From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction

2026-01-13

AI Summary

Current conversational agents exhibit fixed personalities after deployment, limiting their ability to adapt to users’ dynamic expectations across varying contexts. This work presents the first systematic exploration of a context-sensitive mechanism for dynamically modulating AI personality, enabling users to adjust the agent’s traits along eight dimensions in real time across informational, emotional, and evaluative tasks. Leveraging latent profile analysis to identify personality archetypes, combined with trajectory analysis and an online mixed-methods experiment, the study elucidates how user expectations form and evolve. Findings reveal significant discrepancies between initial and final personality configurations, with adjustment trajectories shaped by task context. Moreover, granting users autonomous control over personality settings enhances their perception of the AI as anthropomorphic and increases trust in the system.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Conversational agents are increasingly expected to adapt across contexts and evolve their personalities through interactions, yet most remain static once configured. We present an exploratory study of how user expectations form and evolve when agent personality is made dynamically adjustable. To investigate this, we designed a prototype conversational interface that enabled users to adjust an agent's personality along eight research-grounded dimensions across three task contexts: informational, emotional, and appraisal. We conducted an online mixed-methods study with 60 participants, employing latent profile analysis to characterize personality classes and trajectory analysis to trace evolving patterns of personality adjustment. These approaches revealed distinct personality profiles at initial and final configuration stages, and adjustment trajectories, shaped by context-sensitivity. Participants also valued the autonomy, perceived the agent as more anthropomorphic, and reported greater trust. Our findings highlight the importance of designing conversational agents that adapt alongside their users, advancing more responsive and human-centred AI.

Problem

• conversational agents
• AI personality
• context-sensitivity
• user adaptation
• dynamic adjustment

Innovation

• dynamic personality
• context-sensitive interaction
• conversational agent
• personality adaptation
• human-centred AI