• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.757-760
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• 2014

프로그래밍 능력은 21세기 정보화 사회를 살아가는데 있어 인간이 컴퓨터를 활용하여 다양한 문제를 해결할 수 있도록 도움을 준다. 효과적인 프로그래밍 교육이 이루어지기 위해서는 학습자들에게 순차적 수행, 조건적 수행, 반복적 수행과 같은 기본적인 프로그래밍 개념을 습득하도록 할 필요가 있다. 따라서 본 연구는 스도쿠 인공지능 프로그래밍 교육에서 프로그래밍의 기본 개념을 바탕으로 알고리즘을 설계하는 방법을 학습시키는 방안을 모색하기 위한 목적이 있다. 연구의 목적을 달성하기 위해 중학생 10명을 대상으로 실험 연구를 진행하였다. 연구 결과, 학습자는 연구자가 제안한 활동지가 알고리즘 설계 학습에 도움이 된 것으로 인식한 것을 확인할 수 있었다. 본 연구는 프로그래밍 교육에서 초보 학습자가 이해하기 어려워하는 프로그래밍 개념을 학습하는데 도움이 되는 학습 방법을 제시하였다는데 의의가 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1022-1024
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• 2022

최근 인공지능 기술이 급격하게 발전하고 빠르게 보급되면서, 머신러닝 시스템을 대상으로 한 다양한 공격들이 등장하기 시작하였다. 인공지능은 많은 강점이 있지만 인위적인 조작에 취약할 수 있기 때문에, 그만큼 이전에는 존재하지 않았던 새로운 위험을 내포하고 있다고 볼 수 있다. 본 논문에서는 데이터 유형 별 적대적 공격 샘플을 직접 제작하고 이에 대한 효과적인 방어법을 구현하였다. 영상 및 텍스트 데이터를 기반으로 한 적대적 샘플공격을 방어하기 위해 적대적 훈련기법을 적용하였고, 그 결과 공격에 대한 면역능력이 형성된 것을 확인하였다.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.409-414
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• 2024

This paper utilizes artificial intelligence to analyze vocational training evaluation data for people with disabilities and selects the optimal prediction model using various machine learning algorithms. It predicts the job categories most likely to employ trainees based on data such as gender, age, education level, type of disability, and basic learning abilities. The goal is to design customized training programs based on these predictions to enhance training efficiency and employment success rates.

• The Mathematical Education
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• v.63 no.2
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• pp.369-385
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• 2024

With the advancement of digital technology, a variety of digital materials are being utilized in education. For their appropriate use of digital resources, teachers need to be able to evaluate the quality of digital resource and determine the suitability for teaching. This study explored how preservice teachers evaluate TocToc-Math, an Artificial Intelligence (AI)-based math support system. Based on an evaluation framework developed through prior research, preservice teachers evaluated TocToc-Math with evidence-based criteria, including content quality, pedagogy, technology use, and mathematics curriculum alignment. The findings shows that preservice teachers positively evaluated TocToc-Math overall. The evaluation tendencies of preservice teachers were classified into three groups, and the specific characteristics of each factor differed depending on the group. Based on the research results, we suggest implications for improving preservice teachers' evaluation abilities regarding the use of digital technology and AI in mathematics education.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.59-66
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• 2021

In this paper, an improved integrated security control procedure is newly proposed by applying artificial intelligence technology to integrated security control and unifying the existing security control and AI security control response procedures. Current cyber security control is highly dependent on the level of human ability. In other words, it is practically unreasonable to analyze various logs generated by people from different types of equipment and analyze and process all of the security events that are rapidly increasing. And, the signature-based security equipment that detects by matching a string and a pattern has insufficient functions to accurately detect advanced and advanced cyberattacks such as APT (Advanced Persistent Threat). As one way to solve these pending problems, the artificial intelligence technology of supervised and unsupervised learning is applied to the detection and analysis of cyber attacks, and through this, the analysis of logs and events that occur innumerable times is automated and intelligent through this. The level of response has been raised in the overall aspect by making it possible to predict and block the continuous occurrence of cyberattacks. And after applying AI security control technology, an improved integrated security control service model was newly proposed by integrating and solving the problem of overlapping detection of AI and SIEM into a unified breach response process(procedure).

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.199-208
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• 2021

In this paper, the effect of artificial intelligence convergence education program that provides STEAM education using machine learning platform on elementary school students' STEAM literacy and learning flow was analyzed. A homogeneous group of 44 elementary school 6th graders was divided into an experimental group and a control group. The control group received 10 lessons of general subject convergence class, and the experimental group received 10 lessons of STEAM-based artificial intelligence convergence education using Machine learning for Kids. To develop the artificial intelligence convergence education program, the goals, achievement standards, and content elements of the 2015 revised curriculum to select subjects and class contents is analyzed. As a result of the STEAM literacy test and the learning flow test, there was a significant difference between the experimental group and the control group. In particular, it can be confirmed that the coding environment in which the artificial intelligence function is expanded has a positive effect on learners' learning flow and STEAM literacy. Among the sub-elements of convergence talent literacy, significant differences were found in the areas of personal competence such as convergence and creativity. Among the sub-elements of learning flow, significant differences were found in the areas such as harmony of challenge and ability, clear goals, focus on tasks, and self-purposed experiences. If further expanded research is conducted in the future, it will be a basic research for more effective education for the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.305-308
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• 2003

요즘 사람들이 많이 즐기는 전략 게임은 전략 시뮬레이션이라는 말이 무색할 정도로 장르가 가지는 특성 을 이행하지 못하고 있다. 그래서 게이머들은 별다른 전략 없이 쉽게 컴퓨터를 상대로 쉽게 게임을 승리 할 수 있게 됐다. 이것은 게임의 재미를 크게 반감시키는 한 요인이 된다. 전략 게임의 컴퓨터 플레이어에게 상황 판단과 학습 능력을 갖게 하면, 게이머가 보다 재미있게 컴퓨터와 대전을 할 수 있다. 본 논문에서는 인공지능을 가지는 컴퓨터 플레이어에 사용될 Default 추론 엔진과 컴퓨터 플레이어의 작전과 행동을 결정하기 위한 action & strategy generator 시스템을 연구한다. Default 추론 엔진은 귀납적 학습방법을 통 해서 컴퓨터 플레이어가 추론 및 학습을 할 수 있는 정보를 생성하게 된다. 이렇게 생성된 정보를 바탕으로 컴퓨터 캐릭터의 행동과 전략을 결정한다. 이에 본 논문에서는 전략 게임에 인공 지능으로 machine leaning 기법 중의 하나인 decision Tree 틀 사용하였다. decision Tree를 적용하여 기존 컴퓨터 플레이어의 행위와 어떻게 다른지 차별성을 밝혀내고, 컴퓨터 플레이어가 향상된 전략을 구사할 수 있게 하는 것이 주된 목표다.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.61-67
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• 2021

The purpose of this study is to demonstrate how EFL learners can overcome the limitations of traditional classes and practice communication through the learning activity model. As a research method, it was conducted from March to June 2019 to develop and derive strategies and guidelines through model development, validation, and application. After two validity tests, the model was applied to the experimental group, resulting in an increase of self-direction, engagement, problem-solving, and participation. Moreover the post results showed significant results in all fields, the usefulness of this model was confirmed. However, continuous follow-up research is needed, including the development of software that can easily apply AI related to English learning to classes, and the presentation of convergence activities with more systematic maker education in learning activities.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.44-59
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• 2023

Purpose: In the event of mass casualties, triage must be done promptly and accurately so that as many patients as possible can be recovered and returned to the battlefield. However, medical personnel have received many tasks with less manpower, and the battlefield for classifying patients is too complex and uncertain. Therefore, we studied an artificial intelligence model that can assist and replace medical personnel on the battlefield. Method: The triage model is presented using reinforcement learning, a field of artificial intelligence. The learning of the model is conducted to find a policy that allows as many patients as possible to be treated, taking into account the condition of randomly set patients and the medical capability of the military hospital. Result: Whether the reinforcement learning model progressed well was confirmed through statistical graphs such as cumulative reward values. In addition, it was confirmed through the number of survivors whether the triage of the learned model was accurate. As a result of comparing the performance with the rule-based model, the reinforcement learning model was able to rescue 10% more patients than the rule-based model. Conclusion: Through this study, it was found that the triage model using reinforcement learning can be used as an alternative to assisting and replacing triage decision-making of medical personnel in the case of mass casualties.