• Journal of Practical Engineering Education
• /
• v.15 no.3
• /
• pp.597-603
• /
• 2023

This study evaluates the AI digital competencies of teachers and presents a step-by-step framework for teacher's AI digital competencies that can be utilized in training. To do this, AI digital competencies were analyzed from the perspective of utilization and disposition, linked with the Technological Pedagogical Content Knowledge (TPACK) perspective. Then, as a precedent for step-by-step teacher AI digital competencies, the 3-step competency of the British Education and Training Foundation and the UNESCO ICT Teacher Competency Framework were presented. In this study, teacher's AI digital competencies were divided into three stages: entry, adaptation, and leadership, considering precedent research and domestic conditions. The initial entry stage passed the validity test in the second round of the Delphi survey, and the other two stages passed in the first round. The final entry stage is described as a stage where teachers understand AI digital but have difficulty implementing it, the adaptation stage is a level applied to standard curricula, and the leadership stage is a level where AI digital is applied in advanced courses and teachers serve as models for others. Through the overall AI digital competencies presented in this study, detailed competency development is possible, and it can be used as a reference material for developing evaluation items.

• Journal of Practical Engineering Education
• /
• v.16 no.4
• /
• pp.513-520
• /
• 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 Internet of Things and Convergence
• /
• v.10 no.3
• /
• pp.7-12
• /
• 2024

The development of artificial intelligence technology changes the social structure and educational environment, and the importance of artificial intelligence capabilities continues to increase. This study was conducted with the purpose of developing a checklist of questions to measure AI capabilities of elementary school students. To achieve the purpose of the study, a Delphi survey was used to analyze literature and develop questions. For literature analysis, two domestic studies, five international studies, and the Ministry of Education's curriculum report were collected through a search. The collected data was analyzed to construct core competency measurement elements. The core competency measurement elements consisted of understanding artificial intelligence (6 elements), artificial intelligence thinking (4 elements), artificial intelligence ethics (4 elements), and artificial intelligence social-emotion (3 elements). Considering the knowledge, skills, and attitudes of the constructed measurement elements, 19 questions were developed. The developed questions were verified through the first Delphi survey, and 7 questions were revised according to the revision opinions. The validity of 19 questions was verified through the second Delphi survey. The checklist items developed in this study are measured by teacher evaluation based on performance and behavioral observations rather than a self-report questionnaire. This has the implication that the measurement results of competency are raised to a reliable level.

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.285-293
• /
• 2021

With the advent of the 4th Industrial Revolution, AI utilization capabilities are being emphasized in various industries, but AI education design and curriculum research as universal education is currently lacking. This study offers a design for universal AI education to further cultivate its use in universities. For the AI basic education design, a questionnaire was conducted for experts three times, and the reliability of the derived design contents was verified by reflecting the results. As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived were data structure understanding and processing, visualization, word cloud, public data utilization, and machine learning concept understanding and utilization. The educational design content derived through this study is expected to increase the value of competency-centered AI universal education in the future.

• Informatization Policy
• /
• v.31 no.2
• /
• pp.39-64
• /
• 2024

This study assesses the national AI competitiveness of 38 OECD countries with focus on AI human capital, AI infrastructure, and AI innovation capacity. Utilizing the fuzzy-set ideal type analysis method, these countries were categorized into eight distinct types based on their national AI competitiveness levels, leading to the derivation of pertinent implications. The analysis identified a category termed "AI Leading Country" consisting of North American, Western European, and Nordic countries, along with several Asian nations including South Korea. Remarkably, the United States demonstrated dominant global national AI competitiveness, achieving the highest fuzzy scores across all three evaluative factors. South Korea was classified as an "AI Leading Country" primarily due to its superior AI infrastructure, but its performance in AI human capital and AI innovation capacity was found to be moderate relative to other analyzed nations; thus highlighting the necessity of sustained focus on the accumulation of AI human capital and bolstering of AI innovation capacity.

• 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.

• Journal of Internet of Things and Convergence
• /
• v.10 no.3
• /
• pp.49-55
• /
• 2024

This study was conducted with the purpose of developing a checklist of questions to measure middle school students' AI capabilities. To achieve the goal of the study, literature analysis and question development Delphi survey were used. For literature analysis, two domestic studies, five international studies, and the Ministry of Education's curriculum report were collected through a search. The collected data was analyzed to construct core competency measurement elements. The core competency measurement elements are understanding of artificial intelligence (5 elements), artificial intelligence thinking (5 elements), utilization of artificial intelligence (4 elements), artificial intelligence ethics (6 elements), and artificial intelligence social-emotion (6 elements). elements). Considering the knowledge, skills, and attitudes of the constructed measurement elements, 31 questions were developed. The developed questions were verified through the first Delphi survey, and 10 questions were revised according to the revision opinions. The validity of 31 questions was verified through the second Delphi survey. The checklist items developed in this study are measured by teacher evaluation based on performance and behavioral observations rather than a self-report questionnaire. This has the implication that the level of reliability of measurement results increases.

• Journal of The Korean Association of Information Education
• /
• v.25 no.2
• /
• pp.423-436
• /
• 2021

In the future, artificial intelligence (AI) technology is expected to become a general-purpose technology (GPT), and it is predicted that AI competency will become an essential competency. Several nations around the world are fostering experts in the field of AI to achieve technological proficiency while working to develop the necessary infrastructure and educational environment. In this study, we investigated the status of software education at the liberal arts level at 31 universities in Seoul, along with precedents from domestic and foreign AI education research. Based on this, we concluded that an AI literacy education model is needed to link software education at the liberal arts level with professional AI education. And we classified 20 AI-related lectures released in the KOCW according to the AI literacy competencies required; based on the results of this classification, we propose a model for AI literacy education in the liberal arts for undergraduate students. The proposed AI literacy education model may be considered as AI·SW convergence to experience AI along with literacy in the humanities, deviating from the existing theoretical and computer-science-based approach. We expect that our proposed AI literacy education model can contribute to the proliferation of AI.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.281-293
• /
• 2022

The necessity for introducing artificial intelligence(AI) into the public sector to form an intelligent government has been emerging. This study set 'Organizational Agility', 'Exploitation & Exploration Learning', and 'E-government Capability' as independent variables for the introduction of AI in central government. Dependent variables were set on whether AI was adopted in the central government organization 'Bu(mainly conducts policy planning)', and 'Cheong(mainly performs policy execution)'. Logistic regression analysis was performed on each of the two models. As a result, it was derived that ministry Bu adopted AI as organizational agility increased, and ministry Chung adopted AI as e-government capability increased. Particularly, it was identified that the effect of exploitation learning for Cheong organizations offset the influence of AI introduction according to e-government capabilities, while exploratory organizational learning facilitated the AI introduction. This study is meaningful for suggesting a strategy for adopting AI in government.

• Informatization Policy
• /
• v.28 no.2
• /
• pp.34-56
• /
• 2021

According to the Fourth Industrial Revolution, demand for and interest in jobs in the field of AI and data science - such as artificial intelligence/data analysts - are increasing. In order to keep pace with this trend, and to supply human resources that can effectively perform such jobs in the relevant fields in a timely manner, job seekers must develop the competencies required by the companies, and universities must be in charge of training. However, it is difficult to devise appropriate response strategies at the level of job seekers, companies and universities, which are stakeholders in terms of supplying suitably competent personnel. Therefore, the purpose of this study is to determine which competencies are required in practice in order to cultivate and supply human talents equipped with the necessary job competencies, and to propose plans for the development of the required competencies at the university level. In order to identify the required competencies in the field of AI and data science, data on job postings on the LinkedIn site, the recruitment platform, were analyzed using text mining techniques. Then, research was conducted with the aim of devising and proposing concrete plans for competency development at the university level by comparing and verifying the results of the international graduate school curriculum in the field of AI and data science, and the interview results with the hiring managers, respectively, with the results of the topic model.