• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.321-328
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• 2024

Due to the recent development of artificial intelligence technology, the field of education also needs fundamental changes, such as digital competency and digital media literacy education. However, it is still a situation where one teacher has no choice but to teach diverse students in one classroom. To solve these problems, the Ministry of Education announced a 'digital-based education innovation plan' focusing on the introduction of 'AI digital textbooks'. The purpose of this study is to examine the current status of the government's digital-based education innovation and suggest specific measures for digital-based education innovation. First, one of the key points of the 2022 revised curriculum is the establishment of a teaching, learning, and evaluation system suitable for the digital and AI educational environment. Second, the contents of the 2023 Ministry of Education's digital-based education innovation plan are as follows. The direction of implementation is the realization of the vision of customized education for all, gradual voluntary expansion through digital leading schools and leading teachers, and establishment of cooperative partnerships with various entities within the government and the private sector. The detailed implementation plan for digital-based education innovation is as follows. First, improving digital infrastructure. Second, digital conversion teacher education and capacity building. Third, providing a digital, personalized learning experience. Fourth, developing digital content and platforms. Fifth, ensuring digital safety and personal information protection. Sixth, overcoming digital social inequality.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.387-388
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• 2023

본 논문에서는 인공지능(AI) 체험부터 초중등, 대학 및 평생교육에서 필요한 광범위한 응용과 활용을 할 수 있는 라이브러리를 디지털북 형태로 지원하며, 블록과 텍스트 코딩의 장점을 취합해 입문자들이 쉽고 재미있게 SW·AI 융합 교육을 할 수 있는 플랫폼을 구현하였다. 플랫폼은 웹어셈블리 기반의 파이오다이드를 통해 웹 브라우저에서 파이썬 코딩을 가능하게 하고 복잡한 설치과정 없이 쉽게 이용이 가능하다. 다양한 LMS와 연동이 가능하도록 API를 제공하며, Drag & Fill 블록으로 입문자가 코딩에 겪는 어려움 중 하나인 많은 양의 함수와 파라미터 사용법의 어려움을 해소하였다. 플랫폼은 블록으로 코딩하여 문법의 어려움, 오탈자, 오류 등을 줄이는 동시에 블록에서 생성되는 파이썬 텍스트 코드로 입문자가 텍스트 코드에 익숙해질 수 있는 경험을 제공한다.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.71-76
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• 2022

The innovative development of the 4th industry and the COVID-19 pandemic caused a great change in the education, eventually requiring elementary, middle and high schools, including kindergartens, to implement artificial intelligence(AI) education. However, since early childhood AI education is conducted in the form of results-oriented and special activities, the need for research on what early childhood AI education is and how to apply it to the Nuri curriculum has been raised. Accordingly, this study defined early childhood AI education through literature research, identified the contents of AI education, and organized and operated it in the Nuri curriculum. As a results, AI education for children should be conducted for the purpose of cultivating digital capabilities based on computing thinking skills, and computers, the Internet, and programs were extracted as sub-elements of child AI education contents. Two approaches were proposed to incorporate this into the Nuri curriculum. The first is to set each of the three AI education contents as a life theme, select sub-factors accordingly, and plan and implement activities suitable for each sub-factors. The second is to develop and operate AI education contents at the level of sub-educational activities in accordance with the life theme of the existing Nuri curriculum. It is hoped that this study will consider the characteristics of early childhood education and be organized in the Nuri curriculum to realize the true meaning of early childhood AI education, and more research on AI play education programs according to the five areas of the Nuri curriculum.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.513-520
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• 2024

This study investigates the changes in teachers' roles as the impact of AI on school education expands. Traditionally, teachers have been responsible for core aspects of classroom instruction, curriculum development, assessment, and feedback. AI can automate these processes, particularly enhancing efficiency through personalized learning. AI also supports complex classroom management tasks such as student tracking, behavior detection, and group activity analysis using integrated camera and microphone systems. However, AI struggles to automate aspects of counseling and interpersonal communication, which are crucial in student life guidance. While direct conversational replacement by AI is challenging, AI can assist teachers by providing data-driven insights and pre-conversation resources. Key competencies required for teachers in the AI era include expertise in advanced instructional methods, dataset analysis, personalized learning facilitation, student and parent counseling, and AI digital literacy. Teachers should collaborate with AI to emphasize creativity, adjust personalized learning paths based on AI-generated datasets, and focus on areas less amenable to AI automation, such as individualized learning and counseling. Essential skills include AI digital literacy and proficiency in understanding and managing student data.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.227-236
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• 2021

This study aims to develop an AI education program for secondary school students to help understand AI and to provide an experience of solving real-life problems by using AI, and to analyze the effectiveness of education. The education program based on the AI education system for K-12 developed in the previous study was composed of a total of 12 lessons by selecting the self-driving cars, which is emerging as a recent issue among real life problems, as the main topic. Classes were conducted for secondary school students who had experience in software education, and the effectiveness of education and class satisfaction were analyzed. As a result of the analysis, it was confirmed that the understanding of AI and the sense of AI efficacy were improved, and the class satisfaction was high in all items such as educational content, fun in class, difficulty of class, and interest in AI. Based on these results, implications for AI education for secondary students were proposed.

• Journal of Christian Education in Korea
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• v.79
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• pp.121-145
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• 2024

This study aims to systematically analyze the latest research trends on the educational applications of generative AI. The analysis focused on 107 domestic studies on the educational use of generative AI published between May 2023 and May 2024, examining research fields, methodologies, and subjects. The results show that generative AI has been actively studied in various fields, including digital literacy, educational support, subject education, and language education, with a particular emphasis on literacy and educational support. Methodologically, the majority of studies were empirical, applying generative AI in educational contexts, with university students being the primary research subjects. Based on these findings, this study proposes future research directions and discusses effective strategies for the educational use of generative AI. In particular, the study emphasizes the importance of prompt generation strategies within educational contexts, as well as the need for guidelines and educational programs to address ethical issues and the reliability of information generated by AI. Additionally, the study suggests the necessity of research on AI applications tailored to various educational stages and subjects, the redefinition of the roles of educators and learners, and the development of customized AI applications for specific learner groups, such as special education and gifted education.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.157-162
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• 2022

In a society full of knowledge and information, digital literacy and artificial intelligence (AI) education that can utilize AI technology is needed to solve numerous everyday problems based on computational thinking. In this study, data-centered AI education was conducted while teaching computer programming to non-computer programming students at universities, and the correlation between major factors related to academic performance was analyzed in addition to student satisfaction surveys. The results indicated that there was a strong correlation between grades and problem-solving ability-based tasks, and learning satisfaction. Multiple regression analysis also showed a significant effect on grades (F=225.859, p<0.001), and student satisfaction was high. The non-computer programming students were also able to understand the importance of data and the concept of AI models, focusing on specific examples of project types, and confirmed that they could use AI smoothly in their fields of interest. If further cases of AI education are explored and students' AI education is activated, it will be possible to suggest its direction that can collaborate with experts through interest in AI technology.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.879-890
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• 2021

AI education is being emphasized nationwide as a literacy education. At this point, it is necessary to identify critical issues and suggest the direction of future research by examining domestic AI education research trends. To this end, the study applied the scoping review method. A total of 29 AI educational studies from 2017 to 2020 in South Korea were analyzed. As a result, it was confirmed that the number of studies increased rapidly in 2020, and a large proportion of studies targeted elementary school students. In addition, the study found that AI principles were treated as contents at a high rate, both cognitive and affective aspects were frequently reported as a learning outcome, and various practice environments were used relatively evenly. Based on the results, the direction of future research was discussed and suggested.