A study on the dynamic governance mechanism of digital publishing policies driven by generative AI technology—based on an analytical framework of technological-institutional co-evolution

Introduction – The rapid evolution of generative artificial intelligence (GAI) is disrupting the digital publishing sector, creating governance challenges such as ambiguous copyright ownership and unclear platform liability. Existing research often interprets the technology-institution relationship through a unidirectional causal lens, lacking empirical analysis of their interactive mechanisms. This study aims to analyze the co-evolutionary dynamics between GAI and institutional responses to understand how policy systems adapt to technological change. Methods – This study employs a technological-institutional co-evolutionary framework using a mixed-methods approach. The methodology integrates natural language processing (NLP) topic modeling, judicial case coding, and a breakpoint test. The analysis compares 48 policy documents and 14 judicial cases from China, Europe, and the United States, spanning the period from 2016 to 2025. Results – The findings reveal that GAI has driven a structural shift in policy agendas toward AI governance and copyright issues. Comparative analysis shows divergent evolutionary trajectories: China exhibited administration-led catching-up characteristics with a policy lag of approximately 12 months, whereas Europe and the United States demonstrated collaborative adaptation patterns with a longer lag of approximately 24 months. Legal conflicts were predominantly concentrated in the attribution of copyright for AI-generated content (40.63% of cases) and platform liability (35.94%). Discussion – This study reveals the non-linear structural breaks and divergent evolutionary trajectories of institutional responses to GAI. By providing empirical evidence of how different governance systems navigate the balance between technological change and institutional inertia, the findings contribute to the development of adaptive AI governance strategies.