• Korean Journal of Childcare and Education
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• v.19 no.2
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• pp.61-85
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• 2023

Objective: This study aims to investigate whether science teaching attitude of early childhood teachers mediates the relationship between the professional learning environment of institutions and their intention to accept artificial intelligence (AI) technology, and whether the experience of using smart and digital devices moderates the effect of science teaching attitude. Methods: An online survey was conducted targeting 118 teachers with more than 1 year of experience in kindergarten and day care center settings. Descriptive statistical analysis, correlation analysis, and The Process macro model 4, 14 were performed using SPSS 27.0 and The Process macro 3.5. Results: First, the science teaching attitude of early childhood teachers served as a mediator between the professional learning environment of institutions and teachers' intention to accept AI technology. Second, the experience of using smart and digital devices was found to moderate the effect of teachers' science teaching attitude on their intention to accept AI technology. Conclusion/Implications: This results showed that an institutional environment that supports teachers' professionalism development and provides rich experience is crucial for promoting teachers' active acceptance of AI technology. The findings highlight the importance of creating a supportive institutional envionment for teacher's professional growth, enhancing science teaching attitudes, and facilitating the use of various devices.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.53-61
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• 2022

The purpose of this study was to test whether or not the AI chatbot is effective in acquiring target grammar for Korean EFL college students: prepositions and articles. A quasi-experiment was conducted with 46 first-year students taking part in a required English course. They were randomly divided into two groups: the experimental and control groups (23 students for each, respectively). The experimental group was engaged in six chat sessions with a chatbot over 6 weeks. A pretest and a posttest were used to examine the effectiveness of the chatbot by comparing any changes made in error frequencies of the target grammar in participants' English compositions. The results show that after a conversation with the chatbot, the experimental group significantly reduced the mean of omission errors in both prepositions and articles. To have a great effect in other error categories, chatbot feedback needs to be improved to reduce short responses or inaccurate utterances of students and induce them to actively participate in the conversation.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.675-692
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• 2023

This study used a Chatbot, a generative artificial intelligence (AI), to analyze the interaction between the Chatbot and students when exploring critical minerals from an epistemological aspect. The results, issues to be kept in mind in the teaching and learning process using AI were discussed in terms of the role of the teacher, the goals of education, and the characteristics of knowledge. For this study, we conducted a three-session science education program using a Chatbot for 19 high school students and analyzed the reports written by the students. As a result, in terms of form, the students' questions included search-type questions and non-search-type questions, and in terms of content, in addition to various questions asking about the characteristics of the target, there were also questions requiring a judgment by combining various data. In general, students had a questioning strategy that distinguished what they should aim for and what they should avoid. The Chatbot's answer had a certain form and consisted of three parts: an introduction, a body, and a conclusion. In particular, the conclusion included commentary or opinions with opinions on the content, and in this, value judgments and the nature of science were revealed. The interaction between the Chatbot and the student was clearly evident in the process in which the student organized questions in response to the Chatbot's answers. Depending on whether they were based on the answer, independent or derived questions appeared, and depending on the direction of comprehensiveness and specificity, superordinate, subordinate, or parallel questions appeared. Students also responded to the chatbot's answers with questions that included critical thinking skills. Based on these results, we discovered that there are inherent limitations between Chatbots and students, unlike general classes where teachers and students interact. In other words, there is 'limited interaction' and the teacher's role to complement this was discussed, and the goals of learning using AI and the characteristics of the knowledge they provide were also discussed.

• Information Systems Review
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• v.25 no.2
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• pp.125-144
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• 2023

Generative Artificial Intelligence (AI) technology is gaining global attention as it can automatically generate sentences, images, and voices that humans previously generated. In particular, ChatGPT, a representative generative AI service, shows proactivity and accuracy differentiated from existing chatbot services, and the number of users is rapidly increasing in a short period of time. Despite this growing interest in generative AI services, most preceding studies are still in their infancy. Therefore, this study utilized LDA topic modeling and keyword network diagrams to derive success factors for generative AI services and to propose successful business strategies based on them. In addition, using ChatGPT, a new research methodology that complements the existing text-mining method, was presented. This study overcomes the limitations of previous research that relied on qualitative methods and makes academic and practical contributions to the future development of generative AI services.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.713-722
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• 2024

This study highlights the impact of Artificial Intelligence (AI) technology on journalism, discussing its utility and addressing major ethical concerns. Broadcasting companies and media institutions, such as the Bloomberg, Guardian, WSJ, WP, NYT, globally are utilizing AI for innovation in news production, data analysis, and content generation. Accordingly, the ecosystem of AI journalism will be analyzed in terms of scale, economic feasibility, diversity, and value enhancement of major media AI service types. Through the previous literature review, this study identifies key ethical and social issues in AI journalism as well. It aims to bridge societal and technological concerns by exploring mutual development directions for AI technology and the media industry. Additionally, it advocates for the necessity of integrated guidelines and advanced AI literacy through social consensus in addressing these issues.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.413-421
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• 2022

The online questionnaire platform was conducted with Korean medicine doctors to analyses the recognition of applicability of artificial intelligence(AI) to the field of application and department of Korean medicine. Most of all respondents did not have a chance to participate academic experience or research experience related to AI, but had a high willingness to participate in further learning and research. The level of AI understanding was supervised learning When AI is introduced to Korean medicine, the mean predicted usefulness scores to each application field for research and development of oriental medicine(74.60 points) and social policy establishment(73.68 points) are significantly higher than other of Korean medicine field of application, while those of Sasang constitutional department(66.61 points) and Korean medicine rehabilitation(65.91 points) were evaluated higher than other fields of treatment of Korean medicine. Respondents judged that the introduction of AI could be realistically useful in relatively formal fields of Korean medicine, while it would be difficult in non-formal fields.

• Journal of Oriental Neuropsychiatry
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• v.35 no.2
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• pp.153-162
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• 2024

Objectives: Among OECD countries, South Korea has been having the highest suicide rate since 2018, with 24.1 deaths per 100,000 people reported in 2020. The objectie of this study was to examine the use of generative artificial intellicence (AI) chatbots to train third-year Korean medicine (KM) students in conducting suicide risk assessments for patients with depressive disorders to train students for their clinical practice skills. Methods: The Claude 3 Sonnet model was utilized for chatbot simulations. Students performed mock consultations using standardized suicide risk assessment tools including Ask Suicide-Screening Questions (ASQ) tool and ASQ Brief Suicide Safety Assessment. Experiences and attitudes were collected through an anonymous online survey. Responses were rated on a 1~5 Likert scale. Results: Thirty-six students aged 22~30 years participated in this study. Their scores for interest and appropriateness (4.66±0.57), usefulness (4.60±0.61), and overall experience (4.63±0.60) were high. Their evaluation of the usability of artificial intelligence chatbot was also high at 4.58±0.70 points. However, their trust in chatbot responses (Q12) was lower (3.86±0.99). Common issues related to dissatisfaction included conversation disruptions due to token limits and inadequate chatbot responses. Conclusions: This is the first study investigating generative AI chatbots for suicide risk assessment training in KM education. Students reported high satisfaction, although their trust in chatbot accuracy was moderate. Technical limitations affected their experience. These preliminary findings suggest that generative AI chatbots hold promise for clinical training, particularly for education in psychiatry. However, improvements in response accuracy and conversation continuity are needed.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.3
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• pp.277-287
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• 2024

Transcranial Doppler (TCD) ultrasound is a crucial non-invasive tool for assessing cerebral blood flow and is widely used to diagnose and monitor cerebrovascular diseases. This paper reaffirms the importance of TCD, details examination methods and precautions, and provides a guide for practitioners. TCD evaluates the blood flow velocity to assess stenosis, occlusion, and hemodynamic changes. Distinguishing between increased blood flow volume and decreased vessel diameter based solely on velocity is challenging, necessitating a comprehensive approach to integrating clinical findings and hemodynamic changes. The reliability of TCD results depends on the skill of the examiner and requires standardized procedures and continuous training. Advances in automation and artificial intelligence promise enhanced accuracy and reliability. Future research should focus on validating and clinically applying these technologies. This paper is a review of the clinical significance of TCD, methods, and precautions, offering a valuable guide for practitioners and highlighting the potential benefits of ongoing advancements in TCD for the diagnosis and treatment of cerebrovascular diseases.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.77-87
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• 2024

With the recent advancements in artificial intelligence, various chatbots have emerged, efficiently performing everyday tasks such as hotel bookings, news updates, and legal consultations. Particularly, generative chatbots like ChatGPT are expanding their applicability by generating original content in fields such as education, research, and the arts. However, the training of these AI chatbots requires large volumes of conversational text data, such as customer service records, which has led to privacy infringement cases domestically and internationally due to the use of unrefined data. This study proposes a methodology to quantitatively assess the re-identification risk of personal information contained in conversational text data used for training AI chatbots. To validate the proposed methodology, we conducted a case study using synthetic conversational data and carried out a survey with 220 external experts, confirming the significance of the proposed approach.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.