A study on the Exploring Visual Design Students’ Continuance Intention to Use ChatGPT from a Design Education Perspective

With the rapid advancement of generative artificial intelligence technologies, ChatGPT has increasingly permeated the field of design education. In visual design programs, ChatGPT is widely applied across various stages, including information retrieval, ideation, copywriting, and color matching. Based on an integrated model of the Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) theory, this study investigates the factors influencing visual design students’ continued intention to use ChatGPT. In the TAM framework, perceived usefulness (PU) refers to the extent to which students believe ChatGPT effectively supports their design tasks, while perceived ease of use (PEOU) relates to the simplicity and convenience of operating the tool. The TTF model emphasizes how task characteristics (TAC) and ChatGPT’s technological features (TEC) affect task-technology fit (TTF), the degree to which ChatGPT’s functions align with the needs of design tasks.This study employed a mixed-methods approach, combining structured interviews and a questionnaire survey. Seven visual design students were first interviewed to understand their practical experiences and preferences when using ChatGPT. Based on the interview results, an integrated TAM-TTF model was constructed, and a corresponding questionnaire was developed. A total of 337 valid responses were collected, with results indicating high reliability and validity. Structural equation modeling (SEM) was used to empirically test the proposed model.The findings indicate that task-technology fit (TTF), perceived usefulness (PU), and ChatGPT’s technological features (TEC) are key factors influencing students’ continued use of ChatGPT for design tasks and their willingness to recommend it to others. Based on these findings, this study proposes optimization strategies from an educational perspective—encompassing teaching methods, cognitive scaffolding, institutional support, and practical application. In addition, development strategies are proposed for AI developers, including the design of functional modules, guidance mechanisms, and performance evaluation systems tailored to visual design students. It is hoped that these insights will provide a reference for promoting the intelligent transformation of design education.