• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.05b
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• pp.20-21
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• 2002

• Journal of the Korean housing association
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• v.19 no.1
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• pp.9-16
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• 2008

This study aims to construct the design competence by means of a topological approach. To this end, the linguistic concept of 'competence and performance' in Chomskian sense is borrowed and applied to the study. The usability of this method is then tested against the sample apartment plans from Gangnam-gu area in Seoul, and it is found that this enabled a middle-ground approach to a more productive grammar that overcomes the limits in Glassie's and Stiny's grammar systems. Through a series of analyses on the sample plans, it could be clarified that there appear classificatory levels in the competence that controls the planning of the building, zoning of the unit, and layout of LDK combination. At the end, it is evaluated that the generative grammar, constructed in this research, is the possible world in designers' minds, and this retrospective remodelling of the architectural competence could illuminate the 'design decision flow' that generates the sample plans.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.3
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• pp.234-246
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• 2023

Purpose: This study aimed to develop a case-based nursing education program using generative artificial intelligence and to assess its usability and applicability in nursing curriculums. Methods: The program was developed by following the five steps of the ADDIE model: analysis, design, development, implementation, and evaluation. A panel of five nursing professors served as experts to implement and evaluate the program. Results: Utilizing ChatGPT, six program modules were designed and developed based on experiential learning theory. The experts' evaluations confirmed that the program was suitable for case-based learning, highly usable, and applicable to nursing education. Conclusion: Generative artificial intelligence was identified as a valuable tool for enhancing the effectiveness of case-based learning. This study provides insights and future directions for integrating generative artificial intelligence into nursing education. Further research should be attempted to implement and evaluate this program with nursing students.

• Educational Technology International
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• v.17 no.2
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• pp.253-271
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• 2016

This study investigated the effect of generative concept maps according to learning achievements and cognitive load. A total of 78 students in the first grade of middle school participated in this study. Before the experimental treatment was implemented, students had to fill out a questionnaire assessing prior knowledge. The study was designed where all the students were presented the same learning contents regarding photosynthesis; however, the two experimental groups were provided with different concept map methods: a learner-generative concept map (GCM) and an instructor-provided concept map (PCM). GCM students were asked to make a concept map by themselves in small groups while they are reading material. PCM students were instructed to study in small groups in order to read the material; however, they were provided a concept map developed by their teacher. The control group (CG) had the teacher present the learning contents in traditional lecture format with no accompanying concept map. The results show that there were significant differences in the achievements among the groups. CG showed higher achievement than both the experimental groups. There was also a significant difference in cognitive load. Although the GCM group did not obtain higher achievement than the other groups, the GCM group showed higher mental effort and lower physical fatigue than the other groups. The GCM group might have invested more effort to find and connect ideas when drawing their concept map with peers which is unlike the conditions for the PCM group and CG. In conclusion, we should consider applying GCM in teaching and learning design in order to increase learning achievement and decrease extraneous cognitive load.

• Educational Technology International
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• v.25 no.1
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• pp.1-25
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• 2024

This study aims to analyze the impact of learners' usability perceptions of topic modeling-based visual feedback and generative AI interpretation on reflection levels in online discussions. To achieve this, we asked 17 students in the Department of Korean language education to conduct an online discussion. Text data generated from online discussions were analyzed using LDA topic modeling to extract five clusters of related words, or topics. These topics were then visualized in a network format, and interpretive feedback was constructed through generative AI. The feedback was presented on a website and rated highly for usability, with learners valuing its information usefulness. Furthermore, an analysis using the non-parametric Mann-Whitney U test based on levels of usability perception revealed that the group with higher perceived usability demonstrated higher levels of reflection. This suggests that well-designed and user-friendly visual feedback can significantly promote deeper reflection and engagement in online discussions. The integration of topic modeling and generative AI can enhance visual feedback in online discussions, reinforcing the efficacy of such feedback in learning. The research highlights the educational significance of these design strategies and clears a path for innovation.

• 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 Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.81-86
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• 2024

Advancements in generative AI are reshaping graphic and 3D content design landscapes, where AI not only enriches graphic design but extends its reach to 3D content creation. Though 3D texture mapping through AI is advancing, AI-generated 3D modeling technology in this realm remains nascent. This paper presents AI 2D image-driven 3D modeling techniques, assessing their viability in 3D content design by scrutinizing various algorithms. Initially, four OBJ model-exporting AI algorithms are screened, and two are further evaluated. Results indicate that while AI-generated 3D models may not be directly usable, they effectively capture reference object structures, offering substantial time savings and enhanced design efficiency through manual refinements. This endeavor pioneers new avenues for 3D content creators, anticipating a dynamic fusion of AI and 3D design.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.74-83
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• 2012

This study is an historical consideration about the modern discourse of Japanese spacial tradition driven from cultural background. The purpose of this study is to establish a cartographic map of historical progress, and to shed light on the forming of identity in Japanese traditional space design on the schizo-analytical aspect. It adopts F. Guattari's psychoanalytic theory to the structural analysis of Japanese traditional space design. The process of this study is illustrated as follows: At first, it mentions Guattari's theory of Mechanical Unconscious, Schizo-analysis, Cartography, and Abstract machine as theoretical background. And, it considers the identity of Japanese traditional space constructed by various cultural sign over a long period of time as the statement of apriority. Secondly, it clarifies semiologic generation of Japanese traditional space design based on the analysis of spacial morphemes about each design stemmed from modernization process of Japan. Thirdly, it ascertains semiologic topography the representamens draw, i.e. schizo-analytic cartography from synchronic and diachronic point of view. Fourthly, it analyses traditional discourse structure in terms of generative schizo-analysis and transformational schizo-analysis with four categories- object, style, concept, strategy. Through this process, it studies the reproduction of Japanese tradition in terms of the 'social organization', and explores the way vitalized on the space-time coordinate system by the schizo-analysis of the mechanical unconscious. In conclusion, it clarifies Generative-schizo is accomplished in the level of formulating representamen, and Transformational-Schizo involves experimental mind that induce implantation of the heteromorphic elements and avant-garde experiments of abstract mechanical operation in the schizo-analysis of Japanese traditional space design. The significance of this study is to arrange an opportunity of introspection on Korean-ness seriously from inspecting logic of Japan-ness closely in traditional space design.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.81-88
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• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.