• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Information Systems Review
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• v.26 no.1
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• pp.19-56
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• 2024

This study is empirical research to enhance understanding of AI (artificial intelligence) training data project in South Korea. It primarily focuses on the various concerns regarding data quality from policy-executing institutions, data construction companies, and organizations utilizing AI training data to develop the most reliable algorithm for society. For academic contribution, this study suggests a theoretical foundation and research model for understanding AI training data quality and its antecedents, as well as the unique data and ethical aspects of AI. For this purpose, this study proposes a research model with important antecedents related to AI training data quality, such as data attribute factors, data building environmental factors, and data type-related factors. The study collects 393 sample data from actual practitioners and personnel from companies building artificial intelligence training data and companies developing artificial intelligence services. Data analysis was conducted through Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Network analysis (ANN), presenting academic and practical implications related to the quality of AI training data.

• Journal of Korea Technology Innovation Society
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• v.22 no.3
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• pp.340-361
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• 2019

This study analyzes the technological structure of artificial intelligence (AI) and technological capabilities of AI companies based on patent information. 2589 AI patents registered in USPTO from 2007 to 2017 were collected and analyzed by the Latent Dirichlet Allocation (LDA) to derive 20 AI technology topics. Analysis of technology development trends by AI technology reveals that visual understanding, data analysis, motion control, and machine learning are growing, while language understanding and speech technology are sluggish. In addition, we also investigated leading companies in each sub-field of AI as well as core competencies of global IT companies. The findings of this study are expected to be fruitfully used for formulation and implementation of technology strategy of AI companies.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.491-499
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• 2021

Major countries in the world have established strategies for educating about artificial intelligence(AI) and with large investments are actively implementing these strategies. With this trend, domestic ministries have made efforts to establish national strategies to better educate students about AI. This paper presents the syllabus of AI classrooms which has been developed and presented to pre-service and in-service elementary school teachers for their use. In addition, the AI education tools they particularly preferred and their future plans for utilizing them in the elementary school classroom were investigated. Through this study, it was found that pre-service and in-service elementary school teachers strongly prefer lectures about AI education tools that can be immediately applied in the classroom, rather than learning about the theoretical basis of AI. At issue, however, is that the ability to utilize AI is usually based on a sufficient understanding of the theory. Thus, this paper suggests further study to identify better pedagogical practices to improve students' understanding the theoretical basis of AI.

• Journal for History of Mathematics
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• v.34 no.6
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• pp.195-204
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• 2021

Mathematical induction is one of the deductive methods used for proving mathematical theorems, and also used as an inductive method for investigating and discovering patterns and mathematical formula. Proper understanding of the mathematical induction provides an understanding of deductive logic and inductive logic and helps the developments of algorithm and data science including artificial intelligence. We look at the origin of mathematical induction and its usage and educational aspects.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.443-451
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• 2020

In elementary school software education, a programming process is experienced through a simple problem solving process. And even this experience emphasizes that the problem-solving process is a CS Unplugged activity. However, CS Unplugged has a disadvantage in that it only learns the principles of computing, and the learner cannot experience real problem solving. In this study, a learning program using artificial intelligence robots was developed with the goal of cultivating the ability to solve problems encountered in the real life of elementary school students. Students could solve complex problems in real life from the point of view of artificial intelligence through the developed program, and increase their interest and understanding of artificial intelligence education through robot control.

• The Mathematical Education
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• v.62 no.3
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• pp.401-416
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• 2023

This study systematically examined the influence of preservice teachers' self-efficacy and AI anxiety, on the intention to use AI programs 'knock-knock! mathematics expedition' in mathematics lessons based on a technology acceptance model. The research model was established with variables including self-efficacy, AI anxiety, perceived ease of use, perceived usefulness, and intention of use from 254 pre-service teachers. The structural relationships and direct and indirect effects between these variables were examined through structural equation modeling. The results indicated that self-efficacy significantly affected perceived ease of use, perceived usefulness, and intention to use. In contrast, AI anxiety did not significantly influence perceived ease of use and perceived usefulness. Perceived ease of use significantly affected perceived usefulness and intention to use and perceived usefulness significantly affected intention to use. The findings offer insights and strategies for encouraging the use of 'knock-knock! mathematics expedition' by preservice teachers in mathematics lessons.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.413-421
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• 2022

The online questionnaire platform was conducted with Korean medicine doctors to analyses the recognition of applicability of artificial intelligence(AI) to the field of application and department of Korean medicine. Most of all respondents did not have a chance to participate academic experience or research experience related to AI, but had a high willingness to participate in further learning and research. The level of AI understanding was supervised learning When AI is introduced to Korean medicine, the mean predicted usefulness scores to each application field for research and development of oriental medicine(74.60 points) and social policy establishment(73.68 points) are significantly higher than other of Korean medicine field of application, while those of Sasang constitutional department(66.61 points) and Korean medicine rehabilitation(65.91 points) were evaluated higher than other fields of treatment of Korean medicine. Respondents judged that the introduction of AI could be realistically useful in relatively formal fields of Korean medicine, while it would be difficult in non-formal fields.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.145-156
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• 2022

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.309-314
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• 2021

Even though environmental education has been getting more attention along with the recent rapid increase of natural disasters such as heat wave, heavy snow and downpour, it is unlikely for elementary schools to provide actual lessons due to the shortage of financial support and educational resources. This study is designed to enhance the recycling judgement of the elementary students to define paper, glass, plastic, PET, metal by using AI programming. The survey from the student participants shows that the learning and practice with AI programming was positively helpful for more than 70% of the participants in knowledge obtaining and understanding of recycling. The participants also gained better understanding on artificial intelligence and got motivated to have more opportunities to learn artificial intelligence programming.