• Journal of Christian Education in Korea
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• v.77
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• pp.171-201
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• 2024

Purpose of Research : Since ChatGPT's 2022 release, the educational sector faces mixed reactions to generative AI, sparking innovation but raising concerns about student cognition and communication. While Christian colleges employ AI reflecting their values, secular institutions stress ethical usage. This study explores ethical AI use in these settings, aiming to integrate findings into educational practices. Research content and method : Analyzing AI use and ethics guidelines from 50 domestic and international universities, differences between Christian and secular institutions were explored. Data was categorized, conceptualized via open coding, and components were identified through axial coding. The importance of components for Christian colleges' AI guides was assessed based on the initial data and previous research, leading to the development of tailored AI utilization components for Christian universities. Conclusion : Studies revealed secular institutions have six AI guide components, while Christian colleges found seven in both utilization and ethics guides, focusing on truthfulness, responsibility, and diversity. Emphasizing the need for ethical AI use in Christian colleges, the findings advocate developing AI ethics guidelines to aid marginalized groups and establish a new educational paradigm through further research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.613-625
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• 2024

This study analyzed the convergence case of AI painting and NFT art, art content created based on digital technology, an innovative technology of the 4th industrial technology, and explored its characteristics. Digital technology that innovates the paradigm of life in the 21st century is being used in creative art, and AI painting and NFT art that use it as an expression tool are changing the way they perceive and accept art. AI painting using big data and artificial intelligence technology is evolving into interactive daily art, and NFT art using blockchain and NFT technology is becoming the art of the metaverse with economic and cultural values. Therefore, this study attempted to explore various aspects and values of these digital convergence arts. For the study, representative examples of AI painting and NFT art were classified into cognitive creative AI painting and language generative AI, art economic NFTs, and art and cultural NFTs, and their characteristics, contents, and meanings were analyzed. It is hoped that the results of this study will contribute to the development of AI painting and NFT art, which are digital convergence arts.

• 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.

• Korean Journal of Cognitive Science
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• v.34 no.3
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• pp.197-226
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• 2023

Generative Text Summarization is one of the Natural Language Processing tasks. It generates a short abbreviated summary while preserving the content of the long text. ROUGE is a widely used lexical-overlap based metric for text summarization models in generative summarization benchmarks. Although it shows very high performance, the studies report that 30% of the generated summary and the text are still inconsistent. This paper proposes a methodology for evaluating the performance of the summary model without using the correct summary. AggreFACT is a human-annotated dataset that classifies the types of errors in neural text summarization models. Among all the test candidates, the two cases, generation summary, and when errors occurred throughout the summary showed the highest correlation results. We observed that the proposed evaluation score showed a high correlation with models finetuned with BART and PEGASUS, which is pretrained with a large-scale Transformer structure.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.1-12
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• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.

• Journal of Practical Engineering Education
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• v.16 no.5_spc
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• pp.663-676
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• 2024

This study, from the perspective of Learning with AI, aimed to explore the educational applicability of writing evaluation criteria generated by artificial intelligence. Specifically, it sought to systematically analyze the similarities and differences between AI-generated criteria and those developed by humans. The research questions for this study were set as follows: 1) What characteristics do the writing evaluation criteria generated by AI tools have? 2) What similarities and differences exist between the writing evaluation criteria generated by humans and AI tools? GPT and Claude were selected as representative AI tools, and they were tasked with generating writing evaluation criteria for undergraduate students. These AI-generated criteria were then compared with human-created criteria. The results showed a commonality: Both humans and AI-tools placed the highest importance on categories related to content. However, while humans evaluated based on three main categories - content, organization, and language usage - the AI tools included additional categories such as format and citations, original thinking, and overall impression. In general, human tended to include more detailed items within each evaluation category, while AI tools presented more concise items. Notably, differences were observed in language-related aspects and scoring systems, which were influenced by the AI tools being developed based on English. This study offers important insights into the development of collaborative evaluation models between humans and AI, and it explores the potential role of AI as a complementary tool in educational assessment in the future.

• Electronics and Telecommunications Trends
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• v.39 no.2
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• pp.43-53
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• 2024

The media environment is rapidly evolving to be tailored to viewers using personal mobile devices in accordance with technological evolution and changes in social structures. Broadcast media technology is also advancing to enable new services, including data casting, in various reception environments beyond the existing fixed environment and one-way audio/video content services. In addition, technologies to increase the transmission capacity to accommodate next-generation large-capacity media content as well as communication network utilization and convergence technologies are being developed to facilitate interactive services and expand the broadcasting coverage. We discuss the current status and future prospects in broadcasting technology for terrestrial and mobile communication systems and analyze broadcasting technology elements for upcoming media environments relying on generative artificial intelligence.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.182-190
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• 2024

Various artificial intelligence neural network models that have emerged since 2014 enable the creation of new content beyond the existing level of information discrimination and withdrawal, and the recent generative artificial intelligences such as ChatGPT and Gall-E2 create and present new information similar to actual data, enabling natural interaction because they create and provide verbal expressions similar to humans, unlike existing chatbots that simply present input content or search results. This study aims to present a model that can improve the ChatGPT communication skills of university students through curriculum research on ChatGPT, which can be participated by students from all departments, including engineering, humanities, society, health, welfare, art, tourism, management, and liberal arts. It is intended to design a way to strengthen competitiveness to embody the practical ability to solve problems through ethical attitudes, AI-related technologies, data management, and composition processes as knowledge necessary to perform tasks in the artificial intelligence era, away from simple use capabilities. It is believed that through creative education methods, it is possible to improve university awareness in companies and to seek industry-academia self-reliant courses.

• Journal of Information Technology Applications and Management
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• v.31 no.4
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• pp.139-148
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• 2024

This study takes a comprehensive look at how technological advances have changed the way 2D animation is created. Humans are constantly looking for new ways and technologies to express movement, which has led to many changes in the way 2D animation is produced. In this study, we will examine the impact of these changes on 2D animation production and explore the possibilities for future developments. In the early days of 2D animation, the production method was repeatedly changed by the invention of technologies such as celluloid sheets, rotoscopes, and multiplane cameras, while the advent of digital technology has led to revolutionary changes such as the development of CAPS(computer animation production systems), various digital tools, and the combination of 2D and 3D. In addition, the recent introduction of generative AI is rapidly changing the way 2D animation is produced by automatically handling various tasks. These advances have not only streamlined the production of animation, but have also reduced costs by shortening the production period, and greatly improved the quality of animation by making it easier to implement complex and sophisticated visual effects. The introduction of generative AI has pushed the boundaries of what can be represented in 2D animation. On the other hand, the introduction of digital technology has its drawbacks, as the mechanical and uniform style produced by digital tools can reduce originality and individuality, but advances in technology will open up the possibilities for 2D animation to be produced in a variety of ways, as it fosters the creation of new expressions and creative content.

• 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.