• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.147-157
• /
• 2023

In this study, we approached from the perspective of AI convergence education in elementary, middle and high schools to understand AI convergence education. We examined what capabilities AI convergence education ultimately seeks to pursue, and analyzed various examples of AI convergence education in three dimensions: core curriculum, convergence model, AI learning elements and learning activities. In addition, factors to be considered in order for AI convergence education to be actively carried out include the cultivation of AI convergence education capabilities of teachers, the development and dissemination of AI teaching and learning methods and teaching and learning models, and evaluation methods for AI convergence education.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.790-791
• /
• 2023

본 논문은 스마트 미러를 활용한 스마트팜 디스플레이 장치의 중요성과 잠재적인 활용 가능성을 논의하며, 농업 분야의 혁신과 정보 기술의 융합이 농업 생산성을 향상하는 역할을 강조한다. 스마트 미러를 통해 제공되는 실시간 정보와 데이터는 농업 종사자들에게 생산성 향상 및 농작물의 품질을 향상 시키며, 지속 가능한 농업을 실현하는 데 도움을 줄 것으로 기대된다.

• Journal of Internet Computing and Services
• /
• v.24 no.5
• /
• pp.121-130
• /
• 2023

In the school field, AI convergence education is recommended, which utilizes AI in education to change the paradigm of society. This study was conducted to define the terms of AI and AI convergence education to minimize the confusion of terms and to analyze the educational needs of teachers from the perspective of conducting AI convergence education. To achieve the purpose, 19 experts' opinions were collected, and a self-administered questionnaire was administered to 125 secondary school teachers enrolled in the AI convergence major at the Graduate School of Education. As a result of the analysis, the experts defined AI convergence education as a methodology for problem solving, not AI-based or utilization education. In the analysis of teachers' educational needs, "AI and big data" was ranked first, followed by "AI convergence education methodology" and "learning practice using AI". The significance of this study is that it defined the terminology by collecting the opinions of experts amidst the confusion of various terms related to AI, and presented the educational direction of AI convergence education for in-service teachers.

• Journal of The Korean Association of Information Education
• /
• v.25 no.3
• /
• pp.471-481
• /
• 2021

The purpose of this study is to analyze the effectiveness of AI convergence education program. To this end, the "Elementary Science AI Convergence Education Program for Machine Learning" developed in previous research were taught to elementary school students in the fourth to sixth grades in eight times. The quantitative changes of each factor were analyzed by R program, and the effectiveness of education was analyzed by Pearson correlation and paired samples t-test. As a result, there is a deep correlation between "Attitude to AI technology, Scientific preference and STEAM Literacy" and technical average has improved in many factors. Therefore, AI convergence education program is meaningful in terms of education, and if AI education and AI convergence education are implemented into the primary formal education curriculum, they will have a positive effect.

• Journal of The Korean Association For Science Education
• /
• v.44 no.2
• /
• pp.141-153
• /
• 2024

The 2022 revised science curriculum aims to develop the ability to solve scientific problems arising in daily life and society based on convergent thinking stimulated through participation in research activities using artificial intelligence (AI). Therefore, we developed a science-AI convergence education program that combines the science curriculum with artificial intelligence and employed it in convergence classes for high school students. The aim of the science-AI convergence class was for students to qualitatively understand the movement of a damped pendulum and build an AI model to predict the position of the pendulum using the block coding platform KNIME. Individual in-depth interviews were conducted to understand and interpret the learners' experiences. Based on Giorgi's phenomenological research methodology, we described the learners' learning processes and changes, challenges and limitations of the class. The students collected data and built the AI model. They expected to be able to predict the surrounding phenomena based on their experimental results and perceived the convergence class positively. On the other hand, they still perceived an with the unfamiliarity of platform, difficulty in understanding the principle of AI, and limitations of the teaching method that they had to follow, as well as limitations of the course content. Based on this, we discussed the strengths and limitations of the science-AI convergence class and made suggestions for science-AI convergence education. This study is expected to provide implications for developing science-AI convergence curricula and implementing them in the field.

• Journal of The Korean Association of Information Education
• /
• v.25 no.3
• /
• pp.483-490
• /
• 2021

In the era of the fourth industrial revolution, the importance of artificial intelligence(AI) is growing day by day, and there is no disagreement that AI education will bring great innovation in the future. Various attempts are being made to educate the topic of AI, but students who have no experience in AI education recognize AI only as a difficult target. Therefore, in this study, we analyze the changes in students' perception of AI by teaching them using AI. AI convergence education were conducted for 6th grade elementary school students, and pre and post tests were conducted in the form of AI awareness survey questionnaires which included questions such as interest in AI, changes brought by AI, and AI education. As a result, we confirm significant results that suggest the level of awareness of AI has improved through AI education in all factors. AI convergence education requires various AI convergence education programs as a form of education for social needs and future students, and hopefully a design based on this will help realize student centered education.

• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.411-418
• /
• 2021

In this study, in order to analyze the AI convergence education courses of 38 graduate schools of education, the analysis frame was constructed in terms of basic subject classification, content field of subject, and detailed subject composition by field. And as a result of analysis through this frame, it was found that the number of subjects currently operated by these graduate schools of education is very different from 14 subjects to 48 subjects. Therefore, it was judged that it was urgent to develop a standard curriculum for the AI convergence education major operated by each graduate school of education for the same purpose. The AI Convergence Education Major, which was established for the same purpose and operated in different forms, will eventually produce teachers with different competencies, so there is a risk of bringing confusion to the direction of AI Convergence Education in the school field.

• Advanced Industrial SCIence
• /
• v.3 no.1
• /
• pp.1-16
• /
• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1061-1062
• /
• 2023

양자 CMOS 이미지와 QD-OLED 하이드로겔 저온 증폭 기술을 활용하여 기존 코로나 진단법의 한계를 극복하고, Machine Learning 모델을 통해 자동화된 바이러스 검출 시스템을 개발하는 것이다. 이를 통해 전문가 개입 없이도 높은 정확도로 질병 진단을 수행하는 웹 서비스를 구축함으로써, 코로나와 같은 전염병의 조기 진단과 효율적인 대응을 위한 새로운 도구를 제공하는 것이 목표이다. 이를 통해 의료 분야에서의 혁신과 질병관리의 향상에 기여할 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.81-83
• /
• 2023

태양 에너지 수집형 IoT 기기는 주기적으로 재충전되는 태양 에너지의 특성상, 에너지 소모를 최소화하기보다는 수집된 에너지를 최대한 유용하게 사용하는 것이 중요하다. 한편, 데이터 기밀성과 프라이버시, 응답속도, 비용 등의 이유로 클라우드가 아닌 데이터 소스 근처에서 머신러닝을 수행하는 엣지 AI에 대한 연구도 활발한데, 그 중 하나는 여러 IoT 장치들이 수집한 오디오 데이터를 활용하여, 다양한 AI 응용들을 IoT 엣지 컴퓨팅 환경에서 제공하는 것이다. 그러나, 이와 관련된 많은 연구에서, IoT 기기들은 에너지의 제약으로 인하여, 엣지 서버(IoT 서버)로의 센싱 데이터 전송만을 수행하고, 데이터 전처리를 포함한 모든 AI 과정은 엣지 서버에서 수행한다. 이 경우, 엣지 서버의 과부하 문제 뿐 아니라, 학습 및 추론에 불필요한 데이터까지도 서버에 그대로 전송되므로 네트워크 과부하 문제도 야기한다. 또한, 이를 해결하고자, 데이터 전처리 과정을 각 IoT 기기에 모두 맡긴다면, 기기의 에너지 부족으로 정전시간이 증가하는 또 다른 문제가 발생한다. 본 논문에서는 각 IoT 기기의 에너지 상태에 따라 데이터 전처리 여부를 결정함으로써, 기기들의 정전시간 증가 문제를 완화시키면서 서버 집중형 엣지 AI 환경의 문제들(엣지 서버 및 네트워크 과부하)을 완화시키고자 한다. 제안기법에서 IoT 장치는 기기가 기본적으로 동작하는 데 필요한 에너지 외의 여분의 에너지 양을 예측하고, 이 여분의 에너지가 있는 경우에만 이를 사용하여 기기에서 전처리 과정, 즉 수집 대상 소리 판별과 잡음 제거 과정을 거친 후 서버에 전송함으로써, IoT기기의 정전시간에 영향을 주지 않으면서, 에너지 적응적으로 데이터 전처리 위치(IoT기기 또는 엣지 서버)를 결정하여 수행한다.