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- 저자 : Ismatullaev Ulugbek Vahobjon U (검색결과 3 건)
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Ismatullaev Ulugbek Vahobjon U,Sangho Kim(김상호) 대한인간공학회 2021 대한인간공학회 학술대회논문집 Vol.2021 No.6
Objective: To investigate the effects of factors on the adoption of autonomous vehicles (AVs) using a new extended technology acceptance model. Background: Since self-driving cars are being implemented and developed to use for driving purposes, it is important to find the factors that can hinder the acceptance of these technologies. Factors in traditional technology acceptance theories were used to determine the actual use of AVs. However, factors in behavior theories are not directly observable (latent variables), t herefore the effect of technological factors (manifest variables) on these factors were also studied. In addition, the differences in the acceptance of AVs regarding various human factors (e.g., education, income, driving experience) were evaluated. Method: Research hypotheses of the model were proposed to predict acceptance of AVs. Data were collected by an online survey among 81 residents of South Korea to confirm the hypotheses proposed. Structure Equation Modeling (SEM) was used to validate the relationship between factors and effects on the adoption of AVs. Results: The measurement model showed good reliability and validity after model and hypotheses testing results. A total of 18 of the 25 hypotheses were supported. Moreover, the differences between users who have different driving experience were observed, while no significant effect of e ducation and income were found. Conclusion: It is confirmed that behavioral intention is the main determinant of the actual use of AVs, while it is mostly affected by attitude, subjective norms, and perceived enjoyment. Technological factors, in particular, compatibility, relative advantage, reliability, and complexity have significant effects on factors in behavior theories. Users with less experience with driving are highly dependent on other’s opinions and suggestions to use AVs. It can be helpful for developers to focus on safety risks than privacy, since young adults are not likely to concern about sharing their data with AVs. Application: Key findings of this study will be used as a basis for developing a new extended technology acceptance model for self-driving cars.
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A Study on Identifying Human Factors in Collaboration with the AI-infused Systems
Ismatullaev Ulugbek Vahobjon U,In Gwon Jo(조인권),Sang Ho Kim(김상호) 대한인간공학회 2020 대한인간공학회 학술대회논문집 Vol.2020 No.6
Objective: This paper aims to analyze the human factors in artificial intelligence and discuss the Human-AI collaboration based on previous researches in three major fields such as healthcare, teaching and automated driving. Background: Despite the fact that artificial intelligence is one of the largest and most important inventions of the present, the researches have shown that there are several challenges of using artificial intelligence in the cooperation with human teammates. AI-Infused systems may perform certain operations or tasks faster and more precisely than a person, play chess, drive a car and perform many functions. But, depending on the different type of human factors, users may interact differently with artificial intelligence, and those factors make some challenges in the relationship between human and artificial intelligence. Method: This paper was conducted in three steps: (1) Reviewing previous researches on human factors in three areas; (2) Categorizing human factors in to the table to analyze their importance in adoption to AI infused systems; (3) Identifying the most critical human factors of individuals in three fields in collaboration with artificial intelligence devices to deal with the challenges of AI acceptance to improve teamwork and work efficiency as well as reducing errors mostly caused by human factors for each field. Results: Gender, age and trust in technology are found the most critical factors for each fields, while technology expertise and social influence factors plays important role in adoption AI-Infused systems in Education and Healthcare. Conclusion: It is found that, autonomous driving is most discussed field in terms of use human factors in the interaction with artificial intelligence, while there is still lack of researches about the adoption to AI technologies in education and healthcare based on some differences of human factors such as health, cognitive, work and , physical capabilities. Application: Findings of this study can help for the further studies focusing on identifying the cause of human errors that can be occurred in takeover or handover scenarios in terms of use AI-Infused Systems in healthcare, autonomous driving, and education.
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음성기반 Human-AI Interaction에서 인적요인이 주관적 감성에 미치는 영향
신종규(Jonggyu Shin),허인석(Inseok Heo),Ismatullaev Ulugbek Vahobjon U,김상호(Sangho Kim) 대한인간공학회 2020 대한인간공학회 학술대회논문집 Vol.2020 No.10
Objective: To identify human factors and design parameters that affect user emotion arising from interaction with voice-based intelligent systems. Background: Technology that secures human emotional satisfaction is very important. Recently, AI-infused systems can be developed in the direction of meeting human needs through machine(and deep) learning. Therefore, it is necessary to design guidelines for an intelligent system to evolve in a direction that can secure human emotional satisfaction. Method: Determine human factors and design variables based on prior research. Human factors and design variables are evaluated through experiments using emotional engineering techniques. Emotional factors are derived through factor analysis and emotional differences are analyzed using ANOVA. Results: Among the three human factors (gender, interest in new technology, personality) and the four design speed variables (speech, pitch, structure, and), personality and speed did not affect emotional change. Conclusion: A customized intelligent system can be designed based on human factors and design variables that affect the interaction with the AI-Infused system. Application: This framework will be used as a basic research for integrated emotional evaluation using bio-signals.
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