Generative artificial intelligence (GenAI) can accelerate Building Information Modeling (BIM) and digital-twin workflows, but probabilistic outputs and manipulated inputs create data-integrity risks that conventional construction-software liability rules do not fully explain. This study develops a reproducible doctrinal and comparative-law method that maps the GenAI value chain onto the Architecture, Engineering, and Construction (AEC) lifecycle. Its principal advantage over single-actor product or professional-negligence analyses is a qualitative proportional-liability assessment framework that evaluates technical control, foreseeability, verification capacity, causal contribution, and evidence preservation across developers, deployers, and professional users. The analysis identifies the following two recurrent pathways of harm: autonomous hallucination entering safety-relevant design information and adversarial or erroneous data entering digital-twin feedback loops. It also specifies syntax, semantic, cybersecurity, and human-verification controls linked to AEC information-management and quality standards. The framework is not an empirically calibrated formula and does not determine legally binding percentages. Instead, it provides a transparent decision aid for applying existing tort doctrines and regulatory duties to distributed GenAI-enabled construction workflows.

AI-driven personalization has become an important mechanism in interactive digital marketing, yet its influence on consumers’ online purchase intention remains insufficiently understood. Situated within the institutional context of digital platform consumption and personal information protection in China, this study examines 309 consumers who have received formal legal education. Participants were recruited primarily through Chinese universities and professional legal networks. A theoretical model is developed to investigate how perceived AI-personalized recommendations and perceived AI-personalized content influence online purchase intention, with perceived value and platform trust examined as two independent mediating mechanisms and digital consumer legal literacy as a boundary condition. Structural equation modeling (SEM) and artificial neural network (ANN) analysis are jointly employed to provide both explanatory and predictive perspectives. The results show that perceived AI-personalized recommendations and perceived AI-personalized content are both positively associated with online purchase intention and are indirectly related to it through perceived value and platform trust. Digital consumer legal literacy further positively moderates the effects of perceived AI-personalized recommendation and perceived AI-personalized content on perceived value and platform trust, indicating that consumers’ understanding of digital rights, platform responsibilities, and relevant legal rules shapes how they evaluate the value and trustworthiness of algorithmic personalization. The ANN analysis further identifies the relative importance of the predictor variables and provides predictive evidence complementary to the SEM results. By integrating a dual-path mechanism of “utility evaluation–trust evaluation” with digital consumer legal literacy as an institutionally grounded boundary condition, this study advances the theoretical understanding of the relationship between AI-personalized marketing and online purchase intention within the Chinese legal and platform-governance context, while also offering practical implications for optimizing personalized services and strengthening consumer rights governance on digital platforms.