Generative AI (GenAI) tools offer increasing opportunities for augmenting human cognitive tasks. Among these tasks, information seeking is being rapidly reshaped by GenAI tools, with potentially profound implications for learning and knowledge acquisition. To investigate these implications, we conducted a between-subjects field experiment in which participants pursued informal learning by seeking information through either ChatGPT or Google Search over a span of 8 days. Using a daily diary protocol, we gathered in-situ data on their information-seeking processes. Our findings show that participants in the ChatGPT group experienced diminished agency in their information-seeking processes, as they offloaded much of the information selection to AI, and consequently experienced greater meta-cognitive load arising from this reduced sense of control. We further highlight two sources of distortion in information access when using ChatGPT: biases in ChatGPT outputs, particularly towards providing solution-oriented artifacts over principled knowledge; and systematic shifts in users' information-seeking behaviors, whereby the conversational and socially-oriented interaction paradigm of current GenAI tools may inadvertently reduce exploration of the broader knowledge space. As a result, on average, participants in the ChatGPT group had worse learning outcomes than those using Google, especially for higher-order critical learning. Our work suggests inherent tensions between offloading information seeking to AI and meaningful learning, and provides broader implications for understanding AI's risks to human cognition.

翻译：生成式人工智能（GenAI）工具为增强人类认知任务提供了日益增多的机会。在这些任务中，信息搜寻正被GenAI工具迅速重塑，并可能对学习和知识获取产生深远影响。为探究这些影响，我们进行了一项受试者间实地实验，让参与者在8天时间内通过ChatGPT或谷歌搜索追寻非正式学习。采用每日日记协议，我们收集了其信息搜寻过程的现场数据。研究结果显示，ChatGPT组的参与者在其信息搜寻过程中体验到了能动性减弱，因为他们将大部分信息选择活动外包给AI，并由此因控制感降低而经历了更高的元认知负荷。我们还进一步揭示了使用ChatGPT时信息访问的两类扭曲来源：ChatGPT输出的偏差，尤其是倾向于提供面向解决方案的产物而非原理性知识；以及用户信息搜寻行为的系统性转变，当前GenAI工具的对话式和社交导向交互范式可能无意中减少了对更广泛知识空间的探索。因此，平均而言，ChatGPT组参与者的学习效果劣于使用谷歌的组，尤其是在高阶批判性学习方面。我们的工作揭示了将信息搜寻外包给AI与有意义学习之间的固有张力，并为理解AI对人类认知的风险提供了更广泛启示。