• Journal of Platform Technology
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• v.11 no.5
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• pp.72-83
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• 2023

ChatGPT, commercial launch in late 2022, has shown successful results in various professional exams, including US Bar Exam and the United States Medical Licensing Exam (USMLE), demonstrating its ability to pass qualifying exams in professional domains. However, further experimentation and analysis are required to assess ChatGPT's scholastic capability, such as logical inference and problem-solving skills. This study evaluated ChatGPT's scholastic performance utilizing the Korean College Scholastic Ability Test (KCSAT) subjects, including Korean, English, and Mathematics. The experimental results revealed that ChatGPT achieved a relatively high accuracy rate of 69% in the English exam but relatively lower rates of 34% and 19% in the Korean Language and Mathematics domains, respectively. Through analyzing the results of the Korean language exam, English exams, and TOPIK II, we evaluated ChatGPT's strengths and weaknesses in comprehension and logical inference abilities. Although ChatGPT, as a generative language model, can understand and respond to general Korean, English, and Mathematics problems, it is considered weak in tasks involving higher-level logical inference and complex mathematical problem-solving. This study might provide simple yet accurate and effective evaluation criteria for generative artificial intelligence performance assessment through the analysis of KCSAT scores.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.303-323
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• 2023

The purpose of this study is to introduce this language model in the era of generative AI such as ChatGPT, and to provide direction for data literacy education components in libraries using it. To this end, the following three research questions are proposed. First, the technical features of ChatGPT-like language models are examined, and then, it is argued that data literacy education is necessary for the proper and accurate use of information by users using a service platform based on generative AI technology. Finally, for library data literacy education in the ChatGPT era, it is proposed a data literacy education scheme including seven components such as data understanding, data generation, data collection, data verification, data management, data use and sharing, and data ethics. In conclusion, since generative AI technologies such as ChatGPT are expected to have a significant impact on users' information utilization, libraries should think about the advantages, disadvantages, and problems of these technologies first, and use them as a basis for further improving library information services.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.43-44
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• 2023

대화형 독해 튜터링 시스템을 위한 학생주도 대화 데이터셋 생성 및 확장에 ChatGPT 의 활용 가능성을 평가하였다. 단순히 수동으로만 구축한 기존의 데이터셋과 ChatGPT 에 의해 반자동으로 확장된 데이터셋을 비교한 결과, 구축량, 소요 시간, 비용 및 반복 작업 측면에서 ChatGPT 가 가진 유용성을 알 수 있었다. 그러나, 유형별 배분의 편중과, 부적절한 데이터 생성 등의 한계도 나타났다. Chat GPT 의 빠른 발전이 예상됨에 따라 대화형 튜터링 분야에 ChatGPT 에 의한 반자동 데이터 확장 방법이 널리 활용될 것으로 기대된다.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.71-78
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• 2023

ChatGPT significantly broadens the application of artificial intelligence (AI) services across various domains, with one of its primary functions being assistance in programming and coding. Nevertheless, due to the short history of ChatGPT, there have been few studies analyzing its coding capabilities in Korean higher education. In this paper, we evaluate it using exam questions from three foundational programming courses at S University. According to the experimental results, ChatGPT successfully generated Python, C, and JAVA programs, and the code quality is on par with that of high-achieving students. The powerful coding capabilities of ChatGPT imply the need for a strict prohibition of its usage in coding tests; however, it also suggests significant potential for enhancing practical exercises in the educational aspect.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.53-71
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• 2023

This study explores the relationship between various user factors and the level of trust in ChatGPT, a sophisticated language model exhibiting human-like capabilities. Specifically, we considered demographic characteristics such as age, education, gender, and major, along with factors related to previous AI experience, including duration, frequency, proficiency, perception, and familiarity. Through a survey of 140 participants, comprising 71 females and 69 males, we collected and analyzed the data to see how these user factors have a relationship with trust in ChatGPT. Both descriptive and inferential statistical methods, encompassing multiple linear regression models, were employed in our analysis. Our findings reveal significant relationships between user factors such as gender, the perception of prior AI interactions, self-evaluated proficiency, and Trust in ChatGPT. This research not only enhances our understanding of trust in artificial intelligence but also offers valuable insights for AI developers and practitioners in the field.

• Journal of the Korean Society for information Management
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• v.40 no.2
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• pp.183-209
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• 2023

The purpose of this study is to evaluate the Dublin Core metadata generated by ChatGPT using book covers, title pages, and colophons from a collection of books. To achieve this, we collected book covers, title pages, and colophons from 90 books and inputted them into ChatGPT to generate Dublin Core metadata. The performance was evaluated in terms of completeness and accuracy. The overall results showed a satisfactory level of completeness at 0.87 and accuracy at 0.71. Among the individual elements, Title, Creator, Publisher, Date, Identifier, Rights, and Language exhibited higher performance. Subject and Description elements showed relatively lower performance in terms of completeness and accuracy, but it confirmed the generation capability known as the inherent strength of ChatGPT. On the other hand, books in the sections of social sciences and technology of DDC showed slightly lower accuracy in the Contributor element. This was attributed to ChatGPT's attribution extraction errors, omissions in the original bibliographic description contents for metadata, and the language composition of the training data used by ChatGPT.

• Journal of Korean Biological Nursing Science
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• v.25 no.3
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• pp.183-204
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• 2023

Purpose: The purpose of this study was to evaluate the applicability of ChatGPT in biological nursing science education. Methods: This study was conducted by entering questions about the field of biological nursing science into ChatGPT versions GPT-3.5 and GPT-4 and evaluating the answers. Three questions each related to microbiology and pharmacology were entered, and the generated content was analyzed to determine its applicability to the field of biological nursing science. The questions were of a level that could be presented to nursing students as written test questions. Results: The answers generated in English had 100.0% accuracy in both GPT-3.5 and GPT-4. For the sentences generated in Korean, the accuracy rate of GPT-3.5 was 62.7%, and that of GPT-4 was 100.0%. The total number of Korean sentences in GPT-3.5 was 51, while the total number of Korean sentences in GPT-4 was 68. Likewise, the total number of English sentences in GPT-3.5 was 70, while the total number of English sentences in GPT-4 was 75. This showed that even for the same Korean or English question, GPT-4 tended to be more detailed than GPT-3.5. Conclusion: This study confirmed the advantages of ChatGPT as a tool to improve understanding of various complex concepts in the field of biological nursing science. However, as the answers were based on data collected up to 2021, a guideline reflecting the most up-to-date information is needed. Further research is needed to develop a reliable and valid scale to evaluate ChatGPT's responses.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.659-666
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• 2023

Among the tools based on generative artificial intelligence, the possibility of using ChatGPT is being explored. However, studies that have confirmed what factors should be considered when using it educationally based on learners' actual perceptions are insufficient. Through qualitative research method, this study was to derive consideration factors when using ChatGPT in the education. The results showed that there were five key factors as follows: critical thinking on generated information, recognizing it as a tool to support learning and avoiding dependent use, conducting prior training on ethical usage, generating clear and appropriate questions, and reviewing and synthesizing answers. It is necessary to develop an instructional design model that comprehensively composes the above elements.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.12-12
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• 2023

지하수자원 관리의 정량적 및 정성적 측면에 있어, 최첨단 인공지능 언어 모델인 ChatGPT의 혁신적인 기능이 활용될 수 있다. 본 발표에서는 지하수 자료에 대한 분석과 도출된 문제의 중요도에 따른 목표를 설정, 그리고 지하수 관리 전략 개발에 있어서의 ChatGPT 활용 방법을 논의할 것이다. 이를 위한 구체적 사례로, 지하수자원 관리에 활용될 수 있는 다양한 도구들의 개발과 고도화에 ChatGPT가 기여하는 방식을 살펴볼 것이다. 이러한 개별 도구들은 지하수자원 관리 결정에 있어 더 나은 예측 및 평가를 제공하여, 지하수 자원 관리의 효율성을 도모할 수 있다. 또한, ChatGPT의 문제 발견 및 해결책 제안 능력에 대해서도 다룰 것이다. 이를 통해 지하수 관리에 있어서의 다양한 문제를 식별하고, 이해당사자들이 보다 효과적으로 대응할 수 있는 방안을 찾아낼 수 있을 것이다. 또한 ChatGPT가 제공하는 다양한 정보 및 문제에 대한 솔루션 접근 방식을 활용한 브레인스토밍 방법을 설명할 것이다. 추가적으로, 일반 인공지능(AGI)의 개발에 근접하면서 지하수 관리의 자동화 및 가속화 그리고 산업 및 환경에 미칠 수 있는 영향에 대해 고찰해 볼 것이다. 이를 위하여, ChatGPT와 같은 인공지능 기술이 더욱 고도화되고 향상되면서, 지하수 관리 및 관련 분야에서의 의사결정, 계획 수립, 그리고 모니터링과 같은 작업들이 어떻게 변화할지에 대하여 토의할 것이다. 본 발표는 지하수 자원 관리 분야에서 ChatGPT와 같은 인공지능 기반 접근법의 가치를 보여주며, 복잡한 지하수 환경 문제를 해결하는 데 있어 첨단 기술의 활용 가능성을 강조할 것이다. 또한, AGI가 등장할 때까지 여전히 요구되는 지하수 분야 도메인 지식과 전문기술의 중요성을 강조할 것이다. 지하수 관리자들의 도메인 지식과 전문적 기술은 인공지능 기반 도구와 결합되어 보다 정확한 분석, 예측 및 해결책 도출을 가속화하며 정교화할 것이다. 결론적으로, 지하수 관리에 대한 종합적인 이해와 전문성을 갖춘 전문가들의 인공지능 기술활용은 지속가능한 지하수의 첨단 관리 효과적 달성에 중요한 계기가 될 것으로 판단한다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.205-211
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• 2023

In this study, we analyzed the potential application and validity of ChatGPT, an artificial intelligence technology currently gaining attention across various fields, for the creation of advertising photographs. To do this, we examined the relationship between the visual elements of advertising photographs and language, and investigated use cases of ChatGPT in advertising. Furthermore, we analyzed the elements of each stage in the advertising photograph creation process and conducted expert interviews to determine the validity of ChatGPT's application in these stages. The results revealed that, although somewhat limited, the feasibility of using ChatGPT was found to be high in the planning stage of advertising photographs, but lower in the actual shooting and post-production stages. Considering these findings, it is necessary to continuously monitor the progress of AI technology and strive to enhance the creativity and efficiency of advertising photograph production through collaboration between technology and humans.