From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915990296592384 |
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| author | Jayasiriwardene, Shakyani Zhou, Hongyu Jiang, Weiwei Tag, Benjamin Koemel, Nicholas Ahmadi, Matthew Goncalves, Jorge Stamatakis, Emmanuel Withana, Anusha Sarsenbayeva, Zhanna |
| author_facet | Jayasiriwardene, Shakyani Zhou, Hongyu Jiang, Weiwei Tag, Benjamin Koemel, Nicholas Ahmadi, Matthew Goncalves, Jorge Stamatakis, Emmanuel Withana, Anusha Sarsenbayeva, Zhanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08194 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction Jayasiriwardene, Shakyani Zhou, Hongyu Jiang, Weiwei Tag, Benjamin Koemel, Nicholas Ahmadi, Matthew Goncalves, Jorge Stamatakis, Emmanuel Withana, Anusha Sarsenbayeva, Zhanna Human-Computer Interaction 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. |
| title | From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.08194 |