arXiv:2607.01034v1 Announce Type: new Abstract: Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (ii) context-appropriate metaphors outperform static o
The proliferation of ubiquitous large language model (LLM)-based conversational agents is driving research into optimizing their efficacy for specific tasks and user interactions.
This research outlines a framework for adaptable AI personalities, directly impacting user engagement and the effectiveness of AI-mediated behavior change applications.
AI conversational agents will move beyond static personas to dynamically adjust their personality and delivery based on context, improving trust and adoption in goal-oriented scenarios.
- · AI-powered customer service
- · Ed-tech platforms
- · Health-tech apps
- · AI agent developers
- · Companies relying on static, generic AI personas
- · Businesses with undifferentiated conversational AI
- · Users frequently disengaging from AI interactions
Further development and integration of behavior-adaptive conversational agents across various sectors.
Increased user reliance and trust in AI systems due to more fluid and context-aware interactions.
Ethical considerations around the manipulation of user behavior through highly personalized AI interactions become more prevalent.
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