AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.

翻译：人工智能辅助在各专业领域均能显著提升生产力，尤其对新手工作者而言。然而，这种辅助如何影响有效监督人工智能所需技能的发展尚不明确。新手工作者若高度依赖人工智能完成不熟悉的任务，可能会在此过程中损害自身技能的习得。我们通过随机对照实验，研究开发者在有无人工智能辅助的情况下，如何掌握新的异步编程库。研究发现，使用人工智能会损害概念理解、代码阅读与调试能力，且平均而言并未带来显著的效率提升。完全委托编码任务的参与者虽显示出一定的生产力改善，但这是以牺牲库的学习为代价的。我们识别出六种不同的人工智能交互模式，其中三种涉及认知投入，即使在参与者接受人工智能辅助的情况下也能保持学习效果。我们的研究结果表明，人工智能增强的生产力并非通往能力的捷径，在引入工作流程时应审慎采用人工智能辅助以保护技能形成——尤其在安全关键领域。