• 한국수학교육학회지시리즈C:초등수학교육
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• 제26권4호
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• pp.299-316
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• 2023

본 연구는 교육에서 인공지능 활용의 중요성과 필요성이 제기됨에 따라 수학교육에서 인공지능 활용에 대한 초등 교사들의 인식을 탐색하는 것을 목적으로 한다. 이를 위하여 초등 교사 161명을 대상으로 인공지능과 수학교육에 대한 태도 및 수학 교수, 학습, 평가 도구로서 인공지능 활용에 대한 인식을 5점 Likert 척도를 활용하여 분석하였다. 연구 결과, 초등 교사들은 전반적으로 수학의 교수, 학습, 평가를 위한 도구로 AI를 활용하는 데에 긍정적인 인식을 드러냈다. 특히, AI를 활용한 수학교육은 맞춤형 개별 교수 학습, 선수 학습 보충, 평가 결과 분석에 도움이 될 것이며 인공지능이 교사의 역할을 대체할 수 없다는 데에 강한 긍정을 드러냈다. 한편, 초등 교사들은 인공지능을 활용한 수학 수업에 대한 자신감이나 준비에서는 상대적으로 낮은 인식을 드러냈는데, 이는 인공지능과 관련된 수학 수업의 실행이나 연수 이수의 여부에 따라 유의한 차이를 드러냈다. 본 연구의 결과를 바탕으로 수학 교육에서 인공지능을 효과적으로 활용하기 위한 교사의 역할 및 교사들에게 필요한 지원에 대한 시사점을 논의하였다.

• East Asian mathematical journal
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• 제39권2호
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• pp.93-117
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• 2023

The aim of this study is to explore the pre-service math teachers' characteristics of education to develop their AI literacy and SW competency, and to derive some implications. We conducted a 14-hours AI and SW education program for pre-service teachers with theory and practice, and an analysis on class observation data, video frames of classes and interview, Python programming assignments and papers. The results of this case study for 3 pre-service teachers are as follows. First, two students understood artificial neural network and deep learning system accurately, furthermore, all students conducted a couple of explorations related with performance improvement of deep learning system with interest. Second, coding and exploration activities using Python improved students' computational thinking as well as SW competency, which help them give convergence education in the future. Third, they responded positively to the necessity of AI literacy and SW competency development, and to applying coding to math class. Lastly, it's necessary to endeavor to give a coding education to the student's eye level according to his or her prerequisite and to ease the burden of student's studying AI technology.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제26권4호
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• pp.333-349
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• 2023

Recently, AI has become a crucial tool in mathematics education due to advances in machine learning and deep learning. Considering the importance of AI, examining teachers' beliefs about AI in mathematics education (AIME) is crucial, as these beliefs affect their instruction and student learning experiences. The present study developed a scale to measure preservice teachers' (PST) beliefs about AIME through factor analysis and rigorous reliability and validity analyses. The study analyzed 202 PST's data and developed a scale comprising three factors and 11 items. The first factor gauges PSTs' beliefs regarding their roles in using AI for mathematics education (4 items), the second factor assesses PSTs' beliefs about using AI for mathematics teaching (3 items), and the third factor explores PSTs' beliefs about AI for mathematics learning (4 items). Moreover, the outcomes of confirmatory factor analysis affirm that the three-factor model outperforms other models (a one-factor or a two-factor model). These findings are in line with previous scales examining mathematics teacher beliefs, reinforcing the notion that such beliefs are multifaceted and developed through diverse experiences. Descriptive analysis reveals that overall PSTs exhibit positive beliefs about AIME. However, they show relatively lower levels of beliefs about their roles in using AI for mathematics education. Practical and theoretical implications are discussed.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.597-599
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• 2022

IoT 디바이스에서 on-device AI를 수행할 때, 타겟 서비스나 디바이스의 환경에 따라 필요한 AI 모델이 달라질 수 있다. 또한, 기존 AI 모델도 federated learning과 같이 추가적인 데이터를 이용해 트레이닝을 하거나 보다 향상된 새로운 기법을 사용하는 등 업데이트가 일어날 수 있다. 이에 따라 IoT 디바이스에서 양질의 AI 서비스를 수행하기 위해서는 상황에 따라 필요한 AI 모델을 선택적으로 사용하거나 최적화된 최신 버전의 AI 모델로 업데이트 할 수 있어야 한다. 본 논문에서는 이를 지원하기 위한 AI 모델 레포지토리를 제안한다. 레포지토리는 AI 모델의 등록, 검색, 관리 및 배포를 지원하며 실사용을 위한 웹 포털을 포함한다. 제안하는 시스템의 실효성 확인을 위해 Node.js와 Vue.js로 구현하여 동작을 확인하였다.

• 정보교육학회논문지
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• 제24권4호
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• pp.391-400
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• 2020

인공지능이 4차 산업혁명 시대의 핵심 기술로 주목받는 가운데, 인공지능에 대한 기본적인 소양을 길러주기 위한 교육의 필요성이 대두되고 있다. 이에 따라 본 연구에서는 초·중등학생을 위한 프로젝트 기반의 AI 교육 프로그램을 개발하여 적용하고 그 효과를 분석하였다. 본 연구에 참여한 학생들은 3인 1조로 편성되어 2박 3일 일정의 프로젝트 학습을 수행하였으며, 이 과정에서 자신이 원하는 문제를 선택하고 인공지능 기술을 활용해 이를 해결하는 구체적인 산출물을 제작하였다. AI 프로젝트 수업의 효과를 알아보기 위해 활동 전후 학습자의 인공지능에 대한 태도와 효능감의 변화를 분석하였다. 분석 결과, AI 프로젝트 수업 이후 인공지능에 대한 태도와 효능감 모두에서 통계적으로 유의미한 수준의 긍정적인 변화가 확인되었다. 이러한 변화는 인지된 프로그래밍 언어 활용 수준이 높을수록, 프로젝트 학습에 대한 흥미 수준이 높을수록 더욱 뚜렷하게 나타났다.

• ETRI Journal
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• 제43권3호
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• Journal of Oral Medicine and Pain
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• 제47권2호
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.48-56
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• 2024

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

• 한국수학교육학회지시리즈A:수학교육
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• 제63권2호
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• pp.387-392
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• 2024

The use of artificial intelligence (AI) in mathematics education has advanced as a means for promoting understanding of mathematical concepts, academic achievement, computational thinking, and problem-solving. From a total of 13 studies in this special issue, this editorial reveals threads of potential and future directions to advance mathematics education with the integration of AI. We generated five themes as follows: (1) using ChatGPT for learning mathematical content, (2) automated grading systems, (3) statistical literacy and computational thinking, (4) integration of AI and digital technology into mathematics lessons and resources, and (5) teachers' perceptions of AI education. These themes elaborate on the benefits and opportunities of integrating AI in teaching and learning mathematics. In addition, the themes suggest practical implementations of AI for developing students' computational thinking and teachers' expertise.