• Journal of the Korean School Mathematics Society
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• v.23 no.2
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• pp.223-237
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• 2020

Artificial intelligence technology, which represents the era of the 4th Industrial Revolution, is now deeply involved in our lives, and future education places great emphasis on building students' capabilities for the principles and uses of artificial intelligence. Therefore, the purpose of this study is to develop the contents of AI related education in mathematics, which the relationship is closely connected to each other. To this end, I propose establishing two novel AI-related contents in mathematics education. One subject is related to learning the principle of machine learning based on mathematics foundation. In addition, I draw the core math contents dealt in following subject called 'Basic Mathematics for AI and Data Science.'

• Journal of the Korean School Mathematics Society
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• v.24 no.4
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• pp.391-405
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• 2021

In this study, 'data collection', 'data expression', 'data analysis, and 'optimization and decision-making' were selected as the core AI concepts to be dealt with in the mathematics for AI education. Based on this, the degree of reflection of AI core concepts was investigated and analyzed compared to the mathematical core concepts and content of each area of the elective course. In addition, the appropriateness of the content of was examined with a focus on core concepts and related learning contents. The results provided some suggestions for answering the following four critical questions. First, How to set the learning path for ? Second, is it necessary to discuss the redefinition of the nature of ? Third, is it appropriate to select core concepts and terms for ? Last, is it appropriate to present the relevant learning contents of the content system of ?

• Education of Primary School Mathematics
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• v.27 no.1
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• pp.19-38
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• 2024

This study aims to analyze the characteristics of students' understanding of artificial intelligence and mathematical concepts by developing and applying an artificial intelligence education program for mathematics convergence using robots. To this end, we analyzed the content standards of elementary artificial intelligence education to extract conceptual elements of artificial intelligence and identified mathematics achievement standards that can effectively integrate them. In particular, a five-session (15 classes in total) program was developed by selecting the units 'angle' and 'quadrilateral' suitable for utilizing the robot's movement and reorganizing the lesson to integrate the mathematics achievement standard with the artificial intelligence content elements. As a result of applying this to 22 fourth grade elementary school students over five months and analyzing the students' understanding revealed by topic of artificial intelligence content, the artificial intelligence education program for mathematics convergence using robots was helpful in students' understanding artificial intelligence principles and mathematical concepts. In addition, the use of robots was confirmed to improve students' understanding of artificial intelligence and mathematics as well as their participation in class by making them visually check a series of performing procedures.

• Communications of Mathematical Education
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• v.37 no.3
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• pp.443-465
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• 2023

This study examines the content of vectors and matrices in Artificial Intelligence Mathematics textbooks (AIMTs) from the 2015 revised mathematics curriculum. We analyzed the implementation of foundational mathematical concepts, specifically definitions and related sub-concepts of vectors and matrices, in these textbooks, given their importance for understanding AI. The findings reveal significant variations in the presentation of vector-related concepts, definitions, sub-concepts, and levels of contextual information and descriptions such as vector size, distance between vectors, and mathematical interpretation. While there are few discrepancies in the presentation of fundamental matrix concepts, differences emerge in the subtypes of matrices used and the matrix operations applied in image data processing across textbooks. There is also variation in how textbooks emphasize the interconnectedness of mathematics for explaining vector-related concepts versus the textbooks place more emphasis on AI-related knowledge than on mathematical concepts and principles. The implications for future curriculum development and textbook design are discussed, providing insights into improving AI mathematics education.

• The Mathematical Education
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• v.63 no.2
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• pp.319-334
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• 2024

The purpose of this study was to reveal the discursive constructs of AI-based mathematical objects by analyzing how concrete objects used in the optimization content of AI mathematics textbooks are transformed into discursive objects through naming and discursive operation. For this purpose, we extracted concrete objects used in the optimization contents of five high school AI mathematics textbooks and developed a framework for analyzing the discursive constructs and discursive operations of AI-based mathematical objects that can analyze discursive objects. The results of the study showed that there are a total of 15 concrete objects used in the loss function and gradient descent sections of the optimization content, and one concrete object that emerges as an abstract d-object through naming and discursive operation. The findings of this study are not only significant in that they flesh out the discursive construction of AI-based mathematical objects in terms of the written curriculum and provide practical suggestions for students to develop AI-based mathematical discourse in an exploratory way, but also provide implications for the development of effective discursive construction processes and curricula for AI-based mathematical objects.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.159-181
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• 2023

The purpose of this study is to explore secondary mathematics teachers' perceptions on Artificial Intelligence (AI). For this purpose, we conducted three focus group interviews with 18 secondary in-service mathematics teachers and analyzed their perceptions on AI for math and math for AI. The secondary in-service mathematics teachers perceive that AI allows to implement different types of mathematics instruction but has limitations in exploring students' mathematical thinking and having emotional interactions with students. They also perceive that AI makes it easy to develop assessment items for teachers but teachers' interventions are needed for grading essay-type assessment items. Lastly, the secondary in-service mathematics teachers agree the rationale of adopting the subject ＜Artificial Intelligence Mathematics＞ and its needs for students, but they perceive that they are not well prepared yet to teach the subject and do not have sufficient resources for teaching the subject and assessing students' understanding about the subject. The findings provide implications and insights for developing individualized AI learning tools for students in the secondary level, providing AI assessment tools for teachers, and offering professional development programs for teachers to increase their understanding about the subject.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.277-297
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• 2023

In mathematics, the concept of convolution is widely used. The convolution operation is required for understanding computer vision and deep learning in artificial intelligence. Therefore, it is vital for this concept to be explained in college mathematics education. In this paper, we present our new teaching and learning materials on convolution available for engineering mathematics. We provide the knowledge and applications on convolution with Python-based code, and introduce Convolutional Neural Network (CNN) used for image classification as an example. These materials can be utilized in class for the teaching of convolution and help students have a good understanding of the related knowledge in artificial intelligence.

• The Mathematical Education
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• v.62 no.3
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• pp.303-326
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• 2023

Mathematics is a discipline with a strong systemic structure, and learning deficits in previous stages have a great influence on the next stages of learning. Therefore, it is necessary to frequently check whether students have learned well and to provide immediate feedback, and for this purpose, intelligent tutoring system(ITS) can be used in math education. For this reason, it is necessary to reveal how the intelligent tutoring system is effective in personalized adaptive learning. The purpose of this study is to investigate the functions and applications of intelligent tutoring system for personalized adaptive learning in mathematics. To achieve this goal, literature reviews and surveys with students were applied to derive implications. Based on the literature reviews, the functions of intelligent tutoring system for personalized adaptive learning were derived. They can be broadly divided into diagnosis and evaluation, analysis and prediction, and feedback and content delivery. The learning and lesson plans were designed by them and it was applied to fifth graders in elementary school for about three months. As a result of this study, intelligent tutoring system was mostly supporting personalized adaptive learning in mathematics in several ways. Also, the researcher suggested that more sophisticated materials and technologies should be developed for effective personalized adaptive learning in mathematics by using intelligent tutoring system.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.445-473
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• 2021

The purpose of this study is to derive implications for the design of the next curriculum by analyzing the textbooks designed as a new subject in the 2015 revised curriculum. In the mathematics curriculum documents of , 'related learning elements' are presented instead of 'learning elements'. 'Related learning elements' are defined as mathematical concepts or principles that can be used in the context of artificial intelligence, but there are no specific restrictions on the amount and scope of dealing with 'related learning elements'. Accordingly, the aspects of 'related learning elements' reflected in the textbooks were analyzed focusing on the textbook format, the amount and scope of contents, and the ways of using technological tools. There were differences in the format of describing 'related learning elements' in the textbook by textbook and the amount and scope of handling mathematics concepts. Although similar technological tools were dealt with in each textbook so that 'related learning elements' could be used in the context of artificial intelligence, the focus was on computations and interpretation of results. In order to fully reflect the intention of the curriculum in textbooks, a systematic discussion on 'related learning elements' will be necessary. Additionally, in order for students to experience the use of mathematics in artificial intelligence, substantialized activities that can set and solve problems using technological tools should be included in textbooks.