标题： 个性化AI支架在创意工作中的协同多轮合作 显示英文标题

标题： Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work

摘要： 随着人工智能在知识工作中的深入应用，构建支持人类创造力和专业知识的助手变得越来越重要。 然而，在人机协作中实现协同效应并不容易。 向人工智能提供用户的人口统计信息、心理属性、发散思维和领域专业知识，可能会通过构建更有效的多轮交互来提高性能。 我们实施了一个基于大型语言模型的个性化助手，该助手根据用户的心理测量档案以及关于其工作风格的人工智能引导访谈，帮助用户完成一个虚构初创公司的营销任务。 我们将331名参与者随机分配到与通用AI（n = 116）、部分个性化AI（n = 114）或完全个性化AI（n = 101）一起工作。 与个性化AI一起工作的参与者产生的营销活动质量与创意显著高于AI单独能够产生的水平。 与通用AI相比，个性化AI提高了自我报告的帮助和反馈水平，同时增加了参与者的信任和信心。 因果中介分析表明，个性化通过增强人机交互中的集体记忆、注意力和推理间接地提高了性能。 这些发现提供了一个理论驱动的框架，其中个性化作为外部支架，建立共同基础和共享伙伴模型，减少不确定性并增强联合认知。 这为未来AI助手的设计提供了指导，使其最大化协同效应，在支持人类创造性潜力的同时限制负面同质化。 显示英文摘要

摘要： 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.