Generative-AI platforms increasingly shape users’ visual experiences, raising HCI concerns about how algorithmic exposure affects aesthetic judgment. Drawing on Media System Dependency theory and aesthetic socialization, this study examines a sequential mechanism in which AIGC exposure (AE) increases media dependency (MD), fosters aesthetic internalization (AIN), and influences evaluations. Survey data from 579 respondents were analyzed using structural equation modeling, with platform awareness (PA) and cognitive laziness (CL) tested as moderators. Results support an “AE–MD–AIN” pathway. MD and AIN both enhance aesthetic attractiveness judgment (AAJ), while MD reduces perceived aesthetic diversity (PAD). AIN shows no significant effect on PAD. Moderation analyses show that PA weakens, whereas CL strengthens, the effects of AE on MD and AIN. The findings clarify how human–AI interaction may increase perceived attractiveness while narrowing sensitivity to diversity, offering implications for transparency, diversity-aware recommendation, and media-literacy interventions.
Tao Yu (Mon,) studied this question.