• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.93-100
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• 2020

This study aims to design and develop effective digital contents to improve the ability for artificial intelligence literacy. First, we defined AI literacy and analyzed the competencies required for artificial intelligence literacy. After selecting the educational elements for AI ability, we composed 10 educational programs. To confirm the appropriateness of designed contents, we verified through content validity test by 10 experts. The CVI value was over 0.75, which was highly valid. The developed content was installed on the online system and applied to 55 AI beginners for 4 weeks. The learners showed a positive result of at least 3.85 in the items of content difficulty, understanding, effectiveness, and learning challenge. As a result of this analysis, we can see that the developed content is positive for helping many people understand AI and improving AI literacy.

• The Journal of Information Systems
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• v.28 no.3
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• pp.141-158
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• 2019

Purpose Artificial Intelligence (AI) is a core technology, leading the 4th industrial revolution. This study aims to diagnose the Korean's national competitiveness for AI technologies through patent analyses. Design/methodology/approach In this study, KIWEE and Derwent Innovation databases were used as data source of patents. we extracted 10,510 AI patents data with keywords and classified them into 15 subcategories of AI technology. We executed patent analyses for activity index, patent intensity index, technology strength, and patent family size and diagnosed Korea's national competitiveness in AI industry. Findings The results showed that Korea is less competitive than the United States and Japan in AI industry. However, patent amount has increased since 2010, which is encouraging result. This study has implication on the need for human and R&D investment in AI industry.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.

• Clinical and Experimental Reproductive Medicine
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• v.51 no.1
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• pp.1-12
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• 2024

This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.48-56
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• 2024

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

• Health Policy and Management
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• v.34 no.2
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• pp.103-105
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• 2024

Artificial intelligence (AI) technology is rapidly spreading across various industries. Accordingly, interest in ethical issues arising from the use of AI is also increasing. This is particularly true in the healthcare sector, where AI-related ethical issues are a significant topic due to its focus on health and life. Hence, this issue aims to examine the ethical concerns when using AI tools during research and publication processes. One of the key concerns is the potential for unintended plagiarism when researchers use AI tools for tasks such as translation, citation, and editing. Currently, as AI is not given authorship, the researcher is held accountable for any ethical problems arising from using AI tools. Researchers are advised to specify which AI tools were used and how they were employed in their research papers. As more cases of ethical issues related to AI tools accumulate, it is expected that various guidelines will be developed. Therefore, researchers should stay informed about global consensus and guidelines regarding the use of AI tools in the research and publication process.

• The Journal of Bigdata
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• v.5 no.2
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• pp.121-133
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• 2020

This study was carried out to set the direction of the new industry policy of Busan city by analyzing the changing trend of artificial intelligence technology that has recently developed rapidly and predicting the direction of future development. The company wanted to draw up support measures to utilize artificial intelligence technology, which has been rapidly emerging in the market, in the region's specialized industry. Artificial intelligence is a key keyword in the fourth industrial revolution and artificial intelligence-based data utilization technology can be used in various fields from manufacturing processes to services, and is entering an era of super-fusion in which barriers between technologies and industries will be broken down. In this study, the direction of promotion for fostering Busan as an artificial intelligence city was derived based on the comparison and analysis of artificial intelligence-related ecosystems among major local governments. In this study, we wanted to present a plan to create an artificial intelligence industrial ecosystem that can be called a key policy to foster Busan as an 'AI City'. Busan's plan to foster the AI industry ecosystem is aimed at establishing a policy direction to ultimately nurture the artificial intelligence industry as Busan's future food source.

• Convergence Security Journal
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• v.23 no.1
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• pp.147-159
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• 2023

Recently, Chat GPT artificial intelligence (AI) is of keen interest to all governments, companies, and military sectors around the world. In the existing era of literacy AI, it has entered an era in which communication with humans is possible with generative AI that creates words, writings, and pictures. Due to the complexity of the current laws and ordinances issued during the recent national crisis in Korea and the ambiguity of the timing of application of laws and ordinances, the golden time of situational measures was often missed. For these reasons, it was not able to respond properly to every major disaster and military conflict with North Korea. Therefore, the purpose of this study was to revise the National Crisis Management Basic Act, which can act as a national tower in the event of a national crisis, and to promote artificial intelligence governance by linking artificial intelligence technology with the civil, government, military, and police.

• Journal of Digestive Cancer Research
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• v.12 no.1
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• pp.38-43
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• 2024

Artificial Intelligence (AI) is the intelligence of machines or software, in contrast to human intelligence. Generative AI technologies, such as ChatGPT, have emerged as valuable research tools that facilitate brainstorming ideas for research, analyzing data, and writing papers. However, their application has raised concerns regarding authorship, copyright, and ethical considerations. Many organizations of medical journal editors, including the International Committee of Medical Journal Editors and the World Association of Medical Editors, do not recognize AI technology as an author. Instead, they recommend that researchers explicitly acknowledge the use of AI tools in their research methods or acknowledgments. Similarly, international journals do not recognize AI tools as authors and insist that human authors should be accountable for the research findings. Therefore, when integrating AI-generated content into papers, it should be disclosed under the responsibility of human authors, and the details of the AI tools employed should be specified to ensure transparency and reliability.

• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.