• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.109-115
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• 2020

In this paper, we propose an artificial intelligence(AI) technology analysis using partial least square(PLS) regression model. AI technology is now affecting most areas of our society. So, it is necessary to understand this technology. To analyze the AI technology, we collect the patent documents related to AI from the patent databases in the world. We extract AI technology keywords from the patent documents by text mining techniques. In addition, we analyze the AI keyword data by PLS regression model. This regression model is based on the technique of partial least squares used in the advanced analyses such as bioinformatics, social science, and engineering. To show the performance of our proposed method, we make experiments using AI patent documents, and we illustrate how our research can be applied to real problems. This paper is applicable not only to AI technology but also to other technological fields. This also contributes to understanding other various technologies by PLS regression analysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.13-25
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• 2022

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers' purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Smart Media Journal
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• v.9 no.3
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.339-351
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• 2020

In response to changes in Fourth Industrial Revolution in recent years, the field of education has focused on development of the human resources in the areas of artificial intelligence (AI: Artificial Intelligence) and industrial robot. Due to particular interest in these areas, the importance of developing integrated human resources equipped with artificial intelligence technology is emphasized in higher education and vocational competence development. In regards to rapid changing environment, this study created a program "Fostering personalized AI integrated human resource" and established an operational model correspond to latest personalized education trend. The established operational model was conducted twice using Delphi survey with experts in AI and innovative education in order to verify the suitability of program's basic structure, training process, and the sub-components of the operational strategy. The final training model was applied to the online vocational training platform (STEP) and a plan was proposed to establish a personalized training model to foster an AI integrated competent individual.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.193-204
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• 2021

Based on risk-benefit theory, this study examined a structural equation model accounting for the mechanisms through which affective perceptions of AI predicting individuals' support for the government's Ai policies. Four perceived characteristics of AI (i.e., usefulness, entertainment value, privacy concern, threat of human replacement) were investigated in relation to perceived benefits/risks, attitudes toward AI, and AI policy support, based on a nationwide sample of South Korea (N=352). The hypothesized model was well supported by the data: Perceived usefulness was a strong predictor of perceived benefit, which in turn predicted attitude and support. Perceived benefit and attitude played significant roles as mediators. Perceived entertainment value along with perceived usefulness and privacy concern predicted attitude, not perceived benefit. Neither attitude nor support was significantly associated with perceived risk which was predicted by privacy concern. Theoretical and practical implications of the results are discussed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.69-76
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• 2022

In this paper, we presented the contents and methods of AI classes using AI platforms. First, we extracted the content elements of each stage of the AI class using the AI education platform from experts. Classes using the AI education platform were divided into 5 stages and 25 class elements were selected. We also conducted a survey of 82 teachers and analyzed the factors that they acted importantly at each stage of the AI platform class. As a result of the analysis, teachers regarded the following contents as important factors for each stage that are AI model preparation stage (the learning stage of the AI model), problem recognition stage (identification of problems and AI solution potential), data processing stage (understanding the types of data), AI modelingstage (AI value and ethics), and problem solvingstage (AI utilization in real life).