• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.49-59
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• 2020

At the beginning of the discussion of AI education in K-12 education, the study was conducted to develop and apply an AI education program based on Design Thinking and analyze the effects of the AI education programs. In the AI education program, students explored and defined the AI problems they were interested in, gathered the necessary data to build an AI model, and then developed a project using scratch. In order to analyze the effectiveness of the AI education program, the change of learner's perception of the value of AI and the change of AI efficacy were analyzed. The overall perception of the AI project was also analyzed. As a result, AI efficacy was significantly increased through the experience of carrying out the project according to the Design Thinking process. In addition, the efficacy of solving problems with AI was influenced by the level of use of programming languages. The learner's overall perception of the AI project was positive, and the perceptions of each stage of the AI project (AI problem understanding and problem exploration, practice, problem definition, problem solving idea implementation, evaluation and presentation) was also positive. This positive perception was higher among students with high level of programming language use. Based on these results, the implications for AI education were suggested.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.199-206
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• 2020

In this study, the factors affecting consumers' intention to use AI speakers were focused on the perceived value of the product and the perceived necessity of the product. Factors affectationist consumers' perceived value of the product were divided into benefits and costs. Reflecting the characteristics of information technology products, I included perceptions of usefulness of products. Empirical results show that consumers' perceptions of perceived benefits and usefulness of AI speaker products have a positive effect on perceived value and perceived necessity. Perception of necessity had a positive (+) significant effect on perception of value. Perception of necessity and perception of value had a positive(+) and positive effect on each intention of use. However, the cost perceived by consumers did not have a significant effect on perception of value.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.67-72
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• 2024

The algorithm is applied on the premise that the image generated by AI can be recognized and used smoothly by the user. Other assets are not exposed to the user or discarded because they are unnecessary or unfamiliar. This study aims to expand the scope of the utility of the image generated by AI, which is used as a high-level tool in the design field. To this end, we first examined human information processing and reflection in AI by the cognitive paradigm by examining previous studies and cases, and discussed the value of expansion by focusing on creativity and bottom-up processing of AI's self-organization. Considering the human recogmition process that instinctively grasps an object, the following AI usability was proposed. It is to utilize AI as a high-level tool applied appropriately to human perception, or to utilize the derivative itself by bottom-up self-organization. In addition, it is to set the algorithm to the minimum intervention so that basic elements such as shape, color, size, texture, and movement are composed of figure-ground according to the human perception process that instinctively grasps an object, and to utilize the results. Limiting the use of AI to a tool suitable for human perception and information processing or production by designers or general users is to operate only a part of the convenience and usability of AI. The image creation through AI's self-organization, as seen from the cognitive paradigm, is a step toward opening a new era of design where technical aesthetics meets devices, just as design has been constantly developing in pursuit of novelty and differentiation due to its nature.

• Journal of the Korean Chemical Society
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• v.68 no.3
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• pp.160-175
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• 2024

The purpose of this study is to examine the effects of a Chemistry I class based on an artificial intelligence (AI) classification model. To achieve this, the research investigated the development and application of a class utilizing an AI classification model in Chemistry I classes conducted at D High School in Gyeongbuk during the first semester of 2023. After selecting the curriculum content and AI tools, and determining the curriculum-AI integration education model as well as AI hardware and software, we developed detailed activities for the program and applied them in actual classes. Following the implementation of the classes, it was confirmed that students' self-efficacy improved in three aspects: chemistry concept formation, AI value perception, and AI-based maker competency. Specifically, the chemistry classes based on text and image classification models had a positive impact on students' self-efficacy for chemistry concept formation, enhanced students' perception of AI value and interest, and contributed to improving students' AI and physical computing abilities. These results demonstrate the positive impact of the Chemistry I class based on an AI classification model on students, providing evidence of its utility in educational settings.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.193-204
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• 2021

Based on risk-benefit theory, this study examined a structural equation model accounting for the mechanisms through which affective perceptions of AI predicting individuals' support for the government's Ai policies. Four perceived characteristics of AI (i.e., usefulness, entertainment value, privacy concern, threat of human replacement) were investigated in relation to perceived benefits/risks, attitudes toward AI, and AI policy support, based on a nationwide sample of South Korea (N=352). The hypothesized model was well supported by the data: Perceived usefulness was a strong predictor of perceived benefit, which in turn predicted attitude and support. Perceived benefit and attitude played significant roles as mediators. Perceived entertainment value along with perceived usefulness and privacy concern predicted attitude, not perceived benefit. Neither attitude nor support was significantly associated with perceived risk which was predicted by privacy concern. Theoretical and practical implications of the results are discussed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.275-281
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• 2021

The purpose of this research is to explore what factors affect consumers' purchasing decisions when purchasing artificial intelligence artworks. The research pointed out that in the real shopping model, customer perceived value includes three dimensions: product perceived value, service perceived value and social perceived value. On this basis, an artificial intelligence work purchase decision-making influence model was constructed, and an online survey was attempted to collect data. Through analysis of the reliability, effectiveness and structural equations of SPSS24.0 and AMOS24.0, and scientific verification and analysis, we found that product cognitive value and service cognitive value have a positive impact on consumers' purchase intentions, but social cognition Value has no positive effect on consumers' purchasing intentions.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.329-342
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• 2021

The pharmaceutical industry is experiencing a productivity crisis with a low probability of success despite a long period of time and enormous cost. As a strategy to solve the productivity crisis, the use cases of Artificial Intelligence(AI) and Bigdata are increasing worldwide and tangible results are coming out. However, domestic pharmaceutical companies are taking a wait-and-see attitude to adopt AI platform for drug research. This study proposed a research model that combines the Value-based Adoption Model and the Innovation Resistance Model to empirically study the effect of value perception and resistance factors on adopting AI Platform. As a result of empirical verification, usefulness, knowledge richness, complexity, and algorithmic opacity were found to have a significant effect on perceived values. And, usefulness, knowledge richness, algorithmic opacity, trialability, technology support infrastructure were found to have a significant effect on the innovation resistance.

• 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.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.121-128
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• 2024

With the recent rise in the use of AI-based online translation tools, interest in their methods and effects on education has grown. This study involved 30 prospective elementary school teachers who completed an English writing task using an AI-based online translation tool. The study focused on assessing the impact of these tools on English writing skills and their practical applications. It examined the usability, educational value, and the advantages and disadvantages of the AI translation tool. Through data collected via writing tests, surveys, and interviews, the study revealed that the use of translation tools positively affects English writing skills. From the learners' perspective, these tools were perceived to provide support and convenience for learning. However, there was also recognition of the need for educational strategies to effectively use these tools, alongside concerns about methods to enhance the completeness or accuracy of translations and the potential for over-reliance on the tools. The study concluded that for effective utilization of translation tools, the implementation of educational strategies and the role of the teacher are crucial.