A Dataset Assessing Continuous Use of Image-Generative Artificial Intelligence and Design Thinking Ability among Visual Arts Students: An Integrated UTAUT–ECM Approach

This dataset contains quantitative survey data collected from 278 undergraduate Visual Art students at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, examining factors influencing students’ satisfaction and continuous usage of Image-Generative Artificial Intelligence (Image-GenAI) and its effects on design thinking. The dataset includes respondents’ demographic characteristics (age, gender, academic level, department, and frequency of GenAI use) and 5-point Likert-scale measures adapted from established models, including UTAUT, Expectation–Confirmation Model (ECM), and Design Thinking theory constructs. Key variables cover satisfaction, continuous intention to use, effort expectancy, performance expectancy, social influence, facilitating conditions, expectation confirmation, and design thinking. The data are suitable for PLS-SEM analysis, technology acceptance studies, and research on AI adoption and creative cognition in visual art education.