Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
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Computer Science > Computation and Language
Title:Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
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 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.
| Comments: | Presented at Bridging AI and Behavior Change, a Bridge Program organized at the AAAI Conference on Artificial Intelligence 2026 (AAAI-2026) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| ACM classes: | I.2.1; H.5.2 |
| Cite as: | arXiv:2607.01034 [cs.CL] |
| (or arXiv:2607.01034v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01034
arXiv-issued DOI via DataCite (pending registration)
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