• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.21-29
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• 2020

The purpose of this study is to develop the educational programs for cultivating balanced view of technical understanding and social impact on Artificial Intelligence (AI). To this end, an educational program based on a constructivist approach was developed. Through an experimental class for middle school students we analyzed the concept and perception of AI and the satisfaction of the class. The main results are as follows. First, students' understanding of the concept and the cases of AI in their daily lives has improved. Second, the recognition of the impact of AI on society has emerged and concern about social impact have been lowered. Third, in terms of program satisfaction, all the factors such as understanding of AI, interest in class, interest in AI were high. With these results, we discussed the implications for AI education in elementary and secondary school.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.43-61
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• 2022

Purpose: The purpose of this study was verifying factors that affect to intention to use AI financial services and finding a way of building an user oriented AI ecology. Methods: This study used the UTAUT (Unified Theory of Acceptance and Use of Technology) model with independent variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, trust, personal innovativeness and AI understanding as moderating variable. The data was collected through online & offline survey with questionnaire from 330 financial customers. Results: As a result, the analysis suggested that the performance expectancy, social influence, facilitating conditions, personal innovativeness are statistically significant to the intention to use AI. It was also found that AI knowledge of users differently influence the intention to use through the moderating effect on the facilitating conditions. Conclusion: Performance expectancy, social influence, facilitating conditions, personal innovativeness have positive causation to the intention to use in AI financial service. On the facilitating conditions, unlike other variables, it was found that the user's intention to use was different by the level of AI understanding. It means that customers could have the strong intention to use AI even though they don't have enough pieces of knowledge on the factors. Customers seem to be of recognition that the technology has certain benefits for themselves. The facilitating factors are significantly affected by AI understanding and differently effect on the intention to use AI.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.285-293
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• 2021

With the advent of the 4th Industrial Revolution, AI utilization capabilities are being emphasized in various industries, but AI education design and curriculum research as universal education is currently lacking. This study offers a design for universal AI education to further cultivate its use in universities. For the AI basic education design, a questionnaire was conducted for experts three times, and the reliability of the derived design contents was verified by reflecting the results. As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived were data structure understanding and processing, visualization, word cloud, public data utilization, and machine learning concept understanding and utilization. The educational design content derived through this study is expected to increase the value of competency-centered AI universal education in the future.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.31-36
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• 2021

In the era of the 4th industrial revolution represented by AI technology, various AI education is being conducted in the education field. However, AI education in the educational field is mostly one-off project education or teacher-centered education. In order to practice student-centered, field-oriented education, an artificial intelligence education program was developed based on design thinking. The AI education program based on design thinking will improve understanding and ability to use AI through the process of solving everyday problems with AI, and will develop the ability to create new values beyond understanding AI. It is expected that various AI education will take place in the educational field through design thinking-based artificial intelligence education programs.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.99-110
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• 2023

In this study, in order to strengthen the AI utilization competency of pre-service secondary science teachers, a project activity in which pre-service teachers directly create an 'AI-based molecular structure customized learning support tool' by using Google's teachable machine was developed and applied. To this end, the program developed for 26 third-grade pre-service teachers enrolled in the Department of Chemistry Education at H University in Chungcheongbuk-do was applied for 14 sessions during extracurricular activities. Then, the perceptions of 'understanding how AI works', 'efficacy of using AI in science classes', and 'plans to utilize AI in science classes' were investigated. As a result of the study, it was found that the program developed in this study was effective in helping pre-service teachers understand the operating principle of AI technology for machine learning at a basic level and learning how to use it. In addition, the program developed in this study was found to be effective in increasing the efficacy of pre-service teachers for the use of AI in science classes. And it was also found that pre-service teachers recognized the aspect of using AI technology as a new teaching·learning strategy and tool that can help students understand science concepts. Accordingly, it was found that the program developed in this study had a positive impact on pre-service teachers' AI utilization competency reinforcement and perception improvement at the basic level. Implications of this were discussed.

• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.21-32
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• 2021

Purpose: With the advent of the era of the 4th industrial revolution, called a new technological revolution, the necessity of fostering future talents equipped with AI utilization capabilities is emerging. However, there is a lack of research on AI education design and competency-based education curriculum as education for business major. The purpose of this study is to design AI education to cultivate competency-oriented AI literacy for business major in universities. Research design, data and methodology: For the design of AI basic education in business major, three expert Delphi surveys were conducted, and a demand analysis and specialization strategy were established, and the reliability of the derived design contents was verified by reflecting the results. Results: As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived from this were data structure understanding and processing, visualization, web scraping, web crawling, public data utilization, and concept of machine learning and application. Conclusions: The educational design content derived through this study is expected to help establish the direction of competency-centered AI education in the future and increase the necessity and value of AI education by utilizing it based on the major field.

• Korean Journal of Childcare and Education
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• v.19 no.2
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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.

• The Journal of Information Systems
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• v.33 no.1
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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.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.113-120
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• 2019

Recently, the artificial intelligence(AI) is used in various environments in life, and research on this is being actively conducted in education. In this paper, we designed a Design-Based Research(DBR)-based AI programming education program and analyzed the application of the program for the improvement of understanding of AI in elementary school. In the artificial intelligence education program in elementary school, we should considerthat itshould be used in conjunction with software education through programming activities, rather than creating interest through simple AI experiences. The designed education program reflects the collaborative problem-solving procedures following the DBR process of analysis - design - execution - redesign, allowing the real-world problem-solving activities using AI experiences and block-type programming language. This paper also examined the examples of education programs to improve understanding of AI by using Machine Learning for Kids and to draw implications for developing and operating such a program.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.295-301
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• 2021

This study tried to examine the necessity of AI literacy education to increase the effectiveness of artificial intelligence education ahead of the revision of the next revised curriculum. To this end, AI modeling classes were conducted for high school students and the necessity, content, and training period of AI literacy perceived by students in AI education were investigated through a questionnaire. The results showed that they generally agreed on the need for data utilization and data preprocessing in the AI class, and in the course of the AI class, there were many cases of difficulties due to lack of basic competencies for database use. In particular, it was observed that the understanding of the file structure for data analysis was insufficient and the understanding of the data storage format for data analysis was low. In order to overcome this part, the necessity of prior education for data processing was recognized, and there were many opinions that it is generally appropriate to go to high school at that time. As for the content elements of AI literacy, it was found that there were high demands on the content of data visualization along with data transformation, including data creation and deletion.