"Understanding the Behavioral Intention of Generative AI among Art and Design Students"

"Although the Technology Acceptance Model (TAM) has proven effective in explaining users' adoption of technologies, its explanatory power in creative disciplines such as art and design is still constrained. To address this gap, an integrated model was constructed combining Self-Determination Theory (SDT) with TAM to systematically examine the psychological mechanisms underlying art and design students' adoption of generative artificial intelligence (AI).This dataset contains the survey data used to validate the proposed SDT-TAM integrated framework. A total of 398 valid responses were collected from art and design students. The dataset includes demographic profiles (e.g., gender, age, education, major, and frequency of AI use) and responses to 37 measurement items evaluated on a seven-point Likert scale. These items capture variables including Perceived Autonomy (PA), Perceived Competence (PC), Perceived Relatedness (PR), Intrinsic Motivation (IM), Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Behavioral Intention (BI). The data is suitable for analysis using Structural Equation Modeling (SEM) to explore technology adoption in creative education contexts."