Yongjian Liu

Bias and hallucinations in low-resource cultural artefacts significantly impede text-to-image generation models from understanding and disseminating. Focusing on Tibetan as a Chinese minority culture, we produced a children’s picture book through two methods: AI generation and human illustrator. Eye-tracking experiments were employed to investigate participants’ implicit attitudes, aesthetic biases, and cultural perceptions towards these two sources. The results revealed that (1) the hand-drawn group demonstrated higher fidelity to Tibetan culture, exhibiting a positive aesthetic calibration effect in terms of cultural adaptability owing to viewers’ attention duration to the cultural symbols details. (2) The AI-generated group elicited greater viewer interest and emotional engagement through its asymmetric color palettes, especially in color richness and stylistic rendering, and achieved professional-level compositional maturity in multi-character scene generation. This study provides empirical evidence to inform the division of labor between humans and AI in children’s book illustration and explores potential models for future human-AI collaboration.

With the widespread application of artificial intelligence (AI) technology in the sports industry, the spectator’s experience is increasingly shaped by AI-driven features. To explore the mechanism through which the perceived AI-enabled spectating experience affects viewing behavior, and to validate the mediating role of viewing motivation (SDT Needs Satisfaction) in the relationship between AI and viewing behavior as well as the moderating role of player identification in this mediating pathway, we adopted literature review, survey, and empirical analysis methods. A sample of 272 Chinese tennis enthusiasts was surveyed, and both the measurement model and the structural model were evaluated. The results indicate that the measurement model has good internal consistency, reliability, convergent validity, and discriminant validity. The perceived AI-enabled spectating experience has a significant positive effect on viewing motivation, viewing intention, and recommendation intention. The data show that the indirect effect of the perceived AI-enabled spectating experience on the viewing intention through the viewing motivation is 0.0479, and the indirect effect of the perceived AI-enabled spectating experience on the recommendation intention through the viewing motivation is 0.0548. Both reached a significant level, and the direct effect of the perceived AI-enabled spectating experience has also reached statistical significance. Therefore, viewing motivation plays a partial mediating role between AI and viewing intention and between AI and recommendation intention. Player identification plays a significant positive moderating role (β = 0.2809 on viewing intention, β = 0.1621 on recommendation intention) in the relationship between viewing motivation and viewing behavior; however, it does not moderate the relationship between AI and viewing motivation. In other words, for spectators with higher player identification, viewing motivation drives more strongly both their viewing intention and recommendation intention. We suggest that sports event organizers and media use AI technologies to design differentiated marketing to enhance user engagement and optimize spectators’ experience. For spectators with lower player identification, improving service quality can enhance their satisfaction; for those with higher player identification, efforts should focus on strengthening their connection with the players.