• The Mathematical Education
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• v.63 no.3
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• pp.549-571
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• 2024

As digital·AI-based teaching and learning is emphasized, discussions on the educational use of generative AI are becoming more active. This study analyzed the mathematical performance of ChatGPT 4, Claude 3 Opus, and Gemini Advanced on solving examples and problems from five first-year high school math textbooks. As a result of examining the overall correct answer rate and characteristics of each skill for a total of 1,317 questions, ChatGPT 4 had the highest overall correct answer rate of 0.85, followed by Claude 3 Opus at 0.67, and Gemini Advanced at 0.42. By skills, all three models showed high correct answer rates in 'Find functions' and 'Prove', while relatively low correct answer rates in 'Explain' and 'Draw graphs'. In particular, in 'Count', ChatGPT 4 and Claude 3 Opus had a correct answer rate of 1.00, while Gemini Advanced was low at 0.56. Additionally, all models had difficulty in explaining using Venn diagrams and creating images. Based on the research results, teachers should identify the strengths and limitations of each AI model and use them appropriately in class. This study is significant in that it suggested the possibility of use in actual classes by analyzing the mathematical performance of generative AI. It also provided important implications for redefining the role of teachers in mathematics education in the era of artificial intelligence. Further research is needed to develop a cooperative educational model between generative AI and teachers and to study individualized learning plans using AI.

• Journal of Christian Education in Korea
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• v.79
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• pp.97-120
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• 2024

Purpose of the Study : This study aims to propose alternative church education methodologies utilizing generative AI in the age of artificial intelligence. To achieve this, a project-based church education program using generative AI was developed and applied for a weekend retreat at Y Church's youth ministry located in Seoul. The study involved 18 youth participants and 5 teachers, and was conducted over two weekends, from February 17-18 and February 24-25, 2024, in a non-residential format. Contents and Method : The research methods included developing and applying the generative AI-based Bible education program, then assessing program satisfaction, effectiveness, and the internalization of faith through surveys and interviews with students, teachers, and the overseeing pastor. Satisfaction was measured using pre- and post-program questionnaires, while effectiveness was evaluated through pre- and post-program mind map assessments. To measure the internalization of faith, reflection journals and interviews were conducted. Conlusion : Analysis of the data from the 16 participants who attended both pre- and post-assessments revealed satisfaction with various aspects, including preferences for educational content, the value of educational activities, effort in participating in activities, perceived competence in the activities, preferences for the educators, preferences for the institution, and willingness to recommend the program to peers. The average satisfaction score increased from 11.92 before the program to 27.25 after, showing a significant increase of 15.33, which is statistically significant at the .05 level. Although the changes in faith maturity were not explicitly detailed, a slight change in faith through practical learning was observed. Additionally, the cognitive aspects of the learning content showed longer-lasting effects compared to typical retreats.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.303-323
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• 2023

The purpose of this study is to introduce this language model in the era of generative AI such as ChatGPT, and to provide direction for data literacy education components in libraries using it. To this end, the following three research questions are proposed. First, the technical features of ChatGPT-like language models are examined, and then, it is argued that data literacy education is necessary for the proper and accurate use of information by users using a service platform based on generative AI technology. Finally, for library data literacy education in the ChatGPT era, it is proposed a data literacy education scheme including seven components such as data understanding, data generation, data collection, data verification, data management, data use and sharing, and data ethics. In conclusion, since generative AI technologies such as ChatGPT are expected to have a significant impact on users' information utilization, libraries should think about the advantages, disadvantages, and problems of these technologies first, and use them as a basis for further improving library information services.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.223-230
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• 2024

The purposes of this study is to find out how to use Generative AI by class stage and class model so that classes can be planned using various Generative AI in elementary and secondary education. To this end, contents of using Generative AI according to general instructional stages and instructional models by school level and subject were developed, and revised and supplemented through review by 13 field experts. As for the method of using Generative AI by class stage, general class stages were divided into three stages: 'class preparation', 'in class', and 'class arrangement', and the subject of using Generative AI at each stage, the contents of using it, and the types of Generative AI that can be used are summarized. As a method of using Generative AI according to the class model, eight class contents were developed based on teaching and learning models according to the characteristics of each school level and subject. In order to expand the use of Generative AI in elementary and secondary classes, it is necessary to develop more diverse class contents by school level and subject and distribute them in the field. It is also necessary to develop educational materials on matters to consider when using Generative AI in class.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.191-200
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• 2024

The rapid advancement of computer technology and artificial intelligence has significantly impacted software education in Korea. Consequently, the 2022 revised curriculum demands personalized education. However, implementing personalized education in schools is challenging. This study aims to facilitate personalized education by utilizing incorrect codes and error information submitted by beginners to construct prompts. And the difference in the frequency of correct feedback generated by the generative AI model and the prompts was examined. The results indicated that providing appropriate error information in the prompts yields better performance than relying solely on the excellence of the generative AI model itself. Through this research, we hope to establish a foundation for the realization of personalized education in programming education in Korea.

• Annual Conference of KIPS
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• 2023.11a
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• pp.936-937
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• 2023

본 논문에서는 LLM(Large Language Model) 모델의 fine-tuning 을 통한, 기초 수리 서술형 문항 풀이용 모델 및 Dall-E2 등 이미지 생성형 모델을 활용한 따른 영어 퀴즈풀이용 이미지 생성형 모델을 생성하여, 한국어 기반 LLM 자체 모델 학습 및 교육용 이미지 생성에 대한 방법을 고찰하였다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.31-38
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• 2024

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the "Learning Support" context. However, in other two contexts ("Code Generation" and "Comment Generation"), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the 'system content', responses containing emotional support considering learning emotions were generated.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.231-243
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• 2024

As generative artificial intelligence (AI) services become more diversified and widely used, attempts and discussions on their application in education have become active. The purpose of this study is to investigate and analyze general and work-related perceptions, utilization, and needs regarding generative AI in elementary and secondary education. A survey was conducted among teachers and staff in Chungcheongbuk-do, and 934 responses were analyzed. The main research results are as follows: First, their work-related use of generative AI was lower than their general use, and considering the periodic frequency of more than once a month, the rate was much lower. Second, the main expectation when using generative AI in work appears to be improved work efficiency. Third, regarding the use of generative AI for each task, differences in perception of its usefulness were noticeable depending on position and occupation. They generally responded positively to the usefulness of generative AI in processing documents. To facilitate the use of generative AI for work by elementary and secondary teachers and staff, it is necessary to create an environment that promotes its use while ensuring safety against potential side effects. Additionally, requirements and needs should be considered depending on the position and occupation.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.307-319
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• 2023

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.