Generative AI confronts semiotics with a new kind of sign-producing machine that actively reshapes the production and interpretation of visual content. Addressing the lack of humanities-based transdisciplinary research on this transformation, this study aims to establish a methodological foundation for the semiotic analysis of multimodal AI. By combining visual, social, quantitative, and multimodal semiotics, the paper proposes an integrated micro–meso–macro framework for evaluating AI-generated images. The analysis moves from the micro-level examination of plastic features and text-to-image translation, through the meso-level of enunciation, narrativity, and causality, to the macro-level of social stereotypes, ideology, creativity, rhetoric, truth, and inference. This is supported by a case study on lonely death and a semiotic explanation of latent space.
