用对话式助手引导用户自主判断网络信息真假，提升数字素养。
"Can You Tell Me?": Designing Copilots to Support Human Judgement in Online Information Seeking
- 设计基于大模型的对话助手，通过提问引导用户思考而非直接给答案。
- 实验显示用户深度参与但正确率未提升，因聊天耗时与偏好正面信息。
- 适合关注数字素养教育、需平衡效率与批判性思维的研究者与设计者。
生成式AI正在改变人们的在线信息获取方式，但其流畅且权威的回应可能引发过度依赖，抑制独立验证与推理。本文提出一种基于大语言模型的对话式协作者，旨在支持而非替代用户的认知工作，培养数字素养。在一项预注册的随机对照试验中（N=261），对比三种界面条件，混合方法分析显示用户与协作者深度互动，表现出元认知反思。然而，协作者并未显著提升回答正确率或搜索参与度，主要受制于“聊天时间”与“探索行为”的权衡，以及用户对正面信息的偏见。定性分析揭示了协作者的苏格拉底式提问与用户对效率的需求之间存在张力。研究凸显了教学型协作者的潜力与局限，并提出了协调素养目标与效率需求的设计路径。
原文摘要 · Abstract (English)
Generative AI (GenAI) tools are transforming information seeking, but their fluent, authoritative responses risk overreliance and discourage independent verification and reasoning. Rather than replacing the cognitive work of users, GenAI systems should be designed to support and scaffold it. Therefore, this paper introduces an LLM-based conversational copilot designed to scaffold information evaluation rather than provide answers and foster digital literacy skills. In a pre-registered, randomised controlled trial (N=261) examining three interface conditions including a chat-based copilot, our mixed-methods analysis reveals that users engaged deeply with the copilot, demonstrating metacognitive reflection. However, the copilot did not significantly improve answer correctness or search engagement, largely due to a "time-on-chat vs. exploration" trade-off and users' bias toward positive information. Qualitative findings reveal tension between the copilot's Socratic approach and users' desire for efficiency. These results highlight both the promise and pitfalls of pedagogical copilots, and we outline design pathways to reconcile literacy goals with efficiency demands.
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