• Korean Journal of Child Studies
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• v.4
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• pp.1-10
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• 1983

This study examined the characteristics of the relationship of home environment variables and preschool children's intelligence, learning readiness and socio-emotional developments. The subjects of this study were 63 children at age five and their mothers. Instruments included the children's intelligence test, preschool inventory for learning readiness, the socio-emtional rating scale and the inventory of HOME. The data of the present study were analyzed by the statistical methods of Pearson's product-moment correlation coefficient and step-wise multiple regression analysis. The kinds of HOME variables that significantly predict children's intelligence were "need gratification and avoidance of restriction" "quality of language environment" "play materials" "aspects of physical environment" "organization of stable and predictable environment". The variables that significantly predict children's socio-emotional developments were "breath of experience" "fostering maturity and independence" "developmental stimulation". All of the HOME variables were not significantly predict children's learning readiness. The kinds of HOME factors that significantly predict children's intelligence were factor II and factor III. Factor I predicted children's socio-emotional developments significantly. All of the HOME factors were not significantly predicted children's learning readiness.

• Korean Journal of Child Studies
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• v.25 no.1
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• pp.1-12
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• 2004

This study investigated the relations of children's age, intelligence, and the concept of conservation to their learning of English. Ten 4-year-old children from 1 child-care center and 13 7-year-old children from 1 elementary school were tested after completion of 8 sessions of experimental English classes. Children's intelligence was measured by K-WPPSI for 4-year olds and K-WISC for 7-year-olds. Children were tested for number and liquid conservations. A Korean teacher with 11 years of experience of teaching children at American elementary schools taught the 2 groups with the same subjects and methods. Data were analysed by independent samples t-test, Mann-Whitney U test, and Pearson's r. The results showed that children's age and the concept of conservation were related to English learning. No statistically significant relationship with IQ was found.

• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.697-713
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• 2013

The research aimed to investigate characteristics of middle school students enrolled in a science gifted education center affiliated with university in terms of multiple intelligence, self-regulated learning and personality traits. The 89 subjects in the study responded to questionnaires of multiple intelligence, self-regulated learning ability and a personality trait in October, 2011. It was found that both science and math gifted students presented intrapersonal intelligence as strength and logical-mathematical intelligence as weakness. While physics and earth science gifted ones showed spatial intelligence as strength, chemistry and biology gifted ones did intrapersonal intelligence. For self-regulated learning ability, both science and mathematics gifted students tend to show higher levels than general students, in particular, cognitive and motivation strategies comparatively higher than meta-cognition and environment condition strategies. Characteristics of personal traits widely distributed across science and mathematics gifted students, showing that each gifted student presented distinct characteristics individually. Those gifted students showing certain intelligence such as spatial, intrapersonal, or natural intelligences as strength also showed different characteristics of self-regulated learning ability and personal traits among students showing same intelligence as strength. It was concluded that science and mathematics gifted students showed various characteristics of multiple intelligences, self-regulated learning ability, and personal traits across science and mathematics areas.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.221-232
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• 2021

The purpose of this study is to explore the design direction of artificial intelligence convergence liberal arts subjects based on flip learning and maker education through analysis of learner needs in a non-face-to-face classroom environment caused by COVID-19. To this end, we analyzed the priorities of subject content elements by using the Borich needs assessment and The Locus for Focus model along with students' perceptions of flip learning for students who took and did not take maker education-based liberal arts courses. Based on this, it was used as basic data for designing the curriculum. The study results are as follows. First, the content elements of the artificial intelligence liberal arts curriculum based on maker education consisted of a total of 9 areas and were designed as a class using flip learning. Second, the areas with the highest demand for education are 'Artificial Intelligence Theory', 'Artificial Intelligence Programming Practice', 'Physical Computing Theory', 'Physical Computing Practice', followed by 'Convergence Project', '3D Printing Theory', '3D Printing practice' was decided. Third, most of the questionnaires regarding the application of flip learning in maker education-based artificial intelligence liberal arts subjects showed positive responses regardless of whether they took the course, and the satisfaction of the students was very high. Based on this, an artificial intelligence-based convergence liberal arts curriculum using flip learning and maker education was designed. This is meaningful in that it provides an opportunity to cultivate artificial intelligence literacy for college students by preparing the foundation for artificial intelligence convergence education in liberal arts education by reflecting the needs of students.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.1-9
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• 2017

Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.91-97
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• 2007

Game program had been classed by 3D or on-line game etc, and engine and game programming simply, But, game programmer's kind more classified new, Artifical Intelligence game programmer's role is important. This paper makes game character study and moved by intelligence using reinforcement learning algorithm. Fought with character enemy using developed game, Confirmed whether embodied game character is facile by intelligence, As result of an experiment, we know, studied character defends excellently than randomly moved character.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1512-1523
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• 2022

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

• 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.

• Women's Health Nursing
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• v.26 no.1
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• Korean Medical Education Review
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• v.18 no.2
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• pp.47-50
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• 2016

Development of artificial intelligence is expected to revolutionize today's medicine. In fact, medicine was one of the areas to which advances in artificial intelligence technology were first applied. Recently, state-of-the-art artificial intelligence, especially deep learning technology, has been actively utilized to treat cancer patients and analyze medical image data. Application of artificial intelligence has the potential to fundamentally change various aspects of medicine, including the role of human doctors, the clinical decision-making process, and even overall healthcare systems. Facing such fundamental changes is unavoidable, and we need to prepare to effectively integrate artificial intelligence into our medical system. We should re-define the role of human doctors, and accordingly, medical education should also be altered. In this article, we will discuss the current status of artificial intelligence in medicine and how we can prepare for such changes.