研究助理人格如何影响用户信息查询行为与信任度。

Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does more than retrieve or generate content: it shapes how users articulate constraints, ask follow-up questions, verify claims, and decide when an answer is sufficient for action. Yet little is known about how user personality, assistant personality, and task context jointly influence these interactions. We examine personality as a controllable variable in conversational information seeking and study its effects on user behaviour and interaction quality. We conducted a controlled within-subject study in which assistant personality and task type were experimentally varied, while participant personality was measured using Big Five scores. Twenty-six participants each completed three information-seeking tasks under three assistant personality conditions: extraverted, conscientious, and neutral. Tasks covered exploratory travel planning, comparative smartphone shopping, and verification-sensitive health and diet information seeking. Data included conversation logs, behavioural traces, post-interaction questionnaires, an exit questionnaire, and Big Five measures. The assistant conditions were behaviourally distinct: the extraverted assistant produced longer turns, the conscientious assistant elicited higher user word share and more turns, and the neutral baseline fell between them. The strongest effect was a task-by-assistant interaction on trust and delegation, with preferred styles varying by task. No global winner emerged, but participants strongly preferred style choice or adaptation. These findings position assistant personality as a context-sensitive interactional design variable rather than a globally optimisable system property.