• 지식경영연구
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• 제25권2호
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• pp.49-68
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• 2024

최근 개인정보 공유에 대한 사람들의 우려가 높아지면서 온라인 마케팅을 통해 고객 데이터를 수집하는 것이 점점 어려워지고 있다. 본 연구에서는 생성형 AI 기반 챗봇을 이용하여 고객의 정보 제공의도를 향상시키기 위한 효과적인 요인을 탐색하고자 한다. 보다 구체적으로, 프라이버시 계산이론과 조절초점 이론을 바탕으로 고객의 성향과 생성형 AI 챗봇에 대한 친숙도가 고객의 개인정보 제공의도에 어떻게 영향을 미치는지 살펴보았다. 473명의 참가자를 이용한 실험 결과에 따르면 생성형 AI 기반 챗봇에 대한 친숙도가 낮은 경우, 예방초점 성향의 참가자가 향상초점 성향의 참가자보다 프라이버시 위험을 높게 인식하고 유용성을 더 낮게 지각한 반면, 챗봇에 대한 친숙도가 높은 경우, 예방초점과 향상초점 참가자 간의 프라이버시 위험과 인지된 유용성에는 차이가 나타나지 않았다. 개인정보 제공의도 역시 생성형 AI 기반 챗봇에 대한 친숙도가 낮은 경우, 향상초점 성향의 참가자가 예방초점 성향의 참가자보다 개인정보 제공의도가 더 높게 나타난 반면 챗봇에 대한 친숙도가 높은 경우, 예방초점과 향상초점 참가자 간의 개인정보 제공의도에는 차이가 나타나지 않았다. 이는 개인정보 제공의도에 대한 프라이버시 위험에 의해 매개되었다. 본 연구는 고객의 개인정보 공개를 촉진하기 위해서는 고객의 내재적 성향과 함께 생성형 AI 기반 챗봇에 대한 친숙도를 함께 고려해야 한다는 시사점을 제공하며 더불어 생성형 AI 챗봇에 대한 관련 연구 분야에 기여한다.

• 산업과 과학
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• 제3권1호
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• pp.1-16
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• 2024

본 연구는 고등학교 학생들의 데이터 리터러시를 함양할 수 있는 머신러닝 기반 AI 융합 수업 모형과 수업 설계 원리를 개발하고, 그에 따른 상세 지침을 개발하는 것을 목적으로 하였다. 이를 위해 선행 문헌 연구를 통해 머신러닝을 기반으로 한 수업 모형과 설계 원리 및 상세 지침을 개발하고, 서울 소재 상업계열 특성화고등학교 학생 15명에게 적용하여 실행하였다. 연구 결과 학생들의 데이터 리터러시가 통계적으로 유의미(p< .001)하게 향상되었으므로 본 연구의 수업 모형이 학습자의 데이터 리터러시 향상에 긍정적인 영향을 주었음을 확인할 수 있었고, 앞으로 관련 연구로 이어지길 기대한다.

• 전자통신동향분석
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• 제35권7호
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• pp.46-55
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• 2020

As artificial intelligence (AI) technology becomes more important, the demand for AI talent is increasing. However, there is a shortage of AI talent around the world, and it is difficult to secure. Therefore, it has become more important to nurture the AI workforce. The private sector and government in Korea and other countries are making an effort to cultivate AI talent, and ETRI has proposed "Nurturing AI Professionals" as ETRI AI Strategy #5 to meet both internal and national demands for AI talent. ETRI has suggested three key tasks to implement AI Strategy #5. The first one is to create a "top-notch AI talent training project: the ETRI AI Academy" to strengthen AI research capabilities. The second one is "nurturing AI engineers specialized in local-based industries: the ETRI AI Business School" to help supply the necessary AI workforce in the industry. The third one is the "contribution to AI education service for people: ETRI AI Literacy" to raise the public's understanding and utilization of AI.

• 전자통신동향분석
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• 제35권5호
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• pp.69-82
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• 2020

In many technologies, artificial intelligence (AI) is becoming an important topic for areas based on the field of big data. However, applied AI cases and the research status of radio wave technology are not widely known to the public. The spread of AI to other areas is being followed by radio wave technologies, and much effort is being taken to evolve it into intelligent radio wave technologies in the future. This paper presents the recent areas of interest in radio wave technology, such as spectral sharing, illegal spectrum monitoring, radar detection, radio wave medical imaging, and channel modeling; examines the requirements for applying AI; and describes the applied cases, research trends, and standardization efforts that apply AI technology to them. On this basis, we will discuss the prospects of AI application to the expected radio wave technology of the future.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1240-1245
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• 1990

Intelligent Control is an extended paradigm that subsumes both control and AI paradigms, each of which is limited by its own abstractions. Autonomy, as a design goal, offers an arena where both control and AI paradigms must be applied -and a challenge to the viability of both as independent entities. We discuss hierarchical event-based control architectures in which AI and Control paradigms can be integrated within a model-based approach. In a niodel-based system, knowledge is encapsulated in the form of models at the various layers to support the predefined system objectives. Concepts are illustrated with a robotmanaged space-borne chemical laboratory.

• Educational Technology International
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• 제24권2호
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• pp.143-168
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• 2023

This study aims to analyze the perceptions and educational needs of pre-service teachers for the use of Artificial Intelligence (AI) in education. To this end, we collected survey data from 25 undergraduate students who were enrolled in a teacher education college in Seoul. The purpose of the survey was to measure the importance and current performance for instructional AI use based on the technological, pedagogical, and content knowledge (TPACK) framework, and to explore the priority of educational needs using Borich's needs analysis and the Locus for Focus model. The results of the study confirmed that Ethics and TPK competencies are prioritized. Additionally, the results indicated a high demand for practical knowledge that can be implemented in the practice of education. Based on the results, it is necessary to develop a teacher education program that focuses on ethical aspects and teaching strategy competencies in AI-based education.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.182-184
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• 2022

초·중등학교 교육에서 인공지능(AI)이 강조됨에 따라, 교과 활동에 AI를 접목한 수업에 관한 관심이 높아지고 있다. 학교에서의 AI 수업은 관련 교과뿐만 아니라 다양한 교과를 통해 이루어지므로, 교수자는 융합교육에 대한 이해를 바탕으로 교수·학습 및 평가를 설계할 필요가 있다. 이에 따라, 본 논문에서는 먼저 융합교육의 의미와 효과적인 수업 활동을 위해 검토할 사항을 살펴보았다. 다음으로 초·중등학교에서의 AI 수업 설계를 위해 고려할 사항에 대해 학교에서의 AI 교육의 특징, 교육과정 총론에 제시된 학교급별 교육목표, 수업 내용 구성을 위해 참고할 자료, AI가 적용된 소프트웨어에 대한 관점, 예상 수업 절차의 측면에서 제시하였다. 제언으로서 첫째, 초·중등학교 교육의 특징에 기반하여 AI 교육을 통해 함양할 수 있는 역량 도출의 필요성과 둘째, 학교에서의 AI 교육의 기존 사례 탐구를 바탕으로 교과 특성을 반영한 AI 수업의 교수·학습 설계 요소 및 절차 규명의 필요성을 제시하였다.

• 전자통신동향분석
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• 제35권7호
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• pp.56-66
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• 2020

As the development of artificial intelligence (AI) technology spreads to various industrial sectors, diversity in AI utilization rapidly increases, creating rich user experience. In addition, AI is required to solve various social problems through the use of public data. The spread of AI utilization across all sectors will continue, covering such industrial and public demands. This article examines the domestic and international trends in AI utilization technologies and establishes the direction of research and development (R&D), which is highly consistent with Korea's AI policy. ETRI, which leads AI's national R&D, has used its experience to establish AI R&D implementation strategies as well as technology roadmaps for the utilization of AI to improve individual quality of life, continuous growth in society, industrial innovation, and the solutions to public societal problems. In addition, it has derived tasks and implementation strategies for developing AI utilization technologies in 10 major areas including medical services.

• 한국IT서비스학회지
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• 제20권5호
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• pp.43-54
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• 2021

Currently, citizens' expectations and concerns on utilizing artificial intelligence (AI) technologies in the public sector are widening with the rapid digital transformation. Furthermore the level of global acceptance on the AI and other intelligent digital technologies is augmenting with the needs of non-face-to-face types of public services more than ever due to the unforeseen and unpredictable pandemic, COVID-19. Thus, this study intended to empirically examine what policy directions for the public should be considered to provide well-designed services as well as to promote the evidence-based public policies in terms of Al speaker technology as a non-contact smart city service. Based on the survey of senior citizens' perceptions on AI (AI Speaker technology), this study conducted structure equation modeling analyses to identify whether technology acceptance models on to the varied dependent variables such as actual use, perception, attitude, and brand royalty. The Results of the empirical analyses showed that AI increased the positive level of citizens' perception, attitude and brand royalty on non-contact public services (smart city services) which are becoming more crucial for developing AI oriented government and providing intelligent public services effectively. In addition, theoretical and practical implications are discussed for understanding the changes of public service in the post-corona.

• 대한방사선기술학회지:방사선기술과학
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• 제43권3호
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• pp.195-208
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• 2020

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.