The growing emphasis on sustainable development across industries has prompted the fashion sector to explore responsible and resource-efficient practices. Applying this theoretical framework to the development of original fashion works, this study investigates the relationship between Korean aesthetic consciousness and sustainable fashion design. The research proceeds in three stages. First, a literature review was conducted to analyze the aesthetic characteristics of Korean aesthetic consciousness, categorized as aesthetic naturalism, elegant simplicity, and aesthetic humor, and to examine how these characteristics relate theoretically to sustainable fashion design principles, such as cyclicity, ornamentation, and deconstructivity. Second, based on this analysis, key design attributes were derived and used to develop six sustainable fashion works using the theme of Haya (夏夜, summer night), with motifs drawn from the Cheonghwa Baekja, a cicada, and pearls. Third, digital prototyping was carried out using the CLO 3D program, and final garments were produced with secondhand items, deadstock materials, and textile waste. The results demonstrate that upcycled materials can yield aesthetically sophisticated and functional garments. They also show that Korean aesthetic values provide a culturally grounded framework for sustainable fashion practice. The use of 3D CLO effectively reduced carbon emissions associated with physical sampling and enabled the verification of resource usage. This study proposes a creative method for integrating Korean aesthetics into sustainable fashion design and provides practical directions for future sustainable fashion development.

This study reinterprets the Digital Product Passport (DPP), introduced under the EU Ecodesign for Sustainable Products Regulation (ESPR), not as a post-development information tool but as a design condition embedded throughout the fashion design process. It proposes a design framework that links DPP requirements to stage-based decision-making in fashion design. While existing studies have focused on policy, technological infrastructure, and supply chain management, limited attention has been given to integrating DPP into design decision-making. This study reviews ESPR-related policy documents and prior research to derive six core DPP information categories using a literature-based approach. These categories are then integrated into a seven-stage fashion design process, building on the model of Lamb and Kallal by adding a stage for “information design and lifecycle integration.” This study identifies how DPP information functions as design decision criteria across various stages. Finally, the findings are synthesized into a framework integrating DPP information and post-launch feedback. DPP information in fashion is categorized into six domains: traceability, material and chemical data, supply chain information, environmental impact, use and care, and end-of-life management. When mapped onto the seven-stage design process, DPP requirements guide design directions during the design refinement stage and translate into product-level data at the prototype development stage. This study integrates both pre-design and post-launch feedback and reconceptualizes DPP as an embedded design condition, contributing to a theoretical link between design practice and regulatory compliance.

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 examines the historical background, types, and characteristics of apron-style clothing from the Joseon dynasty and reinterprets them through contemporary fashion design. Here, apron-style clothing refers to clothing worn over a first layer, secured with straps, and structured to cover the front of the body. During the Joseon dynasty, apron-style clothing was differentiated according to purpose as follows: daily use, performance use, and ceremonial use. Structurally, it can be classified into four types: square, square with multiple straps, three-pronged with waist gathers, and three-pronged with narrow pleats. Based on Joseon-period apron-style clothing’s historical significance, structural features, and layered visual effects a total of four contemporary fashion designs were developed using CLO 3D as a design tool: Design 1 is a mini dress in a tube-top style derived from a simple square apron, Design 2 is a mini dress that reinterprets the Yuso [流蘇] decoration of the Boro [甫老], Design 3 is a cape design divided into three sections that is inspired by the Suboro [繡甫老], and Design 4 is a layered skirt reflecting the pleated structure of the Jeonhaeng-utchima [前香上裳]. The design outcomes demonstrate that the structural characteristics of traditional apron-style clothing are effectively expressed through silhouette, while their layered qualities are rendered with depth using different materials and colors. This research are expected to contribute to a deeper understanding of Korean traditional costume and to expand its value and potential for contemporary application.

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.

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.

Modern society seeks coexistence across boundaries, inspiring fashion studies that transcend gender norms to promote equality and harmony. The current study aimed to (a) analyze and classify the design characteristics of men’s genderless fashion (Study 1) and (b) investigate the perceptions, attitudes, and purchase intentions of millennial and Gen Z male consumers regarding genderless fashion (Study 2). For the first study, we selected 192 photographs highlighting genderless features from the men’s clothing collections of 2020 F/W and 2021 S/S in Paris, Milan, London, and New York. A group of fashion experts conducted the classification, with an inter-coder reliability of 92.1%. Genderless fashion was classified into four design properties: fusion, mixing male and female characteristics; juxtaposition, showing coexistence without mixing; transition, emphasizing opposite characteristics; and neutrality, showing neither male nor female features. For Study 2, an online survey was conducted with men in their 20s and 30s (N=74, MeanAge=31.04). The participants reviewed 12 representative photographs that were obtained from Study 1, evaluating recognition, attitudes, and purchase intentions. The results showed that the participants perceived transition as the most feminine, while they judged juxtaposition, fusion, and neutrality as more neutral. The stronger the feminine elements in the materials, colors, silhouettes, fit, and decorative details were, the lower the attitudes and purchase intentions became. This research is significant vis-à-vis identifying the design characteristics of men’s genderless fashion and providing design directions that may encourage actual purchases among millennial and Gen Z male consumers.

This study explores how to integrate the generative artificial intelligence (AI) tool Midjourney into the fashion design process, emphasizing the visualization of sporty fashion concepts. The research applied Midjourney at every stage of the fashion design process: mood board, fashion sketch, flat drawing, production package, fashion show presentation, and store display and sales. Specifically, sporty fashion was selected as the theme, and customized prompts were developed from prior research and design principles to generate visual outputs for each stage. Furthermore, three apparel design experts evaluated the AI-generated images to assess Midjourney’s practical applicability and effectiveness in each phase of the fashion design workflow. Expert evaluations revealed that Midjourney was particularly effective in the early stages, offering diverse and visually engaging imagery that supported creative ideation and mood expression. The tool allowed quick exploration of different silhouettes during the sketching stage but was imprecise in detailed forms and proportions. Limitations became more evident in the flat drawing and work instruction stages, where outputs failed to accurately reflect material textures and technical construction. Prompt refinements and referencebased prompts were tested but often resulted in inconsistent or stylized outputs. Additionally, continuity between stages was missing. Midjourney shows potential as a creative tool, but experts highlight its limitations for practical industry application. Further research is needed to improve prompt optimization and training data for enhanced accuracy and usability in AI-assisted fashion design workflows.

This study aimed to analyze the characteristics of the New-Chinese style appearing in Chinese fashion designer Shiatzy Chen’s collections and, accordingly, recommend various directions for the New-Chinese style design. Regarding the research method, the background and concept of Chinese style and New-Chinese style were reviewed, while relevant characteristics were derived from prior research. Concerning the data collection, 21 season collections from A/W 2014 to A/W 2024, were selected, and 26 reviews and 1,168 collection data were collated. The results are as follows: First, “multicultural convergence” emerges as design methods reflecting social changes and utilizes diverse cultural elements. In fashion design, it emerges in a harmonious combination of culture and artistic styles, expressing the integration of varied cultures —for instance, East–West cultural convergence. Second, “historical tradition” consistently expresses Shiatzy Chen’s fashion; continuously explores Chinese people’s garments and aesthetics; and integrates oriental dignity, culture, and discipline into fashion design. Third, “temporary diversity” refers to a Chinese fashion design culture that represents the seamless blending of traditional culture and modern social values— leading the new consumer market by combining new and old generations, graffiti aesthetics on modern streets, and traditional and contemporary expressions. Fourth, “positive metaphor” expresses the intrinsic mental element and emphasizes covert rather than overt beauty. These expressions metaphorically communicate ancient Chinese legends via artistic techniques, such as contemporary illustration and embroidery, thereby conveying an active mental culture.

This study analyzes the expressive characteristics of fashion design through sensory experiences, focusing on the Metropolitan Museum of Art’s exhibition Sleeping beauties: Reawakening fashion. It aims to explore the creative and innovative potential of fashion design that utilizes sensory experiences while proposing new directions for future fashion exhibitions and the design field. The research scope includes 220 garments and accessories featured in the exhibition. Based on theoretical considerations and case studies, this research classifies and examines fashion design characteristics from the perspectives of olfactory, tactile, visual, auditory, and multi-sensory experiences. The findings indicate that olfactory experiences enhance emotional engagement by integrating fragrance with storytelling, while tactile ones increase immersion through fabric texture and structural elements. Visual experiences strengthen aesthetic appeal and narrative through various digital technologies and lighting effects, while auditory ones convey the emotional messages of fashion design using sound and music. Additionally, multi-sensory experiences combine multiple sensory elements to provide viewers with an immersive, multidimensional experience. This study confirms that sensory experiences expand the creative possibilities of fashion design and suggests new exhibition strategies that integrate sustainability and digital technology. Beyond simple stimuli, sensory experiences foster emotional connections and contribute to enhancing fashion design’s artistic and cultural value. Therefore, this research highlights the importance of integrating sensory experiences with creative approaches and technological convergence to amplify the social and artistic impact of fashion design.

This study explored the development direction of punk fashion through 3D digital fashion design by analyzing the expressive characteristics and inherent meanings of the punk fashion expressed in Vivienne Westwood’s creations. To this end, the concept underlying punk and its expressive characteristics were examined, and the eco-friendly expressive attributes of punk fashion were scrutinized through photos of Vivienne Westwood’s collections. The analysis focused on 10 seasonal collections showcased over the past five years, from the designer’s 2018 S/S to 2022 F/W collections. The results revealed that Westwood’s punk expression was characterized by traditionality, eco-friendliness, playfulness, resistance, and deconstruction. Traditionality appeared in the form of a harmonious redesign of classics to create new works, while eco-friendliness was manifested through the use of slogans and eco-friendly materials and methods. Playfulness conveyed positive messages through comedic situations or characters, and resistance emerged as a means of delivering messages for improving social issues. Finally, the deconstruction of punk was reflected as resistance, boldly destroying fashion structures to express dissatisfaction with society. Punk design in fashion is one of the avenues in which designers effectively express the messages that they want to communicate to society. This study is significant, as it provides foundational data for exploring punk characteristic design strategies to be used in future fashion. The scope of application for 3D virtual clothing programs is expected to expand in the fashion industry, and continuous research on digital fashion design is anticipated.

This study investigates the cognitive characteristics and transition flow of team mental models in virtual reality environments (VRE) compared to Zoom-based collaboration during fashion design problem-solving processes. Using the VRE platform Spatial.io as a case study, the study examines how virtual reality technologies influence the formation of shared mental models among collaborators. The objective is to identify key cognitive features that VREs offer to support problem-solving in multidisciplinary fashion design collaborations. The study employed a comparative experimental design, involving professionals from fashion design, marketing, and production. Participants completed the design concept generation tasks in both VRE and Zoom environments, all interactions were recorded and coded, with the analysis focusing on cognitive transitions, verbal dynamics, and collaborative behavior patterns across both environments. The results reveal that VRE fosters higher frequencies of environmental interaction (EI-EI), cognitive integration (CIM-CI), and planning to execution transitions in team interactions (PP-PIM), forming multidirectional feedback loops. These features enhanced dynamic adaptation to environmental stimuli. In contrast, Zoom-based collaboration relied heavily on linear verbal communication, with fewer cognitive transitions and limited structural feedback loops, thereby reducing efficiency in idea evaluation and execution in team interaction. The study highlights the potential of VREs to transform collaborative practices in fashion design by enabling immersive and multidimensional interactions, contributing to advancing digital collaboration strategies in creative industries, with implications for education and interdisciplinary innovation.