• Journal of Industrial Convergence
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• v.20 no.5
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• pp.39-59
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• 2022

We analyze the perceptions and requirements of early childhood teachers for artificial intelligence(AI) education to develop an AI education program for 5-year-olds. As for the research methodology, we conducted a survey and an in-depth interview to extract the AI educational elements centering on the analysis stage, the first stage of the ADDIE model. The research result is that first, it is necessary to design a curriculum that combines the contents of early childhood education and AI education to be naturally accepted as AI education for 5-year-olds. Second, an evaluation tool for AI education that can showcase the teacher's reflection should be developed systematically. Third, it is necessary to support a play-centered AI education support and environment for early childhood teachers. Lastly, it is essential to establish a system that can be continuously operated in the field of early childhood education in consideration of AI education in the non-curricular curriculum. It is expected that in the future, a play-oriented AI education program for 5-year-olds will be developed to spread awareness of AI education for infants and present an AI education approach for each age and stage of learners.

• Korean Journal of Childcare and Education
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• v.19 no.2
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• pp.61-85
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• 2023

Objective: This study aims to investigate whether science teaching attitude of early childhood teachers mediates the relationship between the professional learning environment of institutions and their intention to accept artificial intelligence (AI) technology, and whether the experience of using smart and digital devices moderates the effect of science teaching attitude. Methods: An online survey was conducted targeting 118 teachers with more than 1 year of experience in kindergarten and day care center settings. Descriptive statistical analysis, correlation analysis, and The Process macro model 4, 14 were performed using SPSS 27.0 and The Process macro 3.5. Results: First, the science teaching attitude of early childhood teachers served as a mediator between the professional learning environment of institutions and teachers' intention to accept AI technology. Second, the experience of using smart and digital devices was found to moderate the effect of teachers' science teaching attitude on their intention to accept AI technology. Conclusion/Implications: This results showed that an institutional environment that supports teachers' professionalism development and provides rich experience is crucial for promoting teachers' active acceptance of AI technology. The findings highlight the importance of creating a supportive institutional envionment for teacher's professional growth, enhancing science teaching attitudes, and facilitating the use of various devices.

• Journal of Christian Education in Korea
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• v.72
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• pp.183-203
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• 2022

The purpose of this study is to construct factors of AI education utilization competency. AI education utilization competency is used as basic data for education to enhance the AI education competency of pre-service early childhood teachers. To this end, 7 studies related to competency factors and models were selected by searching for previous studies. Seven preceding studies were analyzed. As a result, 18 competency factors were extracted, including understanding of artificial intelligence. The extracted competency elements were divided into six areas, which are divided into understanding subject knowledge through coding, class preparation, class management, class result feedback, class guidance, and self-development. And 15 factors were constructed. The draft formed through coding was improved through review by three early childhood education experts. Factors improved through expert review were structured by classifying them into knowledge, skills, and attitudes to organize the curriculum. The validity of the structured competency factor was verified through expert Delphi. As a result of the Delphi verification, all factors were converged in the first survey. Through this, 6 competency areas, 11 competency factors, and 19 competency factors were composed of knowledge, 10 skills, and 5 attitudes. The implication is that the competency factors presented as a result of this study can be used as basic data for organizing a curriculum to improve the ability of pre-service early childhood teachers to use artificial intelligence education.