As AI becomes more deeply embedded in knowledge work, building assistants that support human creativity and expertise becomes more important. Yet achieving synergy in human-AI collaboration is not easy. Providing AI with detailed information about a user's demographics, psychological attributes, divergent thinking, and domain expertise may improve performance by scaffolding more effective multi-turn interactions. We implemented a personalized LLM-based assistant, informed by users' psychometric profiles and an AI-guided interview about their work style, to help users complete a marketing task for a fictional startup. We randomized 331 participants to work with AI that was either generic (n = 116), partially personalized (n = 114), or fully personalized (n=101). Participants working with personalized AI produce marketing campaigns of significantly higher quality and creativity, beyond what AI alone could have produced. Compared to generic AI, personalized AI leads to higher self-reported levels of assistance and feedback, while also increasing participant trust and confidence. Causal mediation analysis shows that personalization improves performance indirectly by enhancing collective memory, attention, and reasoning in the human-AI interaction. These findings provide a theory-driven framework in which personalization functions as external scaffolding that builds common ground and shared partner models, reducing uncertainty and enhancing joint cognition. This informs the design of future AI assistants that maximize synergy and support human creative potential while limiting negative homogenization.

翻译：随着人工智能深度融入知识工作领域，构建支持人类创造力与专业能力的助手变得愈发重要。然而，实现人机协作中的协同效应并非易事。向AI提供关于用户人口统计学特征、心理属性、发散性思维及领域专长的详细信息，可能通过构建更有效的多轮交互支架来提升系统性能。我们开发了一款基于用户心理测量轮廓与AI引导式工作风格访谈的个性化大语言模型助手，帮助用户完成为虚构初创企业设计的营销任务。我们将331名参与者随机分配到使用通用型AI（116人）、部分个性化AI（114人）与完全个性化AI（101人）的实验组中。结果表明，使用个性化AI的参与者产出了质量与创造性显著更高的营销方案，其水平超越AI单独生成的结果。相较于通用型AI，个性化AI带来了更高水平的用户自评辅助性与反馈质量，同时增强了参与者的信任度与自信心。因果中介分析显示，个性化通过增强人机交互中的集体记忆、注意力与推理能力间接提升了任务表现。这些发现构建了一个理论驱动框架，将个性化视作建立共同认知基础与共享伙伴模型的外部支架，通过降低不确定性增强联合认知能力。该研究为未来最大化协同效应、支持人类创造潜能并限制负面同质化的AI助手设计提供了理论指导。