More scientists are now using AI, but prior studies have examined only how they use it 'at the desk' for computer-based work. However, given that scientific work often happens 'beyond the desk' at lab and field sites, we conducted the first study of how scientific practitioners use AI for embodied physical tasks. We interviewed 12 scientific practitioners doing hands-on lab and fieldwork in domains like nuclear fusion, primate cognition, and biochemistry, and found three barriers to AI adoption in these settings: 1) experimental setups are too high-stakes to risk AI errors, 2) constrained environments make it hard to use AI, and 3) AI cannot match the tacit knowledge of humans. Participants then developed speculative designs for future AI assistants to 1) monitor task status, 2) organize lab-wide knowledge, 3) monitor scientists' health, 4) do field scouting, 5) do hands-on chores. Our findings point toward AI as background infrastructure to support physical work rather than replacing human expertise.

翻译：如今越来越多的科学家使用人工智能，但以往研究仅考察了他们在“书桌前”进行计算机工作的使用方式。然而，鉴于科学工作常发生于实验室与野外现场等“书桌之外”的场所，我们首次开展了关于科学从业者如何将人工智能应用于具身物理任务的研究。通过对12位从事核聚变、灵长类认知及生物化学等领域动手实验与野外工作的科学从业者进行访谈，我们发现人工智能在相关场景中面临三大障碍：1）实验装置风险过高，无法承受人工智能失误；2）受限环境使人工智能难以部署；3）人工智能无法匹敌人类的隐性知识。在此基础上，参与者针对未来人工智能助手提出了5项设想性设计：1）监控任务状态，2）组织实验室全局知识，3）监测科学家健康状况，4）执行野外勘测，5）完成体力杂务。研究结果表明：人工智能应作为支撑物理工作的后台基础设施，而非替代人类专业能力。