• 디지털융복합연구
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• 제19권6호
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• pp.1-9
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• 2021

본 연구는 인공지능(AI) 디바이스가 차세대 정보통신기술(ICT)의 핵심 플랫폼으로 급부상하고 있고, 소비자들의 일상에 널리 적용되고 있는 인공지능 디바이스를 통해 소비자의 사용행태 및 사용자 경험에 대해 살펴보았다. 이를 위해 AI 디바이스 사용 경험이 있는 국내 소비자 600명을 대상으로 AI 디바이스의 속성 인식과 사용행태를 도출하였다. 분석결과는 다음과 같다. 첫째, 다양한 속성 중 음악청취를 가장 많이 이용하였고, 날씨 정보제공과 같은 단순한 기능을 유용하게 인식하는 것으로 나타났다. 둘째, AI 디바이스 사용자의 주요 사용기기는 AI 스피커, 스마트폰, PC, 노트북 등으로 확인되었다. 셋째, AI 디바이스에 대한 연상 이미지는 재미있는, 유용한, 신기한, 똑똑한, 혁신적인, 친근한 순으로 나타났다. 따라서 본 연구는 AI 디바이스의 특성을 반영한 사용 행태를 분석함으로써 향후 AI 디바이스를 활용한 사용자의 서비스 제공에 기여할 수 있다는 실무적 시사점을 갖는다.

• 한국정보시스템학회지:정보시스템연구
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• 제33권1호
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• pp.1-30
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• 2024

Purpose The purpose of this study is to examine the customer experience of generative AI among office workers aged 30 to 40, investigating usability, usefulness, and affect, and understanding concerns and expectations. Design/Methodology/Approach This research used Q methodology to assess the customer experience of generative AI. Users are engaged in a problem-solving journey, and data is collected by having participants rank 36 statements based on usability, usefulness, and affect, referred to as the three goals of User Experience. Participants use a forced distribution table with a scale from -5 to +5 to indicate the subjective importance of each statement. The results identified four groups, reflecting different perspectives and attitudes toward generative AI. Findings Participants express overall comfort with generative AI, perceive AI as more knowledgeable in unfamiliar domains, but harbor doubts about AI's understanding. Disagreements emerge on AI replacing humans, the value of unique human roles, data confidentiality, fears of AI advancement, and emotional impacts. Identified four groups: Users who treat AI as a soulless assistant and are active in business use, Uncle users who want to use new technologies properly and are not afraid of technology, users who recognize the limits of AI despite its efficiency, and users who require strong verification in the future. It has the potential to guide future guidelines, ethical codes, and regulations for the appropriate use of AI. In addition, this approach lays the groundwork for future empirical analyses of generative AI.

• 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.

• 디지털산업정보학회논문지
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• 제19권4호
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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.

• International Journal of Advanced Culture Technology
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• 제10권2호
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• pp.62-68
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• 2022

Evaluation as learning is important for the learner competency test, and the applicable method is studied. Assessment is the role of diagnosing the current learner's status and facilitating learning through appropriate feedback. The system is insufficient to enable process-oriented evaluation in small educational institute. Focusing on becoming familiar with the AI through experience can end up simply learning how to use the tools or just playing with them rather than achieving ultimate goals of AI education. In a previous study, the experience way of AI education with PLAY model was proposed, but the assessment stage is insufficient. In this paper, we propose ELAS (Experiential K-12 AI education Learning Assessment System) for small educational institute. In order to apply the Assessment factor in in this system, the AI-factor is selected by researching the goals of the current SW education and AI education. The proposed system consists of 4 modules as Assessment-factor agent, Self-assessment agent, Question-bank agent and Assessment -analysis agent. Self-assessment learning is a powerful mechanism for improving learning for students. ELAS is extended with the experiential way of AI education model of previous study, and the teacher designs the assessment through the ELAS system. ELAS enables teachers of small institutes to automate analysis and manage data accumulation following their learning purpose. With this, it is possible to adjust the learning difficulty in curriculum design to make better for your purpose.

• 한국보육지원학회지
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• 제19권2호
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• pp.43-59
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• 2023

Objective: This study investigated the differences in preschool children's perceptions of artificial intelligence (AI) and their distribution by latent profiles according to their experience with AI robots in daycare centers. Methods: The participants included 119 five-year-old children, 52 of whom had experience with AI robots in daycare centers and 67 of whom did not. Children's perceptions of AI were measured using the Godspeed scale from Bartneck et al.(2009). Data were analyzed using a t-test, latent profile analysis, and chi-square test. Results: The results showed that compared to the inexperienced group, the experienced group reported lower levels of animacy and perceived intelligence of AI robots, indicating higher levels of AI knowledge and understanding. In addition, the experienced group had a higher probability of belonging to the 'machine recognition' type than 'organism recognition' type, although the difference was not statistically significant. Conclusion/Implications: The findings suggest that experience with AI robots in daycare centers can improve children's AI knowledge and understanding. To further enhance this effect, it is necessary to increase the number of robots put into classrooms, and to consider various teaching media that reflect children's preferences.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
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• pp.395-396
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• 2023

본 논문에서는 디지털 전환 시대에 모든 사람들이 인공지능(AI) 체험부터 피지컬컴퓨팅을 통해서 SW·AI 융합해 아이디어를 쉽게 구현하고 교육 받을 수 있는 플랫폼을 구현하였다. AI 체험을 위해 P5.js와 텐서플로우에 기반한 ML5.js 라이브러리를 이용해 블록 코딩을 할 수 있도록 하였다. 또한 피지컬컴퓨팅에서는 마이크로비트와 아두이노, 라즈베리파이 등을 WebUSB를 통해서 PC와 연결하고 플랫폼에서 인공지능의 다양한 서비스와 융합시킬 수 있도록 제공한다.

• 패션비즈니스
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• 제26권1호
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• 방송공학회논문지
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• 제27권5호
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• pp.694-702
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• 2022

본 논문은 메타버스 환경에서 대화형 AI가 어떻게 윤리적으로 진화될 수 있을지에 대한 솔루션을 UX(사용자경험) 관점으로 찾아보는 기술 전략 연구이다. 대화형 AI는 사람들과의 직접적인 인터랙션을 통해 사람들의 온·오프라인의 결정요소에 영향을 미치기 때문에 메타버스 AI 윤리가 필수적으로 반영되어야 한다. 대화형 AI의 머신러닝의 과정에는 사용자 개인의 경험데이터와 함께 문화적 코드들이 포함되고 고려되어야 사용자경험의 오류값을 줄일 수 있다. 이를 통해 초 개인화된 메타버스의 서비스가 사회적 가치를 고려하며 윤리적으로 진화할 수 있다. 위와 같은 가설을 기반으로 본 논문의 연구 결과로 메타버스 서비스 환경에서 컨택스트 기반의 대화형 AI를 위한 머신러닝(ML)과정에 사용자의 경험데이터를 추가한 선행적 관점의 개념 모델을 개발, 제안하였다.

• 한국콘텐츠학회논문지
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• 제18권8호
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• pp.92-101
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• 2018

대화형 AI 스피커가 보편화되면서 음성인식은 자율주행 상황에서의 중요한 차량-운전자 인터랙션 방식으로 인식되고 있다. 이 연구의 목적은 반자율주행 상황에서 음성뿐만 아니라 AI 캐릭터의 시각적 피드백을 함께 전달하는 멀티모달 인터랙션이 음성 단일 모드 인터랙션보다 사용자 경험 최적화에 효과적인지를 확인하는 것이다. 실험 참가자에게 주행 중 AI 스피커와 캐릭터를 통해 음악 선곡과 조정을 위한 인터랙션 태스크를 수행하게 하고, 정보 및 시스템 품질, 실재감, 지각된 유용성과 용이성, 그리고 지속 사용 의도를 측정하였다. 평균차이 분석 결과, 대부분의 사용자 경험 요인에서 시각적 캐릭터의 멀티모달 효과는 나타나지 않았으며, 지속사용 의도에서도 효과는 나타나지 않았다. 오히려, 정보품질 요인에서 음성 단일 모드가 멀티모달보다 효과적인 것으로 나타났다. 운전자의 인지적 노력이 필요한 반자율주행 단계에서는 멀티모달 인터랙션이 단일 모드 인터랙션에 비해 사용자 경험 최적화에 효과적이지 않았다.