• 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.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.34-41
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• 2021

The purpose of this study is to explore any effects of primary English language learners' affective factors and satisfaction through AI-based speaking activity. In order to answer these questions, a total number of 46 ELLs from a public elementary school participated in this research. Survey questionnaire on affective factors and learning satisfaction were distributed and the results were analyzed quantitatively. The findings are as follows. First, participants could expand their knowledge on AI-based activity towards its educational advantages and capability. Second, overall affective factors of the participants on AI-based activity changed positively, with the improvement of the mean score. The paired samples t-test showed that there was a significant difference among interest, value and attitude. Third, the satisfaction degree on AI-based learning escalated, particularly in the sense of efficacy, academic achievement and involvement. Lastly, it was revealed that the satisfaction degree was correlated with learners' self-confidence, interest and attitude.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.219-221
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• 2022

초등학교 인공지능(Artificial Intelligence, AI) 교육은 학교급별 특성과 수준을 고려하여 놀이 및 체험 활동 중심으로 계획되고 있다. 그러나 교육 현장의 수요 및 AI 리터러시 연구에서 AI 개념의 지도 필요성이 제시되고 있다. 초등학생에게 어렵고 생소한 AI 개념을 교육하기 위해 학습자의 발달 특성을 고려한 교수학습 전략이 필요하다. 선행조직자는 개념 지도 시 학습자의 인지적 부하를 줄일 수 있는 효과적인 교수학습 전략 중 하나로 이미 초등학생을 위한 인공지능 교재에 널리 사용되고 있다. 그러나 교재 분석 결과 선행조직자는 학생별 경험과 양육환경의 차이로 인해 선행조직자로서 기능하지 못할 가능성이 있다. 이를 해결하기 위해 본 연구는 초등학교에 널리 활용될 수 있는 선행조직자를 초등 교육과정에서 추출하여 AI 교육 프로그램을 개발하였다. 본 프로그램은 초등학교 5~6학년 AI 교육 내용 기준에서 AI 개념 요소를 추출하여 초등학교 1~4학년 교과 교육과정에서 선행조직자를 선정하였고 4차시의 교육 프로그램을 개발하였다. 본 연구를 통해 개발된 프로그램이 초등학생의 효과적인 AI 개념을 학습과 AI 리터러시 향상에 도움이 될 것으로 기대된다.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.59-69
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• 2020

The purpose of this study is to design the framework of evaluation on learner's cognitive skill for artificial intelligence(AI) education through computational thinking. To design the rubric and framework for evaluating the change of leaner's intrinsic thinking, the evaluation process was consisted of a sequential stage with a) agency that cognitive learning assistance for data collection, b) abstraction that recognizes the pattern of data and performs the categorization process by decomposing the characteristics of collected data, and c) modeling that constructing algorithms based on refined data through abstraction. The evaluating framework was designed for not only the cognitive domain of learners' perceptions, learning, behaviors, and outcomes but also the areas of knowledge, competencies, and attitudes about the problem-solving process and results of learners to evaluate the changes of inherent cognitive learning about AI education. The results of the research are meaningful in that the evaluating framework for AI education was developed for the development of individualized evaluation tools according to the context of teaching and learning, and it could be used as a standard in various areas of AI education in the future.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.449-457
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• 2021

Ministry of education recently announced to implement AI curriculum in elementary, middle school and highschool from 2025 which will include programing, basic AI principal and AI Ethics, and the media is releasing articles that have reservations on it. This study is focused on analyzing the perspective of elementary teachers - who are going to be in charge of AI education - on the implementation of AI education in elementary schools and the teachers are divided into two groups of 'software-experienced' and 'software-inexperienced' in relation to software training background. The results showed that 100% of the 'software-experienced' teachers agreed on implementing AI education and 80% of 'software-inexperienced' teachers also showed positive perspective on it. Among the reasons that 20% of 'software-inexperienced' teachers had negative perspective on AI education, it was highly rated that existing home economics subject covers fulfilling amount of software education. Both 'software-experienced' and 'software-inexperienced' teachers chose grade 5 and 6 as the most appropriate age for software education and considered one class per a week as the most appropriate amount of AI class. In terms of the subject format, 75% of the 'software-experienced' teachers chose the idea that software education has to be an independent school subject which will include AI education. Also, 54% of the 'software-inexperienced' teachers chose the ideas either AI education should be an independent subject or software education should be an independent subject which will include AI education. The preference of the content of AI education appeared in order of basic AI programing, principles of AI and AI Ethics.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.261-279
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• 2024

The purpose of this study is to examine the characteristics of AI courseware for mathematics learning based on Choppin et al.'s (2014) digital typology and to derive implications for directions for AI courseware development. For this purpose, 12 types of AI courseware actively used in domestic were selected for analysis, and the characteristics of these AI courseware in terms of program-student interaction, teacher' s lesson design, and evaluation system were analyzed. As a result, each AI courseware provided unique functional features for students, teachers, and evaluation, but the ability to modify and configure teaching and learning was limited. Based on these results, implications for the direction of development of AI courseware in mathematics education were presented.

• Science of Emotion and Sensibility
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• v.27 no.2
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• pp.69-80
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• 2024

The COVID-19 pandemic highlighted the importance of remote education, yet the adoption rate of AI in the educational sector remains relatively low, and studies into learners' familiarity with using AI learning robots are scarce. In response, this study analyzes the factors influencing users' familiarity with AI learning robots in a smart learning environment tailored to the untact era. To this end, social big data analysis was used to examine changes in public perception and the frequency of mentions of smart learning and AI learning robots. The results showed that positive perceptions of smart learning significantly outweigh negative ones, reflecting the convenience and improved accessibility that technology brings to education. However, there is also a considerable negative perception attached to smartphone use, which is interpreted as reflecting concerns that smartphones may disrupt learning and bring other negative aspects of technology dependence. These results indicate mixed social concerns and expectations regarding the educational use of smart learning and AI technologies. The effective introduction and use of AI learning robots, especially in smart learning environments, necessitate considering these social perceptions. This study provides foundational data for the effective implementation and use of AI learning robots in smart learning environments and suggests the need for approaches that primarily consider users' familiarity and social perceptions in the development of educational technologies.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.733-740
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• 2021

The purpose of this study is to present the direction of elementary school AI education by analyzing cases of classes related to AI education in actual school settings. For this purpose, 19 classes were collected as elementary school class cases based on AI education. According to the result of analyzing the class case, it was confirmed that the class was designed in a hybrid aspect of learning content and method using AI. As a result of analyzing the achievement standards and learning goals, action verbs related to memory, understanding, and application were found in 8 classes using AI from a tool perspective. When class was divided into introduction, development, and rearrangement stages, the AI education element appeared the most in the development stage. On the other hand, when looking at the ratio of learning content and learning method of AI education elements in the development stage, the learning time for approaching AI education as a learning method was overwhelmingly high. Based on this, the following implications were derived. First, when designing the curriculum for schools and grades, it should be designed to comprehensively deal with AI as a learning content and method. Second, to supplement the understanding of AI, in the short term, it is necessary to secure the number of hours in practical subjects or creative experience activities, and in the long term, it is necessary to secure information subjects.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.377-383
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• 2021

The purpose of this study is to present the direction of elementary school AI education by analyzing cases of classes related to AI education in actual school settings. For this purpose, 19 classes were collected as elementary school class cases based on AI education. According to the result of analyzing the class case, it was confirmed that the class was designed in a hybrid aspect of learning content and method using AI. As a result of analyzing the achievement standards and learning goals, action verbs related to memory, understanding, and application were found in 8 classes using AI from a tool perspective. When class was divided into introduction, development, and rearrangement stages, the AI education element appeared the most in the development stage. On the other hand, when looking at the ratio of learning content and learning method of AI education elements in the development stage, the learning time for approaching AI education as a learning method was overwhelmingly high. Based on this, the following implications were derived. First, when designing the curriculum for schools and grades, it should be designed to comprehensively deal with AI as a learning content and method. Second, to supplement the understanding of AI, in the short term, it is necessary to secure the number of hours in practical subjects or creative experience activities, and in the long term, it is necessary to secure information subjects.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.