• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.35-43
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• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.251-258
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• 2022

In this paper, we propose a standard AI developer job training track based on industry needs. The characteristic of this curriculum is that it can minimize the mismatch of AI developer job competency between industries and universities. To develop an AI developer job training track, a survey will be conducted for AI developers working in industrial fields. In this survey, among the five NCS-based AI developer jobs, job analysis is conducted by deriving AI developer jobs with high demand for manpower in industrial fields. In job analysis, the core competency unit elements of the job are selected, and knowledge, skills, tools, etc. necessary to perform the core competency unit elements are derived. In addition, a standard AI developer job curriculum is developed by deriving core subjects and road-map that can educate knowledge, skills, tools, etc. In addition, we present an efficient AI developer job training method using the standard AI developer job training course proposed in this paper.

• Informatization Policy
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• v.31 no.2
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• pp.39-64
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• 2024

This study assesses the national AI competitiveness of 38 OECD countries with focus on AI human capital, AI infrastructure, and AI innovation capacity. Utilizing the fuzzy-set ideal type analysis method, these countries were categorized into eight distinct types based on their national AI competitiveness levels, leading to the derivation of pertinent implications. The analysis identified a category termed "AI Leading Country" consisting of North American, Western European, and Nordic countries, along with several Asian nations including South Korea. Remarkably, the United States demonstrated dominant global national AI competitiveness, achieving the highest fuzzy scores across all three evaluative factors. South Korea was classified as an "AI Leading Country" primarily due to its superior AI infrastructure, but its performance in AI human capital and AI innovation capacity was found to be moderate relative to other analyzed nations; thus highlighting the necessity of sustained focus on the accumulation of AI human capital and bolstering of AI innovation capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.1022-1034
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• 2023

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.188-190
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• 2022

We propose how to configure the AI education system based on the purpose of hybrid learning and the teaching-learning principle. Based on the four components of hybrid learning, we have designed the system conceptual diagram and DB configuration diagram for on-line and offline learning environments for effective AI education. The proposed AI education system model in this paper is expected to be a foundation for maximizing the effectiveness of AI education according to the level and needs of learners and building a more effective learner-centered learning environment in cultivating computational thinking in AI education.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.49-59
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• 2020

At the beginning of the discussion of AI education in K-12 education, the study was conducted to develop and apply an AI education program based on Design Thinking and analyze the effects of the AI education programs. In the AI education program, students explored and defined the AI problems they were interested in, gathered the necessary data to build an AI model, and then developed a project using scratch. In order to analyze the effectiveness of the AI education program, the change of learner's perception of the value of AI and the change of AI efficacy were analyzed. The overall perception of the AI project was also analyzed. As a result, AI efficacy was significantly increased through the experience of carrying out the project according to the Design Thinking process. In addition, the efficacy of solving problems with AI was influenced by the level of use of programming languages. The learner's overall perception of the AI project was positive, and the perceptions of each stage of the AI project (AI problem understanding and problem exploration, practice, problem definition, problem solving idea implementation, evaluation and presentation) was also positive. This positive perception was higher among students with high level of programming language use. Based on these results, the implications for AI education were suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.452-454
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• 2021

Currently, the security control market is operating based on AI technology. The reason for using AI is to detect large amounts of logs and big data between security equipment, and to alleviate time and human problems. However, problems are still occurring in the application of AI. The security control market is responding to many problems other than the problems introduced in this paper, and this paper attempts to deal with five problems. We would like to consider problems that arise in applying AI technology to security control environments such as 'AI model selection', 'AI standardization problem', 'Big data accuracy', 'Security Control Big Data Accuracy and AI Reliability', 'responsibility material problem', and 'lack of AI validity.'

• Women's Health Nursing
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• v.26 no.1
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.93-97
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• 2022

The purpose of this study is to suggest implications for early childhood SW·AI education considering the characteristics of early childhood education through a survey on SW·AI education in kindergartens. For this study, data were collected from 194 kindergartens through convenience sampling. The data was analyzed using frequency distribution, and it was found that 44% of kindergartens are conducting SW·AI education. 22% are conducting SW·AI education in the form of regular curriculum, and 70% are conducting SW·AI education in the form of special activities after school. SW·AI education was found to be conducted mainly by external instructors (97%) in the classroom (80%). For SW·AI education, block coding-based programs developed by companies such as Naver and the Clova were used, and all of these programs used programs and teaching aids in a package format, including teaching aids and materials developed by companies. 56% answered that they are not currently conducting SW/AI education, and lack of awareness on SW·AI education and lack of human/environmental infrastructure were the main factors. In order to realize SW·AI education considering the characteristics of early childhood education based on this survey, First, SW·AI education programs should be developed to develop play-centered computational thinking skills. Second, systematic teacher education at the national level should be conducted. Finally, the establishment of a department dedicated to early childhood SW·AI consisting of early childhood education experts and SW·AI education experts and financial support at the national level should be provided.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.