• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.181-188
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• 2024

Traditional profanity detection methods have limitations in identifying intentionally altered profanities. This paper introduces a new method based on Named Entity Recognition, a subfield of Natural Language Processing. We developed a profanity detection technique using sequence labeling, for which we constructed a dataset by labeling some profanities in Korean malicious comments and conducted experiments. Additionally, to enhance the model's performance, we augmented the dataset by labeling parts of a Korean hate speech dataset using one of the large language models, ChatGPT, and conducted training. During this process, we confirmed that filtering the dataset created by the large language model by humans alone could improve performance. This suggests that human oversight is still necessary in the dataset augmentation process.

• The Mathematical Education
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• v.63 no.2
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• pp.123-138
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• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.43-50
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• 2024

This paper evaluates the Korean sentiment analysis performance of large language models like GPT-3.5 and GPT-4 using a zero-shot approach facilitated by the ChatGPT API, comparing them to pre-trained Korean models such as KoBERT. Through experiments utilizing various Korean sentiment analysis datasets in fields like movies, gaming, and shopping, the efficiency of these models is validated. The results reveal that the LMKor-ELECTRA model displayed the highest performance based on F1-score, while GPT-4 particularly achieved high accuracy and F1-scores in movie and shopping datasets. This indicates that large language models can perform effectively in Korean sentiment analysis without prior training on specific datasets, suggesting their potential in zero-shot learning. However, relatively lower performance in some datasets highlights the limitations of the zero-shot based methodology. This study explores the feasibility of using large language models for Korean sentiment analysis, providing significant implications for future research in this area.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.437-439
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• 2023

본 연구는 프로그래밍 협업 환경에서 생성형 AI인 ChatGPT-4를 활용한 코딩 프로세스를 제안한다. 일관성 있는 결과를 얻기 위해 프롬프트 생성, GPT 실행, 의사코드 변환, 코드 비교, 동일 코드 생성 여부 판단, 테스트 실행, 동일 결과 생성 여부 판단, 코드 검사 및 수정의 8단계를 거친다. 팀 프로젝트와 페어 프로그래밍 등의 다양한 협업 환경에 적용 가능한 이 프로세스를 통해 생성형 AI를 효과적으로 활용할 수 있음을 보여주었다는 점에서 그 의미가 있다. 본 연구는 생성형 AI를 활용한 협업 환경에서의 코딩이 본격적으로 이루어질 것으로 예상되는 이 시점에서, 인간-AI 협업 환경에서의 코딩 효율성 및 일관성을 높일 수 있을 것으로 기대한다. 이러한 연구는 인간과 AI가 함께 작업하는 미래를 위한 기초를 마련하는 데 중요한 역할을 할 것이다.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.303-320
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• 2024

This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.517-524
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• 2024

Generative AI, exemplified by models like ChatGPT, has revolutionized human-machine interactions in the 21st century. As these advancements permeate various sectors, their intersection with the arts is both promising and challenging. Despite the arts' historical resistance to AI replacement, recent developments have sparked active research in AI's role in artistry. This study delves into the potential of AI in visual arts education, highlighting the necessity of swift adaptation amidst the Fourth Industrial Revolution. This research, conducted at a 4-year global higher education institution located in Gyeongbuk, involved 70 participants who took part in a creative convergence module course project. The study aimed to examine the influence of AI collaboration in visual arts, analyzing distinctions across majors, grades, and genders. The results indicate that creative activities with AI positively influence students' creativity and digital media literacy. Based on these findings, there is a need to further develop effective educational strategies and directions that incorporate AI.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.471-485
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• 2024

This study explores the effective utilization of generative AI for conducting in-depth interviews within the lean startup environment. With recent technological advancements, the application of generative AI in enhancing operational productivity has been on the rise across various organizations, and this trend extends to the lean startup milieu. The research develops specific guidelines and a guidebook aimed at assisting practitioners in lean startups to conduct in-depth interviews using AI, even amidst the constraints of limited time and capital. The proposed guidebook facilitates practitioners to swiftly design and conduct interviews, thereby promoting an agile and flexible working environment within lean startups. Moreover, this study investigates practical methods for applying text-based generative AI services like ChatGPT 4 and Luyten in the fields of design and interviewing, thereby contributing to the academic discussion and practical implementation in these areas. The significance of this research lies in its potential to broaden the horizon of scholarly debate and practical application of generative AI in lean startups.