AI 中文总结
研究日常人类与大语言模型交互中的非正式学习，通过分析大规模对话，将学习科学结构转化为行为特征，发现认知与建设性参与情况及相关因素，表明交互含非正式学习特征，转变了AI评估方向。
AI 中文摘要
随着大语言模型越来越有能力为用户完成任务，一个核心担忧是日常人工智能使用可能主要变成认知卸载，侵蚀人们发展自身能力的机会。我们分析大规模人类与大语言模型对话，询问在这种情况下非正式学习行为是否也会出现，即用户是否以保留学习机会的方式进行交流。在128569次自然对话中，我们将学习科学结构转化为轮次级行为特征。认知参与（交流中反映的用户认知努力）出现在491685个用户轮次的31.9%中，而建设性参与（可观察到的最深层次的以学习为导向的参与）出现在4.9%中，表明更深入的意义构建是反复出现但具有选择性的。我们的研究进一步确定了与这些参与形式相关的因素。有支架的助手支持始终标志着更丰富的建设性参与，其关联因用户框架、任务生态、支持形式、时间和用户先前状态而异。这些发现共同表明，日常人类与大语言模型交互不仅是答案传递或认知卸载；它还包含非正式学习的可测量、有选择性且有条件组织的行为特征。它们将人工智能评估从答案传递效率转向为用户在日常解决问题过程中保留推理、测试想法和构建理解的认知机会。
英文摘要
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities through which people develop their own capabilities. We analyse large-scale human-LLM conversations to ask whether informal learning behaviors also emerge in this setting: whether users engage in exchanges in ways that preserve opportunities to learn. Across 128,569 naturalistic conversations, we translated learning-science constructs into turn-level behavioural signatures. Cognitive engagement, users' cognitive effort as reflected in the exchange, appeared in 31.9% of 491,685 user turns, whereas constructive engagement, the deepest observable form of learning-oriented engagement, appeared in 4.9%, showing that deeper sense-making was recurrent but selective. Our study further identifies factors associated with these forms of engagement. Scaffolded assistant support consistently marked richer constructive participation, with associations varying by user framing, task ecology, support form, timing and prior user state. Together, these findings show that everyday human-LLM interaction is not only answer delivery or cognitive offloading; it also contains measurable, selective and conditionally organized behavioural signatures of informal learning. They shift AI evaluation from answer-delivery efficiency toward the preservation of cognitive opportunities for users to reason, test ideas and construct understanding in the course of everyday problem-solving.
