Formulating high-quality research questions is one of the most fundamental challenges facing general practice professionals in China. Although they are deeply embedded in frontline practice and accumulate rich experiential knowledge, many still lack systematic methodological training and sustained research support. To address this gap, this methodological study developed and preliminarily validated a five-step human-AI collaborative approach based on AI chatbots. The approach translates classic methodological theories—such as the JBI PCC framework, implementation science, On Contradiction, and On Practice—into a set of standardized prompts, and combines them with stepwise human input to support five core tasks: practice observation and value assessment, information extraction and evidence-based conceptualization, literature searching and knowledge synthesis, construction of a preliminary research question, and method selection with feasibility assessment. This study provides a practical and accessible toolkit for Chinese general practice professionals, especially those with limited research training, to refine practice-based research questions in a structured manner. By lowering the threshold for moving from practical confusion to research initiation, this approach may help frontline practitioners overcome the common "zero-to-one" difficulty in starting research. More broadly, it offers a feasible methodological pathway for strengthening the generation of locally grounded evidence and promoting a practice-theory cycle in China's primary health care system.

提炼高质量研究问题是当前中国全科医务人员在从事相关领域研究时面临的核心挑战之一。他们虽然拥有丰富的实践经验，却普遍缺乏系统的方法学训练和专业指导。为应对此挑战，这项方法学研究开发并初步验证了一套基于人工智能聊天机器人（AI Chatbot）的"人机协作五步法"。该方法创新性地将经典科研方法学理论（如JBI PCC框架、实施科学、矛盾论与实践论等）"封装"为一系列标准化的AI指示词（Prompt），并与全科医务人员共同组成了一套人机协同系统，以完成"实践观察与价值评估""信息提取与要素循证化""文献检索与知识总结""构建研究问题雏形"和"方法选择与可行性评估"5个关键步骤。本研究为科研基础薄弱的中国全科医务人员提供了一个生成以实践导向科学问题的"提炼辅助器"，该工具的传播和应用，有望系统性地解决全科医务人员，尤其是基层医务人员开展科研工作时难以实现"从0到1"的启动难题。并为推动中国全科医学科研体系向着务实的"产生本土证据、实践-理论循环"的方向发展，提供了一条切实可行的路径。