From narrative generation to interpretive stabilization: generative AI and the mechanism of ethical understanding

Generative Artificial Intelligence (GenAI) is increasingly reshaping narrative practices through character monologues and emotionally guided storytelling on digital platforms such as Bilibili and Chinese TikTok. While existing AI ethics scholarship has primarily focused on issues of bias, transparency, and governance, it offers limited explanatory power for understanding how generative systems reorganize the interpretive conditions under which moral judgment is formed. To address this gap, this study develops Generative Interpretive Mediation (GIM) as a mechanism-based framework for analyzing how GenAI intervenes in interpretive processes prior to explicit ethical evaluation. Based on an interpretive research design, the article examines widely circulated AI-generated character monologues as analytically selected cases of platform-mediated narrative production. The analysis identifies three interrelated operations: interpretive selection, interpretive pre-structuring, and interpretive orientation. Together, these operations do not simply produce narratives, but stabilize interpretation by reducing narrative ambiguity and guiding users toward coherent psychological trajectories. In doing so, GenAI often replaces sustained interpretive effort with pre-shaped explanatory closure, thereby reshaping how understanding is formed in everyday engagement with digital narratives. By bringing narrative hermeneutics into dialogue with behavioral accounts of the “illusion of understanding” and “decisional inertia,” the article situates GIM within an emerging epistemic turn in AI ethics. It argues that the central ethical concern of GenAI lies not primarily in biased outputs, but in its capacity to reorganize interpretive environments in ways that subtly condition the formation of public moral judgment.