Today’s development focuses on the integration of affective meaning into products and services. Kansei Engineering (KE) is a technology that establishes the relationship between customers’ kansei and design elements to optimize the design for customers. This study aims to identify the research status and emerging trends in KE research by using visualization analysis with CiteSpace. We retrieve 2830 articles from the core collection of the Web of Science (1995–2021). First, based on the chronological distribution and background of research institutions, journals, literatures, countries and core authors, this study analyzes the main contributions, knowledge sources, interdisciplinary characteristics and major research domains of KE. Second, the research hotspots and cutting-edge technologies are intuitively presented and analyzed to reveal the knowledge structure and research frontier. Finally, this research expounds upon the evolution and developmental process of each stage and proposes the emerging trends of KE research, which are the use of big data in the Internet era, multidisciplinary cooperation, and the integration of multiple mathematical algorithms to serve multiapplication practice. This is also the orientation for further research on KE in the succeeding stages.

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.