• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.432-433
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• 2023

오늘날 AI 기술은 다양한 분야에서 활용되며 발전해나가고 있다. 하지만 AI 모델의 복잡도가 증가하며 AI의 산출 결과의 해석이 불가능한 Black-box 성격을 지니게 되었고, 이는 실 환경에서 AI 도입의 커다란 걸림돌로 작용하고 있다. 이에 따라 AI 판단 결과에 대한 Interpretation을 제공하는AI Decision Support의 중요성이 커지는 추세이다. 본 논문에서는 Reference 기반 Rule을 통해 AI 모델의 판단 결과에 대한 해석을 제공하고 입력된 데이터에 관한 Rule 적합도를 산출하여 AI Decision Support를 제공하고자 한다. 또한, Rule 적합도 정보를 기반으로 기존의 모델보다 정확한산출 결과를 통해 수집된 데이터의 Label을 확정시킨다. 이를 토대로 AI 모델의 업데이트를 실행하여 지속적으로 AI의 성능을 개선하면서도 지속 운용이 가능한 AI 운용 프레임워크를 제안한다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.75-79
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• 2023

문서에서 테이블은 중요한 정보들을 축약하여 모아 놓은 정보 집합체라고 할 수 있다. 이러한 테이블을 대상으로 질의응답하는 테이블 질의응답 기술이 연구되고 있으며, 이 중 언어모델을 이용한 연구가 좋은 결과를 보이고 있다. 본 연구에서는 최근 주목받고 있는 생성형 언어모델 기술을 테이블 질의응답에 적용하여 언어모델과 프롬프트의 변경에 따른 결과를 살펴보고, 단답형 정답과 생성형 결과의 특성에 적합한 평가방법으로 측정해 보았다. 자체 개발한 EXAONE 1.7B 모델의 경우 KorWiki 데이터셋에 대해 적용하여 EM 92.49, F1 94.81의 결과를 얻었으며, 이를 통해 작은 크기의 모델을 파인튜닝하여 GPT-4와 같은 초거대 모델보다 좋은 성능을 보일 수 있음을 확인하였다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.415-418
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• 2023

테이블 데이터는 일반적으로 헤더와 데이터로 구성되며, 헤더는 데이터의 구조와 내용을 이해하는데 중요한 역할을 한다. 하지만 웹 스크래핑 등을 통해 얻은 데이터와 같이 다양한 상황에서 헤더 정보가 누락될 수 있다. 수동으로 헤더를 생성하는 것은 시간이 많이 걸리고 비효율적이기 때문에, 본 논문에서는 자동으로 헤더를 생성하는 태스크를 정의하고 이를 해결하기 위한 모델을 제안한다. 이 모델은 BART를 기반으로 각 열을 구성하는 텍스트와 열 간의 관계를 분석하여 헤더 텍스트를 생성한다. 이 과정을 통해 테이블 데이터의 구성요소 간의 관계에 대해 이해하고, 테이블 데이터의 헤더를 생성하여 다양한 애플리케이션에서의 활용할 수 있다. 실험을 통해 그 성능을 평가한 결과, 테이블 구조 정보를 종합적으로 활용하는 것이 더 높은 성능을 보임을 확인하였다.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.154-163
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• 2023

With the rapid development of AI technology, ChatGPT and other AI content creation tools are becoming common, and users are becoming curious and adopting them. These tools, unlike search engines, generate results based on user prompts, which puts them at risk of inaccuracy or plagiarism. This allows unethical users to create inappropriate content and poses greater educational and corporate data security concerns. AI content detection is needed and AI-generated text needs to be identified to address misinformation and trust issues. Along with the positive use of AI tools, monitoring and regulation of their ethical use is essential. When detecting content created by AI with an AI content detection tool, it can be used efficiently by using the appropriate tool depending on the usage environment and purpose. In this paper, we collect data on AI content detection tools and compare and analyze the functions and characteristics of AI content detection tools to help meet these needs.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.71-76
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• 2022

The innovative development of the 4th industry and the COVID-19 pandemic caused a great change in the education, eventually requiring elementary, middle and high schools, including kindergartens, to implement artificial intelligence(AI) education. However, since early childhood AI education is conducted in the form of results-oriented and special activities, the need for research on what early childhood AI education is and how to apply it to the Nuri curriculum has been raised. Accordingly, this study defined early childhood AI education through literature research, identified the contents of AI education, and organized and operated it in the Nuri curriculum. As a results, AI education for children should be conducted for the purpose of cultivating digital capabilities based on computing thinking skills, and computers, the Internet, and programs were extracted as sub-elements of child AI education contents. Two approaches were proposed to incorporate this into the Nuri curriculum. The first is to set each of the three AI education contents as a life theme, select sub-factors accordingly, and plan and implement activities suitable for each sub-factors. The second is to develop and operate AI education contents at the level of sub-educational activities in accordance with the life theme of the existing Nuri curriculum. It is hoped that this study will consider the characteristics of early childhood education and be organized in the Nuri curriculum to realize the true meaning of early childhood AI education, and more research on AI play education programs according to the five areas of the Nuri curriculum.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.973-985
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• 2021

The purpose of this study is to suggest the direction and implications of learning with AI in the future by analyzing the trends of research learning with AI in the field of education. For doing this, the final 78 papers published in domestic journals over the past three years from 2019 to July 2021 were selected for analysis through review. The analysis results are as follows. First of all, papers in 2020 among the three years were most published, and the most utilized research method was the qualitative research. In addition, according to the analysis by study subject, studies on elementary school students were the most common, followed by studies on college and graduate students. In the analysis by subject, research related to foreign language education was most utilized and chatbot was most used in the AI technology type. Finally, the research learning with AI accounted for the majority, and student support accounted for the majority as the type of education system learning with AI at the implementation stage among the areas of teaching and learning and evaluation. Based on these results, the direction and implications of learning with AI in the future were presented. This study is meaningful in that it grasped research trends of learning with AI in domestic from an overall perspective, and examined learning with AI focusing on the instructor-learner and the teaching and learning design process.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• Journal of Korean Library and Information Science Society
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• v.48 no.4
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• pp.21-47
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• 2017

This study aims to analyze features of topics about AI(Artificial Intelligence) which is gaining a massive attention these days. Newspaper articles published from 2016 to June, 2017 were selected to analyze key subjects. The reason why the period was selected is people started to get attention on AI since 2016 as AlphaGo came out and gave a shock. The number of coded main message was 1,210 in 525 newspaper articles in total. The messages were categorized as three subject categories: the seven major categories, 62 middle categories. and minor categories. The seven major categories contains issues such as AI research, AI application, AI business, AI era, AI argument, AlphaGo, and other topics. The first features of issues about AI found in the major subject categories is that they are various and complicate. Second, it is important that social and policy-level issues related AI, such as job losses, misuse, and error should be dealt with to utilize AI safely. Last, issues related the role of human and revolution of education system in the AI era were shown as subjects which are important but hard to discuss.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.743-759
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• 2023

Collaboration between AI and fashion designers is becoming essential. Thus, this study explored (1) 321 consumer responses to fashion designers, comparing their capabilities and product quality across different designer types, (2) the relationship between designer capabilities and perceived product quality, and (3) the moderating role of AI knowledge in the effect of capabilities on perceived product quality. Data were analyzed using EFA, ANOVA, regression, and moderation analysis. The results indicated that subjects perceived human designers as having higher capabilities and perceived product quality than AI designers. All subjects' perceived creativity and empathy significantly impacted the perceived functionality, aesthetics, and symbolism-sociality of clothing. Additionally, the perceived creativity of AI and human+AI designers, and the perceived empathy of human and human+AI designers, significantly influenced the perceived functionality and symbolism-sociality, but the perceived creativity of human designers and empathy of AI designers did not directly impact perceived functionality and symbolism-sociality. Moreover, perceptions of the designers' capabilities significantly aesthetics in all subjects. Furthermore, low levels of perceived consumer AI knowledge enhanced the positive impact of perceived human+AI designers' creativity and empathy on perceived functionality and aesthetics. The study suggests that fashion companies should refrain from revealing AI designers at this time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.182-184
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• 2022

As artificial intelligence (AI) is emphasized in elementary and secondary school education, interest in AI-applied class activities is increasing. Since AI is taught across various subjects in schools, teachers must plan lessons based on the principles of convergence education. In this paper, the concept of convergence education and matters to be considered for productive class activities were reviewed. Then, considerations for designing AI classes in schools are presented in the following aspects: characteristics of AI education in schools; educational goals for each school level in the general guidelines of the national curriculum; resources to be referenced when composing class content; perspectives on AI-applied software; and anticipated instructional procedures. As a suggestion, the following is presented. First, it is necessary to derive competencies that can be cultivated by AI education in school. Second, it is necessary to specify the design elements and procedures of AI classes based on the subject characteristics.