• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.589-595
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• 2023

Artificial intelligence(AI) is spurring advancements in EdTech, the merger of technology and education. This includes the creation of effective learning materials and personalized student experiences. Our study focuses on developing a programming education software that employs state-of-the-art generative AI. Our software also includes prompts optimized for programming code analysis, which are based on the well-known ChatGPT API. Furthermore, the necessary functions for acquiring programming skills were created with a user interface and developed as a question-and-answer template function based on an AI chatbot. The objective of this study is to guide the development of educational programmes that make use of generative AI.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.225-234
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• 2020

Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

• 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.

• 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.5 no.4
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• pp.125-134
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• 2004

Recently a lot of researches on the component-based software product lines and on applying generative programming into software product lines are being performed actively. This paper proposes an automatic component reconfiguration tool that could be applied in constructing the component-based software product lines. Our tool accepts the reuser's requirement via a feature model which is the main result of the domain engineering, and makes the feature configuration from this requirement. Then it generates the source code of the reconfigured component according to this feature configuration. To accomplish this process, the component family in our tool should have the architecture of GenVoca that is one of the most influential generative programming approaches. In addition, XSLT scripts provide the code templates for implementation elements which are the ingredients of the target component. Taking the ‘Bank Account' component family as our example, we showed that our component reconfiguration tool produced automatically the component source code that the reuser wants to create. The result of this paper would be applied extensively for creasing the productivity of building the software product lines.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.275-282
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• 2024

This study studied a teaching model for software education using generative AI. The purpose of the study is to use ChatGPT as an instructor's assistant in programming classes for non-major students by using ChatGPT in software education. In addition, we designed ChatGPT to enable individual learning for learners and provide immediate feedback when students need it. The research method was conducted using ChatGPT as an assistant for non-computer majors taking a liberal arts Python class. In addition, we confirmed whether ChatGPT has the potential as an assistant in programming education for non-major students. Students actively used ChatGPT for writing assignments, correcting errors, writing coding, and acquiring knowledge, and confirmed various advantages, such as being able to focus on understanding the program rather than spending a lot of time resolving errors. We were able to see the potential for ChatGPT to increase students' learning efficiency, and we were able to see that more research is needed on its use in education. In the future, research will be conducted on the development, supplementation, and evaluation methods of educational models using ChatGPT.

• 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.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.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.44-51
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• 2019

Designing sophisticate ship structures that satisfy several design criteria simultaneously with minimum weight and cost is an important engineering issue. For a ship structure composed of a shell and stiffeners, this issue is more serious because their mutual effect has to be addressed. In this study, a two-stage optimization method is proposed for the conceptual design of stiffeners in a ship's prow. In the first stage, a topology optimization method is used to determine a potential stiffener distribution based on the optimal results, whereupon stiffeners are constructed according to stiffener generative theory and the material distribution. In the second stage, size optimization is conducted to optimize the plate and stiffener sections simultaneously based on a parametric model. A final analysis model of the ship-prow structure is presented to assess the validity of this method. The analysis results show that the two-stage optimization method is effective for stiffener conceptual design, which provides a reference for designing actual stiffeners for ship hulls.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.6
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• pp.1137-1156
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• 2023

With the advent of artificial intelligence (AI) during the Fourth Industrial Revolution, the fashion industry has simplified the production process and overcome the technical difficulties of design. This study anticipates likely changes in the digital age and develops a model that will allow consumers to design textile patterns using AI technology. Previous studies and industrial examples of AI technology's use in the textile design industry were investigated, and a textile pattern was developed using an AI algorithm. A new textile design model was then proposed based on its application to both virtual and physical clothing. Inspired by traditional Korean masks and props, AI technology was used to input color data from open application programming interface images. By inserting these into various repeating structures, a textile design was developed and simulated as garments for both virtual and real garments. We expect that this study will establish a new textile design development method for Generation Z, who favor customized designs. This study can inform the use of personalization in generative textile design as well as the systemization of technology-driven methods for customized and participatory textile design.