  Developing novel research questions (RQs) often requires extensive literature
reviews, especially in interdisciplinary fields. To support RQ development
through human-AI co-creation, we leveraged Large Language Models (LLMs) to
build an LLM-based agent system named CoQuest. We conducted an experiment with
20 HCI researchers to examine the impact of two interaction designs:
breadth-first and depth-first RQ generation. The findings revealed that
participants perceived the breadth-first approach as more creative and
trustworthy upon task completion. Conversely, during the task, participants
considered the depth-first generated RQs as more creative. Additionally, we
discovered that AI processing delays allowed users to reflect on multiple RQs
simultaneously, leading to a higher quantity of generated RQs and an enhanced
sense of control. Our work makes both theoretical and practical contributions
by proposing and evaluating a mental model for human-AI co-creation of RQs. We
also address potential ethical issues, such as biases and over-reliance on AI,
advocating for using the system to improve human research creativity rather
than automating scientific inquiry.
