The purpose of this study is to develop and implement a customized AI-based speaking diagnosis, learning, and assessment system, SpeakMaster, in order to overcome the lack of systematic evaluation and practice opportunities in school English speaking class. This system integrates automated speaking scoring to provide students with feedback on their speaking abilities across pronunciation, conversation, and presentation. This study adopts a design-based research methodology, demonstrating the development and implementation process. 1,451 students and eight teachers in elementary, middle, and high schools participated in the experiment. Data were collected through learning logs, teacher journals, interviews, and post-surveys. The findings indicate that the system design is appropriate for English class, promoting students’ flow in engaging speaking practice. Students showed motivation and satisfaction while teachers found the system valuable for monitoring student progress and facilitating speaking assessments. Despite the challenges of improving chatbot performance and enhancing scoring reliability, the results suggest that SpeakMaster shows potential to enhance English speaking education.

While the adoption of AI-based design tools is accelerating in design education, limited research has examined learners’ psychological acceptance of these tools. This study therefore investigates perceptions of CLO 3D, Stable diffusion, and ChatGPT through the Technology Acceptance Model (TAM). Survey data were collected from 70 design majors at a university in Seoul and analyzed using regression methods, focusing on four key variables: perceived learning difficulty, efficiency, visual satisfaction, and commercialization potential. The results revealed paradoxical patterns in learning experience, where higher learning intention and perceived intuitiveness sometimes increased learning burden, while efficiency and output similarity reduced it. Efficiency perceptions were strengthened by learning intention, CLO 3D output similarity, and ChatGPT’s visualization support, but weakened when learners relied heavily on traditional creativity or when Stable diffusion’s creativity reflection was emphasized. Visual satisfaction was positively influenced by portfolio development and practical application intentions yet decreased when judged strictly by conventional creativity standards. Commercialization potential increased with efficiency, time savings, ChatGPT utilization, and application planning, but declined with greater familiarity with hand sketching. These findings validate TAM’s dimensions of usefulness and ease of use while highlighting the moderating role of comparison with traditional workflows. The study contributes theoretically by extending TAM to creative education contexts and provides practical guidance for developing instructional strategies that balance efficiency, creativity, and professional applicability.

생성형 AI 시대에 디자인 비전공자의 창작 참여가 확대되고 있으나, 결과물의 전문성 부족이라는 한계에 직면하고 있다. 본 연구는 이러한 한계점을 극복하기 위한 효과적인 생성형 AI 융합 디자인 교육 방안을 모색하고자, 디자인 비전공자 대상 생성형 AI 활용 포스터 공모전을 진행하였으며, 디자인 전공 학생들의 비판적이고 전문적인 시각을 분 석하여 비전공자의 생성형 AI 활용 결과물의 완성도 향상에 필요한 시사점을 도출하고자 한다. 연구 결과, 디자인 비전 공자들은 생성형 AI 활용 교육에 높은 만족도(4.32/5점)를 보이며 창작 참여 의향이 유의미하게 증가했다. 반면, 디자인 전공생들은 비전공자의 결과물 품질을 비판적으로 평가하였으며, 디자인 전공자 인식 분석 결과, 4학년(86.7%)이 1학 년(26.7%)보다 유의미하게 더 부정적이었다. 이는 비전공자 대상 생성형 AI 활용한 디자인 교육이 단순히 도구 활용을 넘어, 전문적 안목을 바탕으로 심미성, 창의적 사고, 결과물의 완성도를 높이는 방향으로 나아가야 함을 시사한다.

This study explores the pedagogical opportunities and instructional practices that emerge when elementary preservice teachers design science lessons using generative artificial intelligence (GenAI). Drawing on Chiu’s (2024) fourdomain model—Learning, Teaching, Assessment, and Administration—ten third-year pre-service teachers in South Korea participated in a four-week workshop using ChatGPT to design and refine Earth Science lessons aligned with the national curriculum. The participants documented their lesson planning, AI interactions, and reflections, producing qualitative data that were analyzed thematically. Findings show that participants identified various educational possibilities: GenAI supported idea generation and inquiry scaffolding (Learning), helped structure student-centered strategies (Teaching), improved formative assessments and clarified misconceptions (Assessment), and assisted with lesson preparation and time management (Administration). These possibilities translate into specific pedagogical practices, including revising teachercentered approaches to inquiry-based learning, developing scaffolded materials suited to students’ cognitive levels, and reflecting on their evolving roles as science educators. This study suggests that GenAI can act not merely as a tool but also as a catalyst for pedagogical reflection and professional growth. This highlights the need for teacher education programs to foster critical pedagogical reasoning and ethical AI literacy to ensure thoughtful and responsible use of GenAI in science classrooms.

This study proposes a real-time content design pipeline optimized for Unreal Engine, integrating generative AI-based image creation with AI-assisted 3D modeling tools. The pipeline aims to streamline the production of high-quality assets for real-time applications, including games and simulations. Two types of subjects were selected: a bust combining organic character features, and a stone slab characterized by planar and symmetrical structure. Multi-angle image data were first synthesized using advanced generative AI models to simulate diverse viewpoints. These were then processed using AI-enhanced photogrammetry and modeling tools to reconstruct detailed 3D meshes and extract base textures. Post-processing steps, including mesh decimation, UV unwrapping, and texture baking, were performed to ensure compatibility with Physically Based Rendering (PBR) workflows used in Unreal Engine. The final assets were successfully imported into Unreal Engine, demonstrating visual fidelity and performance suitability in a real-time environment. The study confirms the pipeline’s potential for accelerating asset development and suggests promising future directions in AI-driven digital content creation.

Advances in digital tools and building structure technologies have enabled more flexible architectural design, with AI-based performance design gaining considerable attention as a new design methodology. Stadium design must consider the two primary elements of sports events: athletes and spectators. Given that the facade of a stadium directly impacts solar energy efficiency, it is essential to incorporate environmental performance considerations from the initial design phase. This study employs an AI-based Generative Design process to generate a facade form that efficiently manages solar radiation and daylight, satisfying two conflicting performance objectives: max- imizing sunlight for turf growth in the pitch zone and minimizing direct sunlight exposure in the stadium seating zone. The optimal solution derived ranks 331st for pitch zone sunlight and 408th for stadium seating sunlight out of a dataset of 1,000 models. While this solution does not represent the absolute best for either individual objective, it is evaluated as the most balanced alternative, achieving the goal of maximizing sunlight in the pitch zone and minimizing it in the seating zone

In the case of Korean coastal fishing vessels primarily, it satisfies the fishing capacity and shifts in pace with trends. At the moment, speedy vessels with large load capacity and competitive hull forms are preferred since catch has decreased. However, in the design of Korean coastal fishing vessels, performance verification designers and related commercial programs are not utilized in various fields such as large vessels. Moreover, alleviated standards are applied, making securing and verifying the performance of fishermen’s preferred hull a must. To meet such demands, this research suggests a design system that the modules can be brought together as a fishing vessel model by AI; this would be a turnaround of coastal fishing vessel designing.

본 연구에서는 Text-to-3D 생성형 AI 기술을 활용하여 메타버스 방 꾸미기 게임의 프로토타 입을 설계하고 구현하고자 하였다. <Roblox>와 <Minecraft>와 같은 가상 현실 기반의 메타버 스 게임은 사용자를 단순한 플레이어에서 창작자인 크리에이터로 발전할 수 있게 하였고 이러 한 재미 요소는 대중적인 인기에 이바지하였다. 생성형 AI는 데이터와 패턴을 기반으로 다양 한 형태의 미디어 콘텐츠를 쉽게 생성할 수 있으며, 게임 개발에도 마찬가지로 유용하다. 이러 한 생성형 AI를 통한 콘텐츠 제작은 시간과 비용을 절약할 뿐만 아니라 결과적으로 콘텐츠의 품질을 높이고 다양성을 확보할 수 있다. 본 연구에서는 언리얼 엔진의 네트워크 프레임워크 를 활용한 리슨 서버(Listen-Server) 방식으로 방 꾸미기 게임을 설계 및 구현하였다. 이 게 임의 핵심 시스템은 메타버스에서 사용자가 쉽게 생성형 AI로 3D 모델을 생성하고, 자신의 방 에 배치할 수 있게 하는 것이다. 본 연구를 통해 코딩 기초 이해는 물론 좀 더 쉬운 방법으로 3D 오브젝트 생성을 통해 사용자가 원하는 메타버스 플랫폼 제작을 가능하게 하며 이러한 과 정은 사용자뿐만 아니라 동시에 창작자의 역할로 이용자의 주체성, 창의성, 의사소통 능력 등 을 향상할 가능성을 찾고자 한다. 그뿐만 아니라 기본적인 코딩 학습을 이해함으로써 사용자 의 창작 활동에 기회를 확장할 뿐만 아니라 메타버스 콘텐츠 개발에 이바지하고자 한다.

이 연구는 생성형 AI(Generative AI) 기술을 활용하여 기본 시각디자인 분야에서의 새로운 접근 방법과 가능 성을 탐색한다. Generative AI는 데이터 기반 학습을 통해 창의적인 디자인을 생성하는 인공지능 기술로, 시 각디자인에서 중요한 역할을 하고 있다. 본 연구는 Generative AI가 시각디자인의 기본 요소와 원리에 어떻게 적용되며, 디자인 프로세스를 어떻게 혁신할 수 있는지를 분석한다. 먼저, Generative AI가 색채, 형태, 구성과 같은 디자인의 기본 요소를 어떻게 해석하고 재창조하는지에 대해 연구한다. 이를 통해 AI가 디자인의 창의 성과 예술성을 어떻게 향상시킬 수 있는지를 탐구한다. 또한, AI가 디자인 결정 과정에서 어떻게 인간 디자 이너를 보조할 수 있는지에 대해서도 연구한다. 이 연구는 Generative AI를 실험하고, 이를 통해 얻은 시각디 자인 결과를 통해, AI가 시각디자인의 전통적인 접근 방식에 어떤 새로운 시각과 해석을 제공하는지를 조사 한다. AI 기술의 발전이 디자인의 미래와 디자이너의 역할에 어떤 변화를 가져올지에 대한 통찰과 디자인 분 야의 전문가뿐만 아니라, AI 기술에 관심 있는 학자들에게도 중요한 시사점을 제공한다는 점에서 의의를 찾 을 수 있다.

In recent years, the trend of customer demand and personalization has become more and more obvious. The previous innovation model can no longer meet the diversified needs of consumers. Therefore, firms vigorously develop open innovation to promote internal and external innovation (von Hippel, 1988). With the rapid development of AI technology, open innovation communities have more interactions with the users. Organizations continue to rely on their open innovation community to collect innovative ideas from non-professional customers and then integrate them into their new product development process to produce innovative products that are more in line with customer preferences (Bayus, 2013). At present, the research on user design focuses on how to increase user design implementation and the idea popularity (Yang et al., 2022; Zhang et al., 2022). Few studies discussed how to motivate consumers to participate in innovative content output from the source. In addition, academic research on user design is mostly limited to management comments, lacking in-depth empirical research (Franke et al. 2008). Previous studies have proved that the number of leading users in the open innovation community is far less than that of non-leading users (Hofstetter et al., 2018), so it is very necessary to improve the willingness of users to participate in community creative activities. With the vigorous development of the new technology, it is an urgent problem to be solved to encourage users to participate in innovation activities and improve the innovation performance of firms (Chesbrough, 2012). Today, firms pay more and more attention on the implementation of AI technology. With AI and user design as the research background, “AI recommendation” and “willingness to design” as the key variables, and the “S-O-R model” and “Self-determination Theory” as the basis, this paper deeply explores whether AI recommendation can be used as a factor affecting user’s participation in design activities from the perspective of users, focusing on the intermediary role of user’s inspiration, competency and self-expression. It also puts forward that product involvement and aesthetic experience openness (Donghwy and Youn, 2018) are the boundary conditions that affect user’s willingness to participate in design. The results show that user’s willingness to participate in design is higher when providing AI recommedation, and the sense of inspiration, competence and self-expression play a mediating role in it. Furthermore, the results show that when product involvement is high, users are more willing to participate in design. Similarly, users with a high degree of aesthetic experience openness are more willing to participate in design activities. This study enriches the theory of enterprise community management, promote the internal information flow of the open innovation community, and provide theoretical guidance and reference for firms to optimize the new product design process.