• 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.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.779-790
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• 2021

This study analyzed the effect of AI maker coding education on improving students' computational thinking. The subjects of the study were 10 students at H Elementary School in Ansan, and a total of 8 AI maker coding education using the Instructional Model for Maker Education based on SW Coding was applied to students to find out the degree of improvement of computational thinking. Students who participated in the class performed a process of solving real-life problems through coding and making activities, measured the degree of improvement in computing thinking before and after education through a computing thinking test paper, and observed students' thinking processes related to computing thinking components through interviews. As a result, it was confirmed that the average score of all students' computational thinking skills was improved, and the deviation of scores between students decreased. Through the interview, it was found that students actively utilize their thinking skills related to computational thinking skills in the problem-solving process. Through this, it was confirmed that AI maker coding education can have a positive effect on improving students' computing thinking skills.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.723-731
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• 2021

In this study, the AI education program for elementary school students was developed and applied by introducing the design thinking process, which is attracting attention as a creative problem solving process. A design thinking-based AI education program was developed in the stages of Understanding AI, Identifying sympathetic problems, Problem definition, Ideate, Prototype, Test and sharing, and the development program was applied to elementary school students in 4th-6th grade. As a result of pre- and post-testing of students' computational thinking skills to confirm the effectiveness of the program, computational thinking skills increased by grade level, and students experienced a process of collaboration for creative problem solving based on insights gained from sympathetic problem finding. In addition, it was possible to get a glimpse of the attitude of using AI technology to solve problems, and it was confirmed that ideas were generated in the prototype stage and developed through communication between team members. Through this, the design thinking-based AI education program as one of the AI education for elementary school students guarantees the continuity of learning and confirms the possibility of providing an experience of the creative problem-solving process.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.927-934
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• 2021

AI(Artificial intelligence) is gradually taking up a large part of our lives, and the pace of AI development is accelerating. It is called ACT that develop students' computational thinking in the way artificial intelligence learns. Among ACTs, pattern recognition is an essential factor in efficiently solving problems. Pattern analysis is part of the pattern recognition process. In fact, Netflix's personalized movie recommendation service and what it named Covid-19 after repeated symptoms are all the results of pattern analysis. While the importance of ACT, including pattern recognition, is highlighted, software education for kindergarten and elementary school lower grades is much insufficient compared to foreign countries. Therefore, this study aims to design and develop textbooks for the development of artificial intelligence-based computational thinking through pattern analysis for kindergarten students.

• 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 Industrial Convergence
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• v.20 no.2
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• pp.29-36
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• 2022

Computational thinking in the AI and Big Data era for digital society means a series of problem-solving methods that involve expressing problems and their solutions in ways that computers can execute. Computational thinking is an approach to solving problems, designing systems, and understanding human behavior by deriving basic concepts in computer science, and solving difficult problems and elusive puzzles for students. We recently studied 93 pre-service teachers who are currently a freshman at ◯◯ university. The results of the first semester class, the participants created a satisfactory algorithm of the video level. Also, the proposed model was found to contribute greatly to the understanding of the computational thinking of the students participating in the class.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.253-259
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• 2021

In line with the modern society where artificial intelligence has spread throughout society, the Ministry of Education decided to provide AI education in kindergartens, elementary, middle and high school classes in 2025, and develop related learning materials and textbooks from 2021. Korea currently does not have state-led AI education for kindergarten and elementary school, so there is no systematic teaching material. Therefore, this study designed and presented textbooks for pattern analysis-based computational thinking development based on the GPS curriculum, a kindergarten SW curriculum studied by Y. S. Jeong and S. E. Lim (2020). Class procedures for using the textbooks were classified as introduction activities, development activities, and organization activities. Supplementary explanations were presented by presenting textbooks and teaching aids along with explanations of each activity. In order for this study to help AI education conducted in 2025, research must be conducted to demonstrate its effectiveness through actual application in the future.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.413-421
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• 2020

AI(Artificial intelligence) has brought many changes to our living in the Fourth Industrial Revolution. In our daily lives, we can easily access unmanned automatic systems using AI such as unmanned cameras and unmanned delivery boxes. Therefore, AI education has become an important part of daily life in the future. Currently, however, we have very few cases of AI education for young students, such as kindergarten and lower grades in elementary schools. Based on the software education curriculum of kindergarteners and lower graders previously studied, we presented the examples of AI-based teaching and learning activities and presented related AI-based computational thinking by each topic. However, in order for these teaching and learning activities to be applied to public education, it is necessary to secure sufficient class time, improve the educational environment, and actively support teaching activities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.188-190
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• 2022

We propose how to configure the AI education system based on the purpose of hybrid learning and the teaching-learning principle. Based on the four components of hybrid learning, we have designed the system conceptual diagram and DB configuration diagram for on-line and offline learning environments for effective AI education. The proposed AI education system model in this paper is expected to be a foundation for maximizing the effectiveness of AI education according to the level and needs of learners and building a more effective learner-centered learning environment in cultivating computational thinking in AI education.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.917-925
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• 2021

This study aims to specify a constructivism-based instructional method using a modular data analysis tool. The value and meaning of a modular data analysis tool have been examined to be applied in the national curriculum for artificial intelligence education and the process of cultivating problem-solving ability based on computational thinking. The modular data analysis tool visually expresses the cognitive thinking process that forms the schema in equilibrating through assimilation and adjustment. Artificial intelligence education has features that embody abstract knowledge and structure the data analysis module through the represented schema as a BlackBox implemented as an algorithm. Therefore, the value of the modular data analysis tool could be examined because it has the advantage of connecting the conceptual and implicit schema.