Tracing Generative AI in Digital Art: A Longitudinal Study of Chinese Painters' Attitudes, Practices, and Identity Negotiation
Yibo Meng, Ruiqi Chen, Xin Chen, Zhiming Liu, Yan Guan
TL;DR
This paper presents a five-year longitudinal mixed-methods study of 17 Chinese digital painters to trace how attitudes and practices toward generative AI evolve and how professional identity is negotiated. By combining annual surveys with in-depth interviews, it reveals a three-phase trajectory from resistance to pragmatic adoption to reflective reconstruction, mediated by peer influence and emotional shifts. It contributes a rare longitudinal dataset, a theoretical lens of identity and value negotiation in technology adoption, and design implications for user control, transparency, diverse collaboration modes, and preserving serendipity in human-AI creative work. These insights advance understanding of human-AI collaboration in creative domains and offer guidance for designing sustainable, artist-centered AI tools.
Abstract
This study presents a five-year longitudinal mixed-methods study of 17 Chinese digital painters, examining how their attitudes and practices evolved in response to generative AI. Our findings reveal a trajectory from resistance and defensiveness, to pragmatic adoption, and ultimately to reflective reconstruction, shaped by strong peer pressures and shifting emotional experiences. Persistent concerns around copyright and creative labor highlight the ongoing negotiation of identity and values. This work contributes by offering rare longitudinal empirical data, advancing a theoretical lens of "identity and value negotiation," and providing design implications for future human-AI collaborative systems.