• The Mathematical Education
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• v.63 no.2
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• pp.233-254
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• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• Journal of the Korean Chemical Society
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• v.68 no.3
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• pp.160-175
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• 2024

The purpose of this study is to examine the effects of a Chemistry I class based on an artificial intelligence (AI) classification model. To achieve this, the research investigated the development and application of a class utilizing an AI classification model in Chemistry I classes conducted at D High School in Gyeongbuk during the first semester of 2023. After selecting the curriculum content and AI tools, and determining the curriculum-AI integration education model as well as AI hardware and software, we developed detailed activities for the program and applied them in actual classes. Following the implementation of the classes, it was confirmed that students' self-efficacy improved in three aspects: chemistry concept formation, AI value perception, and AI-based maker competency. Specifically, the chemistry classes based on text and image classification models had a positive impact on students' self-efficacy for chemistry concept formation, enhanced students' perception of AI value and interest, and contributed to improving students' AI and physical computing abilities. These results demonstrate the positive impact of the Chemistry I class based on an AI classification model on students, providing evidence of its utility in educational settings.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.33-39
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• 2024

This study examines the changes in learners' positive/negative perceptions of classroom experience and actual utilisation of AI chatbots in response to the recent changes in education trends caused by generative AI. AI chatbots were utilised in web programming classes for six classes of engineering students over two semesters. The learners' experience and usage were analysed from the beginning of the semester through surveys until the submission of midterm and final examination reports. The study's results indicate that the chatbot enhanced learning by providing Q/A feedback and solving practical problems. Additionally, the perception of the chatbot improved from midterm to the end of the course. The study also drew meaningful conclusions about the issue of community disconnection (personalisation) in the classroom and how to use it as educational software. This research is significant for the development of generative AI-based software.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.67-86
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• 2024

This study developed a novel ISO/IEC 25010 quality model for the quality management of artificial intelligence (AI)-based software by using quality characteristics classification card (QCCC) quality models. We used AI models to add, modify, and restructure AI quality attributes for the product quality model and the quality-in-use model of the ISO/IEC 25010 quality model to derive a novel ISO/IEC 25010 quality model. By integrating quality standards derived from various AI-related models, we enhanced the accuracy of the derived model. The product quality model included 10 main quality and 45 subquality attributes, and the quality-in-use model included 10 main quality and 28 subquality attributes. In AI-based models, the quality-in-use model was found to require modifications. The results revealed the direction of improvement of the AI-compatible software quality model and the possibilities for potential standardization and conflict resolution. This study presents the direction for standardization reviews on reorganizing the quality attributes, concepts of attributes, and relationships so that they can be applied to AI software while maintaining the framework of the currently defined software quality model. The results can serve as criteria for the quality management of AI-based software and can also contribute to research on quality models for AI-based software.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.385-393
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• 2024

This study focuses on optimizing the layout of mobile educational content using AI technology, with a particular emphasis on vertical aspect ratio design. Against the backdrop of changing educational content consumption patterns due to the increased mobile device usage and advancements in AI technology, this research analyzes the characteristics and effects of vertical aspect ratio design and explores its potential combination with AI technology. The research methodology combines John Yablonski's UX laws and the concept of human effective field of view with AI technology to analyze the impact of vertical aspect ratio design on the educational content user experience and learning effectiveness. Results show that vertical aspect ratio design effectively focuses users' attention, reduces cognitive load, and contributes to increased learning immersion. Specifically, when combined with AI technology, vertical aspect ratio design proves effective in providing personalized learning experiences, enhancing learning abilities, developing creativity, and optimizing data analysis across various domains. This study is expected to contribute to the qualitative improvement of educational content by emphasizing the importance of vertical aspect ratio design in mobile learning environments and proposing optimization methods using AI technology. Future studies are anticipated to further develop these findings, providing important guidelines for mobile educational content development and the advancement of AI educational technology.

• Convergence Security Journal
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• v.24 no.3
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• pp.33-39
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• 2024

The remarkable advancement of artificial intelligence technology is bringing innovative changes to the field of education. In particular, generative AI models like ChatGPT hold great potential in self-directed programming education due to their natural conversational abilities. This study analyzed the learning effects of using ChatGPT in Scratch classes for non-SW majors. Dividing the classes into those using ChatGPT and those not, and conducting the same evaluations and surveys for the ChatGPT-utilizing group, the results showed that ChatGPT significantly enhanced learning outcomes and the utility of ChatGPT was highly evaluated in advanced learning areas such as understanding Scratch's advanced features and algorithms. This study is significant as it empirically demonstrates the potential of generative AI like ChatGPT as an effective tool in programming education.

• Bulletin of the Korean Chemical Society
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• v.32 no.8
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• pp.2732-2736
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• 2011

In pH 4.2 HAc-NaAc buffer solution, horseradish peroxidase (HRP) catalyzed $H_2O_2$ oxidation of nanosilver to form $Ag^+$. After centrifugation, $Ag^+$ in the supernatant can be measured by graphite furnace atomic absorption spectrophotometry (GFAAS) at the silver absorption wavelength of 328.1 nm. When HRP concentration increased, the $Ag^+$ concentration in the supernatant increased, and the absorption value enhanced. The HRP concentration in the range of 0.84-50 $ng{\cdot}mL^{-1}$ was linear to the enhanced absorption value (${\Delta}A$), with a regression equation of ${\Delta}A$=0.012C+0.11, correlation coefficient of 0.9988, and detection limit of 0.41 $ng{\cdot}mL^{-1}$ HRP. The proposed GFAAS method was used to detect HRP in waste water samples, with satisfactory results.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

• 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.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.403-428
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• 2023

This study investigates the development path and intellectual structure of data literacy research, aiming to identify emerging topics in the field. A comprehensive search for data literacy-related articles on the Web of Science reveals that the field is primarily concentrated in Education & Educational Research and Information Science & Library Science, accounting for nearly 60% of the total. Citation network analysis, employing the PageRank algorithm, identifies key papers with high citation impact across various topics. To accurately trace the development path of data literacy research, an enhanced PageRank main path algorithm is developed, which overcomes the limitations of existing methods confined to the Education & Educational Research field. Keyword bibliographic coupling analysis is employed to unravel the intellectual structure of data literacy research. Utilizing the PNNC algorithm, the detailed structure and clusters of the derived keyword bibliographic coupling network are revealed, including two large clusters, one with two smaller clusters and the other with five smaller clusters. The growth index and mean publishing year of each keyword and cluster are measured to pinpoint emerging topics. The analysis highlights the emergence of critical data literacy for social justice in higher education amidst the ongoing pandemic and the rise of AI chatbots. The enhanced PageRank main path algorithm, developed in this study, demonstrates its effectiveness in identifying parallel research streams developing across different fields.