Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
For designers of LLM-based conversational agents, this framework addresses the problem of static persona causing poor user experience in varied contexts like medical advice or coaching.
The paper identifies a gap in conversational agents that fix persona and personality, leading to misalignment in dynamic contexts. It proposes a Fluid Personality Framework that adapts both metaphorical persona and personality expression intensity based on task context, user goals, and urgency.
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 one-note assistants on user experience and uptake. Yet most CAs still fix both persona and style, risking misalignment when dynamics, urgency, and formality vary, for example in medical information seeking, fitness coaching, and reflective learning. We propose a Fluid Personality Framework that jointly adapts (1) the agent's metaphorical persona, such as coach, tutor, librarian, or tool, and (2) its personality expression intensity, low, medium, or high, as a function of task context, user goals and traits, and situational urgency. We sketch the framework and its core design dimensions.