This study explores creative experiences and educational needs related to the use of generative artificial intelligence (AI) in fashion design and proposes educational strategies through an AI-based fashion design process framework. A qualitative research design was employed, involving semi-structured in-depth interviews with 10 fashion design educators who had experience using generative AI. Inductive content analysis was performed on the collected data using NVivo, comprising coding, categorization, and theme development. The findings were organized into four major themes: (a) perceptions of AI use in fashion design, (b) functional roles of AI in the creative process, (c) human–AI collaboration and creative agency, and (d) educational needs and ethical considerations. The results showed that generative AI was perceived not as a substitute for human designers but as a supportive tool that could enhance creative thinking, particularly in the ideation and visualization stages. Specifically, AI enabled rapid exploration of diverse design alternatives and reduced psychological pressure in early creative phases. Human–AI collaboration was characterized by a complementary structure in which AI generated visual suggestions but human designers retained the responsibility for aesthetic judgment and final decision-making. Finally, an AI-based fashion design process framework aligned with the Double Diamond model was derived from these findings, providing a conceptual basis for educational strategies in fashion design education.

This study investigated the visualization accuracy and educational applicability of generative artificial intelligence (AI) tools in fashion design education by comparing images generated from the same blouse sketch using GPT-based tools, LOOK AI, and Stable Diffusion under identical prompt conditions. Thirty-two professional fashion designers evaluated the generated outputs using a structured 10-item assessment scale, focusing on silhouette accuracy, detail representation, structural clarity, and overall visual completeness. Statistical differences among the tools were analyzed using one-way analysis of variance followed by post-hoc comparisons. The results revealed significant differences (p<.05) in key evaluation criteria: silhouette accuracy, detail implementation, structural interpretability, and overall completeness. LOOK AI excelled in representing structural elements such as seams, pleats, and pattern logic, indicating its strength in design-oriented applications and technical visualization tasks. In contrast, Stable Diffusion received higher ratings for overall visual balance and aesthetic coherence, despite showing relatively lower structural fidelity. GPT-based outputs received lower ratings for structural accuracy but were seen as valuable for promoting critical AI literacy via prompt-based exploration, iterative refinement, and reflective evaluation. These findings suggest that differences among AI tools should not be interpreted in terms of absolute superiority but as distinct educational affordances. Accordingly, this study proposes a three-axis instructional framework that integrates structure-oriented learning, creative visualization, and critical inquiry-based learning.

This study explores the use of Midjourney (V6) by fashion design undergraduates for AI-supported ideation, focusing on how outcomes differ based on fashion-domain competence and prompt/parameter instruction. A focused ethnographic, comparative case-study design was used to observe a short collection-development module. Data included Discord prompt and parameter logs, generated image outputs (mood boards, look proposals, and pattern drafts), one-on-one interviews, classroom observation notes, and expert co-coding and qualitative evaluation. Participants were organized into four groups by crossing Basic vs. Advanced Fashion competence (BF/AF) with Basic vs. Advanced Prompt training (BP/AP): BF-BP, AF-BP, BF-AP, and AF-AP. BF-BP depended on repetitive/imaginary use and generic descriptors, resulting in visually appealing yet conceptually fragmented and low-feasibility results. AF-BP leveraged a richer domain vocabulary to improve item-level adequacy but struggled to maintain collection-level consistency, leading to the use of external editing tools such as Photoshop and Illustrator for portfolio-level refinement. BF-AP quickly mastered commands and parameters (e.g., /describe, --chaos, --stylize, --ar, --tile, --no, --sref, --cref), generating appealing concept imagery while failing to convert outputs into wearable garments and cohesive collections. AF-AP combined advanced fashion knowledge with strategic parameter sequencing— broad exploration, followed by consistency control and selective refinement—achieving the most coherent, feasible outcomes and positioning AI as an early-stage accelerator rather than a substitute for core design and making skills. Overall, this study proposes “parameter literacy” as a domain-specific extension of GenAI literacy and offers a parameter–process mapping (divergent generation, consistency control, and editing/refinement) to enhance fashion curricula.

This study examines group differences in AI dependence, self-efficacy, and design fixation based on learners’ AI utilization experience, and further explores the relationships between AI dependence and perception-related variables within an AI-based design education context. To this end, I surveyed 42 learners who participated in an AI-based design class. I then performed data analysis in IBM SPSS Statistics 26, using one-way analysis of variance (ANOVA) and Pearson correlation. The ANOVA revealed statistically significant group differences in AI dependence by AI usage level, but no significant differences in self-efficacy or design fixation. Furthermore, age and professional experience showed no significant influence on most variables. Meanwhile, the correlation analysis revealed that AI dependence was significantly positively associated with design fixation, but not with self-efficacy. These results suggest that, in AI-based design education, learners’ perceptions and attitudes are not strongly differentiated by personal background factors such as age or professional experience, but are instead associated with their experience with AI use and perceived dependence on AI. By distinguishing and analyzing AI utilization and AI dependence, this study provides empirical evidence that contributes to a more nuanced understanding of learners’ cognitive perceptions in AI-based design education in practice.

본 연구는 AI 컴패니언을 활용한 관계 중심 게이미피케이션 기반 소아 디지털 치료 시스 템의 UX 설계 사례를 탐색적으로 제시한다. 기존 소아 디지털 치료제가 보상 중심의 행동 강화에 주로 초점을 맞춰 온 것과 달리, 본 연구는 게이미피케이션을 아동과 AI 컴패니언 간의 관계적 상호작용을 지원하는 정서적 경험 프레임으로 재정의하였다. 이를 위해 Norman의 정서디자인 이론을 기반으로 단계적 정서 UX 구조를 적용한 감정 중심 AI 워크북 을 설계하였다. 파일럿 사용자 관찰을 포함한 탐색적 사용성 연구 결과, 캐릭터 기반 AI 컴패니언과 음성 중심 인터랙션은 아동의 정서 표현 부담을 완화하고 자발적 참여를 유도 하는 데 긍정적인 역할을 하는 것으로 나타났다. 본 연구는 임상적 효과 검증을 목적으로 하지 않으며, 관계 중심 UX 접근이 소아 디지털 치료 설계에서 가질 수 있는 가능성을 제 시하는 탐색적 설계 사례로서, 향후 임상 및 장기 연구를 위한 기초적 논의를 제공한다.

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.

Recent advances in AI-based video synthesis and character generation technologies have opened new possibilities for content production. However, as AI-generated humanlike characters become increasingly realistic, they often evoke discomfort and rejection—a phenomenon known as the "uncanny valley." While technical realism has progressed rapidly, it alone does not guarantee emotional engagement or viewer acceptance. This study explores how emotional design, particularly as conceptualized by Donald Norman in visceral, behavioral, and reflective dimensions, serves as a strategic response to the uncanny valley in AI-generated content. Through case analyses—such as anthropomorphized animal shorts, interactive AI chatbots, and the reanimation of historical figures—this paper demonstrates how emotional design fosters trust, immersion, and affective resonance. Furthermore, it discusses the ethical implications of emotional manipulation and authenticity in AI-human interactions. By examining how emotional design restructures user perception beyond visual fidelity, this study offers a reinterpretation of the uncanny valley from an affective and aesthetic perspective.

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