Enterprise chatbots show promise in supporting knowledge workers in information synthesis tasks by retrieving context from large, heterogeneous databases before generating answers. However, when the retrieved context misaligns with user intentions, the chatbot often produces "irrelevantly right" responses that provide little value. In this work, we introduce VizCopilot, a prototype that incorporates visualization techniques to actively involve end-users in context alignment. By combining topic modeling with document visualization, VizCopilot enables human oversight and modification of retrieved context while keeping cognitive overhead manageable. We used VizCopilot as a design probe in a Research-through-Design study to evaluate the role of visualization in context alignment and to surface future design opportunities. Our findings show that visualization not only helps users detect and correct misaligned context but also encourages them to adapt their prompting strategies, enabling the system to retrieve more relevant context from the outset. At the same time, the study reveals limitations in verification support regarding close-reading and trust in AI summaries. We outline future directions for visualization-enhanced chatbots, focusing on personalization, proactivity, and sustainable human-AI collaboration.

翻译：企业聊天机器人在信息整合任务中展现出支持知识工作者的潜力，其通过从大型异构数据库中检索上下文后再生成答案。然而，当检索到的上下文与用户意图不一致时，聊天机器人常会产生"无关正确"的响应，这些响应价值有限。本研究提出VizCopilot，一个通过融入可视化技术以主动引导终端用户参与上下文对齐的原型系统。通过将主题建模与文档可视化相结合，VizCopilot在保持可控认知负荷的同时，实现了对检索上下文的人工监督与修改。我们采用VizCopilot作为设计探针，通过"设计驱动研究"方法评估可视化在上下文对齐中的作用，并揭示未来的设计机遇。研究发现表明，可视化不仅帮助用户检测并修正错位上下文，还能促使用户调整其提示策略，使系统能够从一开始就检索到更相关的上下文。同时，研究也揭示了在细粒度阅读验证和AI摘要可信度方面的支持局限。我们展望了可视化增强型聊天机器人的未来发展方向，重点关注个性化、主动性和可持续的人机协作。