• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.253-261
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• 2022

In this study, the educational effect were sought to be identified after developing and applying project learning for the artificial intelligence based liberal arts education for the non-computer majors. A paired-sample t-test was performed within each group to determine the extent of improvement in the learning flow and artificial intelligence efficacy in the experimental and control groups. After class, an independent sample t-test was performed to examine the statistical effects of pre-test and post-test on the learning flow and artificial intelligence efficacy in the experimental and control groups. The experimental group and control group demonstrated significant improvements in the learning flow and artificial intelligence efficacy before and after class, each respectively. There was no statistically significant difference in the learning flow between the experimental group for which the project learning method was applied and the control group for which only theory and practice were conducted in the artificial intelligence class. It was also confirmed that the experimental group for which the project learning method was applied improved the efficacy of artificial intelligence to a significant level compared to the control group which only proceeded with theory and practice.

• Korean Journal of Child Studies
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• v.26 no.5
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• pp.105-121
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• 2005

Data were collected from 120 teachers at 67 childcare centers in Seoul and Kyunggi-Do. The Assessment Scale for Day Care Programs(Rhee et ai., 2003) was used to observe teachers' interaction behaviors. Subjects responded to the teachers' job stress scale developed by the author and a modified version of teachers' efficacy scale based on the Science Teaching Efficacy Belief Instrument(Enochs & Riggs, 1990). Data were analyzed with descriptive statistics, Pearson correlations, and hierarchical regressions. Results showed that quality of teachers' interaction behaviors correlated negatively with teachers' job stress, and positively with teachers' personal efficacy; teachers' belief of efficacy moderated the relationship between job stress and teachers' interaction behaviors.

• Journal of Breast Disease
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• v.6 no.2
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• pp.46-51
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• 2018

Purpose: Endocrine therapy is the preferred treatment for hormone receptor (HR)-positive metastatic breast cancer (MBC). We investigated the efficacy of combined aromatase inhibitor (AI) and luteinizing hormone-releasing hormone (LHRH) agonist in premenopausal patients with HR-positive MBC. Methods: We retrospectively analyzed the medical records of 21 HR-positive premenopausal MBC patients treated with combined AI and LHRH agonist therapy. Results: The median follow-up period was 32.9 months. The overall response rate was 47.6%, with three complete responses (14.3%) and seven partial responses (33.3%). Nine patients (42.9%) achieved stable disease lasting more than 6 months; thus, the clinical benefit rate was 90.4%. The median time to progression was 45.4 months. No patients experienced grade 3 or 4 toxicity. Conclusion: Combined AI and LHRH agonist treatment safely and effectively induced remission or prolonged disease stabilization, suggesting that this could be a promising treatment option for HR-positive premenopausal patients with MBC.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.67-73
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• 2023

The Digital Age calls for improvement of information literacy particularly among children and youth who are vulnerable to cybergrooming. Taking an interdisciplinary approach by leveraging our team's expertise including child and adolescent development, data analytics, and cybersecurity, this study proposes an interactive artificial intelligence (AI)-based preventive simulation program that raises youth knowledge and awareness about the risk of cybergrooming as well as increases resilient self-efficacy in their cybersecurity-relevant skills. The primary purpose of this project is to evaluate the effectiveness of the simulation program on preventing cybergrooming. More specifically, this study is designed to examine developmental changes in self-efficacy of cybersecurity-relevant skills among youth participants as a function of the preventive simulation program. Further, this study will identify risk and protective factors that explain interindividual differences in the ability of children and youth either to fall victim to advances from a cyber predator or to recognize and deter such threats. The preliminary data will help improve the effectiveness of the preventive simulation program as well as the methods of implementation to large groups of youth. The findings from the proposed study will contribute to making specific recommendations to parents, educators, practitioners, and policy makers for the prevention of cybergrooming.

• Korean Journal of Weed Science
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• v.13 no.1
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• pp.14-18
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• 1993

Greenhouse study was undertaken to find tank-mix feasibility of quinclorac with molinate and propanil, selective post-emergence herbicides in controlling barnyardgrass, for reducing the application rate of quinclorac. Following foliar application in combination of quinclorac at 0.038, 0.075, 0.150, and 0.300kg ai/ha with molinate at 0.190, 0.380, 0.750 and 1.500kg ai/ha, and propanil at 0.263, 0.525, 1.050, and 2.100kg ai/ha at 3.5-leaf stage of barnyardgrass, fresh weight and weeding efficacy and their interaction by Colby's efficacy method were evaluated. Percent inhibition of barnyardgrass growth by quinclorac, molinate and propanil at recommended rate were 78.1, 26.1, and 61.7%, respectively. The dose combination shown above 85% in weeding efficacy were from 0.300kg of quinclorac with 0.75kg of molinate and 0.150kg of quinclorac with all rates of propanil. Therefore, combination of quinclorac with molinate tended to additive interaction and that of quinclorac with propanil appeared partially synergistic interaction. Conclusively, for reducing the application rate of quinclorac, the combination of quinclorac with propanil was more synergistic than that of quinclorac with molinate.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.34-41
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• 2021

The purpose of this study is to explore any effects of primary English language learners' affective factors and satisfaction through AI-based speaking activity. In order to answer these questions, a total number of 46 ELLs from a public elementary school participated in this research. Survey questionnaire on affective factors and learning satisfaction were distributed and the results were analyzed quantitatively. The findings are as follows. First, participants could expand their knowledge on AI-based activity towards its educational advantages and capability. Second, overall affective factors of the participants on AI-based activity changed positively, with the improvement of the mean score. The paired samples t-test showed that there was a significant difference among interest, value and attitude. Third, the satisfaction degree on AI-based learning escalated, particularly in the sense of efficacy, academic achievement and involvement. Lastly, it was revealed that the satisfaction degree was correlated with learners' self-confidence, interest and attitude.

• Korean Medical Education Review
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• v.22 no.2
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• pp.99-114
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• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.656-659
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• 2023

정서적 지원 대화를 위한 챗봇 개발 시, 사용자의 챗봇에 대한 사용성 및 대화 적절성을 높이기 위해서는 사용자 감정에 적합한 지원 콘텐츠를 제공하는 것이 중요하다. 이를 위해, 본 논문은 사용자 입력 텍스트의 감정 강도 예측 모델을 제안하고, 사용자 발화 맞춤형 정서적 지원 대화에 적용하고자 한다. 먼저 입력된 한국어 문장에서 키워드를 추출한 뒤, 이를 각성도 (arousal)과 긍정부 정도(valence) 공간에 투영하여 키워드가 내포하는 각성도-긍정부정도에 가장 근접한 감정을 예측하였다. 뿐만 아니라, 입력된 전체 문장에 대한 감정 강도를 추가로 예측하여, 핵심 감정 강도 - 문맥상 감정강도를 모두 추출하였다. 이러한 통섭적 감정 강도 지수들은 사용자 감정에 따른 최적 지원 전략 선택 및 최적 대화 콘텐츠 생성에 공헌할 것으로 기대한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1797-1808
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• 2021

Artificial intelligence is considered one of the core technologies leading the 4th industrial revolution. It is adopted in various fields bringing about a huge paradigm shift throughout our society. The field of biotechnology is no exception. It is undergoing innovative development by converging with other disciplines such as computers, electricity, electronics, and so on. In drug discovery and development, big data-based AI technology has a great potential of improving the efficiency and quality of drug development, rapidly advancing to overcome the limitations in the existing drug development process. AI technology is to be specialized and developed for the purpose including clinical efficacy and safety-related end points based on the multidisciplinary knowledge such as biology, chemistry, toxicology, pharmacokinetics, etc. In this paper, we review the current status of AI technology applied for drug discovery and consider its limitations and future direction.

• 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.