Generative AI Art and Creative Subjectivity: A Mixed-Methods Study Based on Grounded Theory and CRITIC

다국어 초록 (Multilingual Abstract)

With the rapid application of Generative AI technologies in image generation, style transfer, and creative assistance, the process and logic of artistic creation are undergoing a profound transformation. However, research on how AI art platforms influence creators' subjectivity remains limited, and few studies have systematically examined users' real-world experiences. To bridge this gap, this study investigated three representative AI art platforms — Midjourney, Runway ML, and Stable Diffusion —using a mixed-methods approach that integrates grounded theory and the CRITIC method. Semi-structured interviews were conducted with 12 experienced AI art practitioners between March and May 2025, and a theoretical framework comprising three core categories, 12 main categories, and 29 initial categories was constructed through grounded theory coding. Building on these findings, we distributed a structured questionnaire using a 7-point Likert scale to 426 participants from June to August 2025, and we quantitatively evaluated the relative importance of the main categories using CRITIC analysis. The results showed that control over expressive outcomes (w₁ = 0.106) and technical accessibility of model customization (w₉ = 0.103) were the two highest-weighted factors, indicating that creators prioritize autonomy and usability in AI-mediated art-making. In contrast, aspects such as cultural bias in training data and algorithmic transparency received lower weights, reflecting a limited awareness of structural issues in AI systems. Synthesizing the qualitative and quantitative findings, the study proposes a theoretical mechanism of "Generative AI intervention → reconstruction of the creative process → changes in subjectivity," illustrating how AI platforms reshape creative control, identity perception, and aesthetic decision-making. These findings provide theoretical insights into human–machine collaborative creativity and practical guidance for designing AI art platforms that enhance user autonomy, support diverse cultural expressions, and optimize mechanisms for creative identity.