This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

Artificial intelligence (AI) is reshaping design education, yet field-specific instruments to assess AI literacy remain scarce. Drawing on UNESCO’s AI Competency Framework together with Design Thinking, Systems Thinking and human–AI co-creativity, this study reconceptualizes AI literacy for undergraduate design students and develops the AI-CIEI Scale as a four-dimensional measurement tool. An initial item pool was generated through literature synthesis and expert review, and then refined via cognitive interviews and pilot testing. A survey of 485 design majors from eight public universities in China (5-point Likert scale) was conducted; exploratory factor analysis and confirmatory factor analysis supported a 25-item structure comprising Cognition & Collaboration, Implementation & Integration, Ethics & Critical Judgment, and Innovation & Systemic Thinking, with excellent model fit, high internal consistency, and satisfactory convergent and discriminant validity. Criterion-related validity was examined using a MIMIC-type structural equation model including gender, academic year, major, AI training frequency, and AI usage duration as predictors: academic year showed small but consistent positive associations with all four dimensions, whereas the other variables displayed weak and non-significant effects. These findings indicate that AI literacy in design education reflects intertwined cognitive, procedural, ethical and systemic competencies shaped more by program-level learning trajectories than by short-term exposure, and position the AI-CIEI Scale as a theoretically grounded tool for diagnostics, curriculum alignment and future intervention studies.

This paper is based on the theory of “image schema” in cognitive linguistics, taking the teaching interface of clothing operation knowledge as a research case, to explore the innovative application of image schema in 3D virtual simulation teaching interfaces, aiming to improve learners' ability to gain clothing operation knowledge. This study includes three main stages. First, the image schema related to clothing operation behavior is summarized through an expert interview method. Next, according to the prototypes of image schema such as start-up, light–dark, path, matching, and compulsion schema, 3D animation is created, and the matching degree experiment between the schema and operation behavior is carried out. Finally, based on the high matching degree schema, the teaching interface is optimized, and its effectiveness is verified through interface satisfaction evaluation. The experimental results show that the matching degree of the 3D representation of the image schema introduced with the physical operation behavior of clothing is significantly greater than that of the design without the schema. Moreover, the optimized interface has significant advantages in terms of learners' satisfaction with the learning effect of operation knowledge. The experiment verifies the applicability of image schema theory in clothing operation virtual simulation teaching, proposes a new method of 3D representation based on image schema, and provides theoretical support and a practical path for the optimization of the clothing field operational 3D teaching interfaces.

With the widespread application of generative artificial intelligence (AIGC) in design, its impact on design conception across different user groups is a significant research issue in design education. This study explores the differences in design conception between designer users and ordinary users using AIGC for poster design, and evaluates the application of multimodal discourse theory and SEM models. The study defines four variables in the poster design concept: representational meaning, interactive meaning, compositional meaning, and color meaning. A questionnaire with 22 items was distributed to 120 respondents. SEM analysis showed a good fit and significant inter-group differences. Designers’ design ideation is influenced by all dimensions, while compositional meaning does not affect ordinary users. Designers’ path coefficients were balanced across dimensions, while ordinary users showed significant differences, particularly in representational and interactive meaning. This study verifies the applicability of multimodal discourse theory and offers insights into design conception differences in the AIGC context. It emphasizes the need to improve non-professional users’ design skills and provides guidance for developing more user-friendly AIGC tools.