Light Trumps Shadow? How Generative AI Agent’s Language Arousal Influences Users’ Interactive Willingness: Evidence From Multimodal Analysis
研究了生成式AI语言唤醒度对用户交互意愿的影响，发现高唤醒语言显著提升交互意愿，且阴影视觉氛围比明亮氛围更能增强用户参与，挑战了“越亮越好”的设计假设。
This study investigates how language arousal in Generative AI systems influences users' interaction willingness, examining the roles of social identity and visual atmosphere. Drawing on the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) and Social Identity Theory, we constructed a theoretical model integrating language arousal, social identity, visual atmosphere, and interaction willingness, and analyzed 8,809 interactions from Character.AI using multimodal methods combining linguistic analysis and visual processing. Our findings reveal that high-arousal language significantly increases interaction willingness, with social identity mediating this relationship. Most notably, we discovered a “psychological defense-curiosity paradox”: shadow visual atmospheres, despite triggering initial defensive reactions, enhance engagement more effectively than light atmospheres, challenging conventional “brighter is better” design assumptions. This research advances theory by repositioning language arousal as a direct causal variable in AI interaction, extending cognitive processing models to human-AI contexts, and demonstrating how visual elements strategically modulate psychological responses. These insights provide valuable direction for developing emotionally intelligent AI systems that effectively balance linguistic stimulation and visual atmosphere to create more engaging human-AI experiences.