• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.77-85
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• 2022

Unlike previous studies that focused on the diversity of YouTube content, this study tried to identify factors affecting users' satisfaction with the YouTube recommendation system. Specifically, by adding content preference suitability and privacy concerns to the technology acceptance model, we empirically analyzed how these variables affect user's satisfaction of the YouTube AI recommendation system. For this purpose, asurvey was conducted on college students in their 20s and 30s, and the main research results are as follows. First, in the respondents of this study, playfulness and usefulness, which are major variables of the technology acceptance model, appeared as significant factors affecting the satisfaction of the YouTube AI recommendation system, whereas the effect of ease to use was not found. Second, content preference suitability was found to affect the satisfaction with AI recommendation system, but privacy concerns did not affect the satisfaction with YouTube AI recommendation system. Based on these research results, the implications of the study and the directions for future studies were suggested.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.167-176
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• 2024

Chatbot services are used in various fields in connection with AI services. Security research on AI is also in its infancy, but research on practical security in the service implementation stage using it is more insufficient. This paper analyzes the security requirements for chatbot services linked to AI services. First, the paper analyzes the recently published papers and articles on AI security. A general implementation model is established by investigating chatbot services provided in the market. The implementation model includes five components including a chatbot management system and an AI engine Based on the established model, the protection assets and threats specialized in Chatbot services are summarized. Threats are organized around threats specialized in chatbot services through a survey of chatbot service managers in operation. Ten major threats were drawn. It derived the necessary security areas to cope with the organized threats and analyzed the necessary security requirements for each area. This will be used as a security evaluation criterion in the process of reviewing and improving the security level of chatbot service.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.821-827
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• 2024

Generative AI is creating new business methodologies across various industries. By generating code values through ChatGPT prompting and using Rhino Python Script, 3D modeling can be performed in Rhino. This research began with an interest in a new process where a workflow initiated in natural language culminates in 3D modeling. The aim of the research is to establish an efficiency-based modeling method through automation in Rhino 3D, and for this purpose, studies on Rhino Python research and the modification and extension of modules were conducted. The research results confirmed that utilizing generative AI can enhance production productivity and improve user accessibility to 3D modeling. Standardization through Rhino Python Script increased work efficiency in terms of modification and extension. Furthermore, the necessary conditions for optimal 3D design were identified as clear prompting and the incorporation of feedback from AI. Through this research, it is hoped that generative AI will assist in creativity based on efficiency in jewelry 3D modeling.

• Journal of Christian Education in Korea
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• v.61
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• pp.233-262
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• 2020

Rapidly developing modern science &technology have a profound impact on Christians and pastoral work. Recently, the 4th Industrial Revolution has induced lots of discussions in the field of church and theology, and artificial intelligence (AI) has become an important issue in many ways. Nevertheless, there is a lack of empirical research on how the AI would affect church and pastoral work. This study examined and analyzed the theological students' perception of AI. A survey was conducted on the perception of seven sub-areas of 220 male and female theological students at major seminaries in Korea. The seven subareas were including the degree of interest in AI, social influence, AI's alternative influence, and AI's church influence. The results showed that theological students generally agree with the academic relevance of AI or the need for education on AI. However, it presented alow perception of the impact of AI on the church. Such recognition may reflect the following belief. Students are aware that the AI is a necessary and important part of social and general education, but at the same time, they think the AI may not significantly threaten the church. Therefore, wes uggest that considering a response of Christian education to raise the perception of theological students of AI, courses related to science and technology should be organized in the curriculum of seminaries at various levels from the perspective of the Christian worldview.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.193-198
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• 2020

Purpose: This study investigated knowledge, attitudes, and perceptions regarding the future of artificial intelligence (AI) for radiological diagnosis among dental specialists in central India. Materials and Methods: An online survey was conducted consisting of 15 closed-ended questions using Google Forms and circulated among dental professionals in central India. The survey consisted of questions regarding participants' recognition of and attitudes toward AI, their opinions on directions of AI development, and their perceptions regarding the future of AI in oral radiology. Results: Of the 250 participating dentists, 68% were already familiar with the concept of AI, 69% agreed that they expect to use AI for making dental diagnoses, 51% agreed that the major function of AI would be the interpretation of complicated radiographic scans, and 63% agreed that AI would have a future in India. Conclusion: This study concluded that dental specialists were well aware of the concept of AI, that AI programs could be used as an adjunctive tool by dentists to increasing their diagnostic precision when interpreting radiographs, and that AI has a promising role in radiological diagnosis.

• Journal of dental hygiene science
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• v.24 no.3
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• pp.171-180
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• 2024

Background: The Fourth Industrial Revolution highlights the importance of artificial intelligence (AI) and digital literacy in dental hygiene education. However, research on students' attitudes toward AI and their digital literacy levels is limited. Therefore, this study investigated the attitudes of dental hygiene students toward AI and digital literacy levels. Methods: In total, 167 dental hygiene students in Baekseok University participated in the study and provided informed consent. The survey tool included general characteristics, smartphone usage patterns, attitudes toward AI, and digital literacy levels. Attitudes toward AI and digital literacy based on general characteristics and smart device usage were analyzed using t-tests and one-way ANOVA. Correlations among attitudes toward AI, digital literacy awareness, and digital literacy behaviors were analyzed using Pearson's correlation analysis. The impact of AI attitudes and digital literacy awareness on digital literacy behavior was examined using linear regression analysis. Results: Students with higher interest in their major had more positive attitudes toward AI, and those with higher smart device usage showed increased AI attitudes and digital literacy (p<0.05). Simple frequency or duration of smartphone use did not affect digital literacy, but students who perceived their smart device usage positively and believed that they used smart devices effectively in their studies exhibited higher levels of digital literacy (p<0.05). A positive attitude toward AI is associated with higher levels of digital literacy (p<0.05). Digital literacy awareness and attitudes toward AI influenced digital literacy behavior (p<0.05). Conclusion: These results suggest that the qualified utilization and application of digital devices in dental hygiene education are important. Improving the educational curriculum is necessary; as a result, digital technology can be effectively utilized, and various educational programs should be introduced to enhance digital literacy.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.827-832
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• 2024

The AI program Sora is a video production model that can be used innovatively and is the starting point of a major paradigm shift in video planning and production in the future. In this paper, through consideration of the characteristics, application, and process of the AI video production program, the characteristics of the AI design video production method were understood, and the production algorithm was considered. The detailed consideration and characteristics of the work creation process for the video graphic AI video generation program that will be intensified every year were examined. Next, the method of generating a customized video with a text prompt and the process of innovative production results different from the previous production method were considered. In addition, the design direction through the generation of AI images was studied through the review of the strengths and weaknesses of the image details of the recently announced AI music video results. By considering the security of the AI generation video Sora and looking at the internal process of the actual AI process, it will be possible to present indicators for the future direction of AI video model production and education along with the direction of the design designer and education system. In the text and conclusion, we analyzed the strengths and weaknesses and future status of OpenAI Sora image, concluded how to apply the Sora model's capabilities, limitations, quality, and human creativity, and presented problems and alternatives through examples of the Sora model's capabilities and limitations to increase human creativity.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Review of Korea Contents Association
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• v.17 no.1
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• pp.14-17
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• 2019

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.567-575
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• 2024

This study was conducted to provide basic data on how to use Generative AI and education to strengthen Generative AI competency in vocational counseling by confirming the experience and perception of job counselors' use of Generative AI. A questionnaire was produced based on literature research and FGI preliminary surveys, and the main contents of the questionnaire were 'experience in using Generative AI (whether to have experience, type of tool, job, educational experience, etc.) and Generative AI recognition (recognition level, usefulness, availability, educational needs, etc.). An online survey was conducted for vocational counselors, and a total of 293 data were analyzed. As a result of major research, first, there were many counselors who had no experience in using Generative AI(60%), and the response that the reason for not using it was because they did not feel the need(28%). Second, the 'degree of recognition' in the Generative AI was somewhat low (M=2.77), and 'Generative AI usefulness' was found to be at a normal level (M=3.32), and it was recognized that it would be necessary mainly for jobs related to 'vocational information'. Third, 'tool (computer use, etc.) competency' (26%) was the highest as the competency required for future vocational counselors, and 'how to use Generative AI' (57%) accounted for a high proportion of the educational content necessary to improve these competencies.