• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.33-40
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• 2024

The Ministry of Education announced the main points of the 2022 revised curriculum on November 24, 2021. Democratic citizenship education to foster citizenship appears as one of the detailed tasks among the key tasks of the 2022 revised curriculum. We are promoting democratic citizenship education to foster citizenship. Therefore, what does democratic citizenship education specifically mean and what does it consist of? There is a need to look into what methods this should be used for. The purpose of this study is to explore the meaning of democratic citizenship education revealed in the 2022 revised curriculum. The contents of democratic citizenship education for the cultivation of citizenship revealed in the general discussion of the 2022 revised curriculum are analyzed as follows. First, it means education related to democracy and social issues. The specific contents of democracy and social issues are ① peace, ② human rights, ③ gender equality, and ④ cultural diversity. Second, critical thinking education. Third, media literacy education is necessary because democratic citizenship education must respond appropriately to the times in line with social changes such as the emergence of Chat GPT. Fourth, while emphasizing democratic decision-making education, it includes social empathy and communication education. Fifth, it contains local and national community participation and practical education as a method for citizen participation and practice. As described above, democratic citizenship education was specified in the general introduction of the 2022 revised curriculum. In order to carry out such democratic citizenship education systematically, it is necessary to establish the principles of democratic citizenship education.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.3
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• pp.234-246
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• 2023

Purpose: This study aimed to develop a case-based nursing education program using generative artificial intelligence and to assess its usability and applicability in nursing curriculums. Methods: The program was developed by following the five steps of the ADDIE model: analysis, design, development, implementation, and evaluation. A panel of five nursing professors served as experts to implement and evaluate the program. Results: Utilizing ChatGPT, six program modules were designed and developed based on experiential learning theory. The experts' evaluations confirmed that the program was suitable for case-based learning, highly usable, and applicable to nursing education. Conclusion: Generative artificial intelligence was identified as a valuable tool for enhancing the effectiveness of case-based learning. This study provides insights and future directions for integrating generative artificial intelligence into nursing education. Further research should be attempted to implement and evaluate this program with nursing students.

• The Mathematical Education
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• v.63 no.2
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• pp.387-392
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• 2024

The use of artificial intelligence (AI) in mathematics education has advanced as a means for promoting understanding of mathematical concepts, academic achievement, computational thinking, and problem-solving. From a total of 13 studies in this special issue, this editorial reveals threads of potential and future directions to advance mathematics education with the integration of AI. We generated five themes as follows: (1) using ChatGPT for learning mathematical content, (2) automated grading systems, (3) statistical literacy and computational thinking, (4) integration of AI and digital technology into mathematics lessons and resources, and (5) teachers' perceptions of AI education. These themes elaborate on the benefits and opportunities of integrating AI in teaching and learning mathematics. In addition, the themes suggest practical implementations of AI for developing students' computational thinking and teachers' expertise.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.369-374
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• 2024

This study aims to improve the accessibility and efficiency of sign language education for both hearing impaired and non-deaf people. To this end, we developed VR realistic sign language education content that integrates hand tracking technology and conversational AI. Through this content, users can learn sign language in real time and experience direct communication in a virtual environment. As a result of the study, it was confirmed that this integrated approach significantly improves immersion in sign language learning and contributes to lowering the barriers to sign language learning by providing learners with a deeper understanding. This presents a new paradigm for sign language education and shows how technology can change the accessibility and effectiveness of education.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.591-614
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• 2023

For a long time, mathematics education institutions such as NCTM(National Council of Teachers of Mathematics) have emphasized the essential role of writing, and recent surveys by the Ministry of Education report a decline in foundational academic skills in the post-COVID19 period. The purpose of this study is to redefine the significance of mathematics writing in mathematics education, focusing on competencies highlighted in the field, particularly in the areas of problem-solving, communication, and reasoning. The research findings indicate that writing in problem-solving enhances cognitive organization, fostering the ability to grasp concepts and methods. Writing in communication builds confidence through the meta-cognitive process, and writing in inference allows self-awareness of step-by-step identification of areas lacking understanding. Particularly in the future society where artificial intelligence(AI) is utilized, changes in the learning environment necessitate research for the establishment of authenticity judgment through writing and the cultivation of a proper writing culture.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.411-416
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• 2023

The purpose of this study is to investigate the utilization of major learning tools among radiology science students and assess the accuracy of a conversational artificial intelligence service program, specifically a chatbot, in the context of the national radiologic technologist licensing exam. The survey revealed that 84.3% of radiology science students actively utilize electronic devices during their learning process. In addition, 104 out of 140 respondents said they use search engines as a top priority for efficient data collection while studying. When asked about their awareness of chatbots, 80% of participants responded affirmatively, and 22.9% reported having used chatbots for academic purposes at least once. From 2018 to 2022, exam questions from the first and second periods were presented to the chatbot for answers. The results showed that ChatGPT's accuracy in answering first period questions increased from 48.28% to 60%, while for second period questions, it increased from 50% to 62.22%. Bing's accuracy in answering first period questions improved from 55% to 64.55%, and for second period questions, it increased from 48% to 52.22%. The study confirmed the general trend of radiology science students utilizing electronic devices for learning and obtaining information through the internet. However, conversational artificial intelligence service programs in the field of radiation science face challenges related to accuracy and reliability, and providing perfect solutions remains difficult, highlighting the need for continuous development and improvement.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.79-86
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• 2024

In this paper, we propose a virtual learning platform based on various interactions that occur during real class activities, rather than the existing content delivery-oriented learning metaverse platform. In this study, we provide a learning environment that combines AI and a virtual environment to solve problems by talking to real-time AI. Also, we applied G-learning techinques to improve class immersion. The Virtual Edu platform developed through this study provides an effective learning experience combining self-directed learning, simulation of interest through games, and PBL teaching method. And we propose a new educational method that improves student participation learning effectiveness. Experiment, we test performance on learninng activity based on real-time video classroom. As a result, it was found that the class progressing stably.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.