A key challenge in professional fact-checking is its limited scalability in relation to the magnitude of false information. While many Natural Language Processing (NLP) tools have been proposed to enhance fact-checking efficiency and scalability, both academic research and fact-checking organizations report limited adoption of such tooling due to insufficient alignment with fact-checker practices, values, and needs. To address this gap, we investigate a co-design method, Matchmaking for AI, which facilitates fact-checkers, designers, and NLP researchers to collaboratively discover what fact-checker needs should be addressed by technology and how. Our co-design sessions with 22 professional fact-checkers yielded a set of 11 novel design ideas. They assist in information searching, processing, and writing tasks for efficient and personalized fact-checking; help fact-checkers proactively prepare for future misinformation; monitor their potential biases; and support internal organization collaboration. Our work offers implications for human-centered fact-checking research and practice and AI co-design research.

通过合作设计方法“AI撮合”，我们与22名专业事实核查员进行了合作设计会议，得出了一套11个新颖的设计理念，针对高效个性化事实核查方面的信息搜索、处理、写作任务提供帮助，帮助事实核查员主动准备应对未来的误导信息，监测潜在的偏见，并支持内部组织的协作，该研究对人本事实核查研究及AI合作设计研究具有重要意义。

人本自然语言处理事实核查：使用AI进行与事实核查人员的协同设计