• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.201-208
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• 2023

As the demand for AI education increases, AI education is actively conducted in the educational field, but it is difficult to internalize AI education due to securing time, difficulty in organizing class contents, and lack of curriculum. As a way to solve this problem, there is a school autonomous course. The school autonomous course allows schools to have autonomy and discretion throughout the curriculum, such as adjusting the number of hours in the subject group and restructuring the use of achievement standards. In this study, in order to enhance AI education, the effect was analyzed by developing and applying an AI education immersion course using a school autonomous curriculum. In the AI education immersion course, students continuously experience AI education in a dense manner within a limited time, so substantial AI education can be achieved. After the AI curriculum, it was found that students' overall AI literacy and self-determination learning motivation improved. It is expected that this study will be able to present a direction to internalize AI education using school autonomous curriculum.

• Convergence Security Journal
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• v.24 no.2
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• pp.89-104
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• 2024

This study is an empirical study to examine the factors that influence the intention to use artificial intelligence (AI) technology for SW engineering-related tasks, and the purpose of the study is to understand the key factors that influence the use in terms of AI augmentation characteristics and interactive UI/UX characteristics. For this purpose, a survey was conducted among information and communication workers who have experience in using AI-related technologies and the collected data was analyzed. The results of the empirical analysis showed that perceived usefulness was positively influenced by the factors of expertise, interestingness, realism, aesthetics, efficiency, and flexibility, and perceived ease of use was positively influenced by the factors of expertise, interestingness, realism, aesthetics, and flexibility. Variety had no effect on both perceived ease of use and perceived usefulness. Perceived ease of use had a significant effect on perceived immersion, which positively influenced intention to use. These findings are significant in that they provide an academic understanding of the factors that influence the use of AI-enhanced tools in SW engineering-related tasks such as application design, development, testing, and process automation, as well as practical directions for the creators of tools that provide AI-enhanced development services to develop user acquisition strategies.

• Environmental Mutagens and Carcinogens
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• v.22 no.4
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• pp.243-247
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• 2002

Prolonged exercise is known to increase steady-state serum high-density lipoprotein cholesterol (HDL-cholesterol) and apolipoprotein AI(apo AI) concentrations. We investigated the effect of adaptation to endurance exercise on the association of the genetic polymorphism in the apo AI gene with these biochemical parameters. 108 male subjects were randomly selected from a group of elite athletes, and 65 male samples used as sedentary control group from Korean general population. The genetic polymorphism in the apo AI gene locus was detected by polymerase chain reaction(PCR) and DNA digestion with Msp I restriction endonuclease. The genotype frequency for the Msp I RFLP was significantly different between the elite athletes and sedentary controls(P<0.05). There were, however, no significant associations between the Msp I RFLP of the apo AI gene and the biochemical parameters in elite athletic group. Therefore, our findings indicate that the Msp I RFLP of the apo AI gene was not associated with the serum apo AI and HDL-cholesterol concentrations in Korean male elite athletes.

• Journal of Engineering Education Research
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• v.26 no.3
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• pp.12-19
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• 2023

The purpose of this study is to develop the Artificial Intelligence thinking-based education program for improving AI literacy and verify its effectiveness for beginner. This program consists of 17 sessions, was designed according to the "ABCDE" model and is a project-based program. This program was conducted on 51 first-year middle school students and 36 respondents excluding missing values were analyzed in R language. The effect of this program on ethics, understanding, social competency, execution plan, data literacy, and problem solving of AI literacy is statistically significant and has very large practical significance. According to the result of this study, this program provided learners experiencing Artificial Intelligence education for the first time with Artificial Intelligence concepts and principles, collection and analysis of information, and problem-solving processes through application in real life, and served as an opportunity to enhance AI literacy. In addition, education program to enhance AI literacy should be designed based on AI thinking.

• Korean Journal of Materials Research
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• v.4 no.4
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• pp.483-490
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• 1994

The effect of vanadium additions on the thermal stability of Al-TI alloy \vas investigated. Al- 8wt.%Ti and Al-8wt.%(Ti+V) alloys wirh different Ti to V atomic ratios of 3 : 1 and 1 : 1 were pre- pared by mechanical alloying. The steady states wwe obtalncd after mechanical alloy~ng for ltihours for all the alloy compositions. The mechanically alloyed powders were consolidaicd by vacuum hot pressing and thermal st.ability was investigated by hardness testing afrcr aging thc specimens at $400^{\circ}C$, $480^{\circ}C$, $550^{\circ}C$ for up to 1000hrs. It was confirmed that addit~on of V- increased the thermal stability of Al-Ti alloy by reducing coarsening rate of $Ai_{3}Ti$ intermetallic compound.

• Reproductive and Developmental Biology
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• v.37 no.2
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• pp.75-77
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• 2013

This study was conducted to investigate optimal time of artificial insemination (AI) to Hanwoo female after natural estrus. AI was occurred 12 and 24 hours after natural estrus in both heifer and multiparous recipient then pregnancy and parturition rates were estimated. Results indicated that AI performed at 24 hours after natural estrus showed significant (p<0.05) higher pregnancy rate in both heifer and multiparous recipient groups with significantly (p<0.05) higher abortion rate. However, there are no significant differences of parturition rate, twin birth and sex ratio in both heifer and multiparous recipient groups. Therefore, our results may suggest that performance of AI at 24 hours after natural estrus promise higher pregnancy rate than AI at 12 hours after natural estrus in both heifer and multiparous recipient.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.75-80
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• 2021

Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.

• The Mathematical Education
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• v.63 no.3
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• pp.573-591
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• 2024

The purpose of this study is to explore research trends on AI-based mathematics teaching and learning. For this purpose, a systematic literature review was conducted on 57 literatures in terms of research subject, research method, research purpose, learning content, type of AI, role of AI, and role of teachers. The results indicate that student accounted for the largest proportion at 51% among the research subjects, and quantitative research was the highest at 49% among the research methods. The purpose of study was distributed as follows: effect analysis 44%, theoretical discussion 35%, case study 21%. 'Numbers and Operations' and 'Variables and Expressions' covered learning contents most, and Intelligent Tutoring System (ITS) was used the most among the types of AI. 'Student teaching' was the largest parts of role of AI at 40.4%, followed by 'teacher support' at 22.8%, 'student support' at 21%, and 'system support' at 15.8%. The role of teachers as 'AI recipients' was highlighted in earlier studies, and the role of teachers as 'constructive partner with AI' was highlighted in more recent studies. Also, role of teachers was explored in pedagogical, AI-technological, content aspects. Through this, follow-up research was suggested and the roles that teachers should have in AI-based mathematics teaching and learning were discussed.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.