How to Talk to AI: The Role of Preset Prompt Language Styles in Shaping Conversational Experience

Abstract

With the proliferation of AI conversational agents, the design of preset prompts—text suggestions that guide user interactions—has become crucial for enhancing user experience. This study investigates the impact of different preset prompt language styles (social-oriented vs. task-oriented) on user satisfaction. Utilizing two empirical studies, we examined how these language styles influence user perceptions of an AI agent’s warmth and competence, and how these perceptions mediate overall satisfaction. In the first study, participants interacted with an AI agent using either social-oriented or task-oriented prompts under conditions of service success or failure. The results indicated that social-oriented prompts significantly enhance user satisfaction by increasing perceptions of warmth, but not competence. However, this positive effect diminishes in the event of service failure. In the second study, we explored the moderating effect of task urgency. Findings revealed that the positive impact of social-oriented prompts on satisfaction is significant in low-urgency tasks but not in high-urgency scenarios. These insights underscore the importance of prompt language style in AI interactions and provide practical implications for designing more effective AI communication strategies, especially in customer service contexts.