Abstract:Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such designs shape user perceptions. This study investigates how personality expression levels and user-agent personality alignment influence perceptions in goal-oriented tasks. In a between-subjects experiment (N=150), participants completed travel planning with CAs exhibiting low, medium, or high expression across the Big Five traits, controlled via our novel Trait Modulation Keys framework. Results revealed an inverted-U relationship: medium expression produced the most positive evaluations across Intelligence, Enjoyment, Anthropomorphism, Intention to Adopt, Trust, and Likeability, significantly outperforming both extremes. Personality alignment further enhanced outcomes, with Extraversion and Emotional Stability emerging as the most influential traits. Cluster analysis identified three distinct compatibility profiles, with "Well-Aligned" users reporting substantially positive perceptions. These findings demonstrate that personality expression and strategic trait alignment constitute optimal design targets for CA personality, offering design implications as LLM-based CAs become increasingly prevalent.
| Comments: | 30 pages, CHI 2026 conference paper (article no. 371) |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2509.09870 [cs.HC] |
| (or arXiv:2509.09870v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2509.09870 arXiv-issued DOI via DataCite |
|
| Related DOI: | https://doi.org/10.1145/3772318.3790388
DOI(s) linking to related resources |
Submission history
From: Hasibur Rahman [view email]
[v1]
Thu, 11 Sep 2025 21:43:49 UTC (11,250 KB)
[v2]
Wed, 29 Apr 2026 01:28:14 UTC (10,201 KB)