AI 中文总结
研究对话式产品顾问的可用性，通过对含受限自然语言生成等功能的笔记本搜索聊天机器人进行研究，发现设计透明不保证理解，排名解释有严重问题，参与者虽看重省力但希望有更多直接操作控件，贡献了可用性问题及设计建议。
AI 中文摘要
大语言模型能使对话式产品顾问流畅但不透明。若其在自然语言回复中隐藏排名背后的逻辑和推荐证据，会挑战用户理解、信任及引导结果的能力。一种应对是在顾问中构建透明度。我们报告了对一个此类系统的形成性、有主持人的出声思考可用性研究：一个用于笔记本电脑搜索的聊天机器人，具有受限自然语言生成、按需排名解释和比较功能。七名参与者完成了三项笔记本电脑搜索任务并报告了任务后的可用性指标。我们将他们的会话编码为严重程度分级的可用性问题。任务期间的易用性和满意度较高，但有两个发现突出。首先，设计透明并不保证理解：一些参与者原则上重视排名解释，但它导致了最严重的问题。其次，参与者重视顾问节省的精力，但一些人希望有额外的直接操作控件。我们为以用户为中心的对话式产品顾问贡献了一组按严重程度排序的可用性问题和设计建议。
英文摘要
Large language models can make conversational product advisors fluent but opaque. If they hide the logic behind a ranking and the evidence for a recommendation inside natural-language replies, they challenge users' ability to understand, trust, and steer the results. One response is to build transparency into the advisor. We report a formative, moderated think-aloud usability study of one such system: a chatbot for laptop search with constrained natural-language generation, an on-demand ranking explanation, and a comparison feature. Seven participants completed three laptop-search tasks and reported post-task usability measures. We coded their sessions into severity-rated usability problems. Ease and satisfaction during the tasks were high, but two findings stand out. First, transparency by design did not guarantee understanding: several participants valued the ranking explanation in principle, yet it caused the most severe problem. Second, participants valued the effort the advisor saved, but some wanted additional direct-manipulation controls. We contribute a severity-prioritized set of usability problems and design implications for human-centered conversational product advisors.
DOI：10.18420/muc2026-mci-ws103-310
