Developing novel research questions (RQs) often requires extensive literature reviews, especially for interdisciplinary fields. Leveraging Large Language Models (LLMs), we built an LLM-based agent system, called CoQuest, supporting RQ development through human-AI co-creation. We conducted an experimental design with 20 participants to examine the effect of two interaction designs: breadth-first and depth-first RQ generation. The results showed that participants found the breadth-first approach more creative and trustworthy upon task completion. However, during the task, they rated the RQs generated through the depth-first approach as more creative. We also discovered that AI processing delays allowed users to contemplate multiple RQs simultaneously, resulting in more generated RQs and an increased sense of perceived control. Our work makes both theoretical and practical contributions by proposing and assessing a mental model for human-AI co-creation RQs.

翻译：开发新颖的研究问题（RQs）通常需要大量文献综述，尤其是跨学科领域。利用大语言模型（LLMs），我们构建了一个名为CoQuest的LLM智能体系统，通过人机共创支持研究问题开发。我们设计了包含20名参与者的实验，探究两种交互设计——广度优先与深度优先研究问题生成的效果。结果显示，参与者在任务完成后认为广度优先方法更具创造性和可信度；但在任务过程中，他们对深度优先方法生成的研究问题评价更高。我们还发现，AI处理延迟使用户能够同时思考多个研究问题，从而生成更多研究问题并增强感知控制感。我们的工作通过提出并评估人机共创研究问题的心理模型，做出了理论与实践的双重贡献。