opportunity and platform for wider citizens to participate in intelligent government, to strengthen information accessibility, guarantee and strengthen human rights and basic rights of the socially vulnerable.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.