Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework

Technical Summary: Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework

Problem Statement
Large Language Model (LLM)-based conversational agents (CAs) are increasingly deployed across diverse domains such as healthcare, education, and productivity. While these agents possess the capacity to project nuanced personalities and adopt metaphorical roles, current design practices often rely on static personas (e.g., a fixed "friendly assistant") applied uniformly across varying contexts. This rigidity creates a misalignment risk: a casual, buddy-like persona may erode credibility in serious domains like medicine, while a strictly formal expert persona may suppress necessary empathy in coaching or reflective learning scenarios. Furthermore, existing research has largely treated two critical dimensions in isolation: (1) the selection of a metaphorical persona (the agent's role) and (2) the intensity of personality expression (the strength of trait manifestation). Adapting only one dimension while holding the other fixed can lead to suboptimal user experiences, such as a well-chosen "coach" persona that overwhelms the user with excessive enthusiasm.

Methodology and Proposed Framework
The paper does not present new empirical data but proposes a conceptual Fluid Personality Framework designed to unify metaphor-fluid persona design with adaptive personality expression. The framework operates on the premise that an agent must dynamically configure two orthogonal modules before each dialogue turn based on contextual factors (task type, domain, urgency) and user-specific factors (personality traits, interaction history).

1. Persona Module (Metaphor Adaptor):
   This module selects the most suitable role metaphor for the immediate situation from a fluid portfolio rather than a fixed identity. The agent can shift between archetypes such as a "Planner" (organized, pragmatic) for goal-setting, a "Cheerleader" (upbeat, supportive) for celebrating progress, or a "Tutor" (analytical, patient) for concept explanation. The framework also incorporates non-human metaphors (e.g., "Library" or "Guide") for information-heavy dialogues, extending beyond traditional anthropomorphic designs. Transitions between personas are managed via subtle linguistic bridging to maintain coherence, ensuring the agent is perceived as responsive rather than erratic.

2. Personality Module (Trait Intensity Modulator):
   This module modulates the strength and explicitness of personality traits (e.g., Extraversion, Agreeableness, Conscientiousness) within the selected persona. Drawing on the "Trait Modulation Keys" approach, the system adjusts tone, affect, and formality. For instance, in urgent contexts (e.g., medical reminders), the agent may reduce "agreeable chit-chat" to adopt a concise, serious tone (lowering Agreeableness, raising Conscientiousness). Conversely, for long-term behavior change, it may increase empathy to sustain motivation. The module also aligns with the user's profile, such as lowering extraversion for introverted users to avoid verbosity. The goal is to maintain the agent's expression within a "Goldilocks zone," where moderate expression outperforms low or high extremes.

Key Contributions

• Unified Framework: The primary contribution is the integration of metaphorical persona selection and trait-intensity modulation into a single, cohesive model. This addresses the gap where previous research treated role and expression intensity as separate concerns.
• Dynamic Adaptation Mechanism: The paper outlines a mechanism for real-time adaptation where both the "who" (persona) and the "how" (expression intensity) are functions of task context, user goals, and situational urgency.
• Design Dimensions: It sketches core design dimensions for CAs, emphasizing that persona fluidity (shifting roles) and expression modulation (adjusting intensity) are distinct but interdependent levers for optimizing user experience.

Results and Claims
As a position paper presenting a framework, the authors do not report new experimental results. Instead, the paper synthesizes findings from prior literature to justify the proposed approach:

• Persona Fluidity: Citing Desai et al. (2025), the paper notes that shifting personas (e.g., from "Genie" to "Search Engine") based on context increases user enjoyment and adoption intention without compromising trust or perceived intelligence.
• Expression Intensity: Citing Rahman and Desai (2026), the paper highlights an inverted-U relationship between personality expression intensity and user evaluations, demonstrating that moderate expression consistently outperforms low or high extremes in trust, intelligence, and enjoyment for goal-oriented tasks.
• Contextual Necessity: The paper argues that static personas fail to account for domain-specific needs (e.g., the preference for a "doctor" persona in health vs. a "calculator" in finance) and that human coaches naturally adjust role and tone, a capability CAs currently lack.

Significance
The paper posits that moving from static to adaptive personality is essential for the next generation of behavior-change agents. By coupling metaphorical persona selection with trait-intensity modulation, the Fluid Personality Framework aims to enable CAs to better align with changing user goals and contexts. The authors claim this approach will improve user experience and effectiveness in behavior-change settings by ensuring the agent remains suited to the specific demands of the moment, avoiding the pitfalls of misaligned personas or inappropriate expression levels. The work serves as a foundational proposal for designing CAs that are not just intelligent, but contextually and emotionally fluid.