探究用户如何与AI聊天机器人的轮次级设计摩擦进行交互
Probing How Users Interact with Turn-Level Design Frictions for AI Chatbots
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中文总结 AI 辅助
本研究探究AI聊天机器人的轮次级设计摩擦，设计6种摩擦探针开展24人被试内研究，发现该类摩擦会增加用户工作量等，且其影响因用户目标和工作流程而异。
中文摘要 AI 辅助
AI聊天机器人可帮助人们更快地写作，但也可能因能将极少输入转化为可用文本而导致用户过度依赖。本研究探讨轮次级设计摩擦：即对每次聊天机器人交互添加的刻意约束，用于减缓、限制或引导用户请求、获取或使用模型响应的方式。我们设计了6种摩擦探针，围绕三类机制组织：引导用户贡献、限制对生成内容的访问、重塑系统输出。在一项含24名参与者的被试内研究中，与常规AI聊天机器人相比，所有6种探针均增加了用户的工作量、任务时长及感知所有权，而其对回忆和识别的影响则更具选择性。我们还发现，参与者以不同方式适应摩擦，且同一约束可根据用户目标和工作流程的不同，支持或阻碍用户参与。
英文摘要
AI chatbots can help people write faster, but they can also encourage overreliance by making it easy to turn minimal input into usable text. We study turn-level design friction: intentional constraints added to each chatbot exchange that slow, limit, or redirect how users request, access, or use model responses. We designed six friction probes, organized around three mechanisms: eliciting user contribution, restricting access to generated content, and reshaping system output. In a within-subject study with 24 participants, all six probes increased workload, task duration, and perceived ownership relative to a conventional AI chatbot, while their effects on recall and recognition were more selective. We further found that participants adapted to friction in different ways, and that the same constraint could support or obstruct involvement depending on users' goals and workflows.
