AI 中文总结
研究探索大语言模型辅助分析中生成式用户界面带来的新机会。通过采访、分析工具及比较研究，发现GUI利弊，总结权衡形成临时设计框架，为未来大语言模型辅助分析工作流程中的生成式用户界面设计提供参考。
AI 中文摘要
人们越来越多地使用ChatGPT等聊天机器人进行日常分析任务。虽然聊天机器人统一了许多分析功能，但长对话难以导航，难以重温先前步骤或重用成功的工作流程。大语言模型现在能生成高保真GUI代码，使人们能创建超越文本的定制分析工具。然而，生成式用户界面给分析工作带来的新机会仍不明确。我们采访了六位专业人士，分析了公开共享的大语言模型生成的GUI工具，并在聊天机器人和TaskArtisan之间进行了比较研究。我们发现GUI提高了清晰度和视觉呈现，但也带来了僵化和额外的提示挑战。我们将这些权衡总结为一个临时设计框架，为未来大语言模型辅助分析工作流程中的生成式用户界面提供参考。
英文摘要
People increasingly use chatbots such as ChatGPT for everyday analysis tasks. While chatbots unify many analysis functions (e.g., scripts, visualizations, summaries), long conversations become hard to navigate, making it difficult to revisit prior steps or reuse successful workflows. LLMs now generate high-fidelity GUI code that enables people to create customized analysis tools beyond text. Yet, what new opportunities generative UIs bring to analysis work remain unclear. We interviewed six professionals about analysis with chatbots, analyzed publicly shared LLM-generated GUI tools, and conducted a comparison study (N=12) between a chatbot and TaskArtisan, a technology probe that enables people to create and assemble generative analysis UI widgets for sequential and fan-out composition. We find that GUI improved clarity and visual presentation but also introduced rigidity and additional prompting challenges. We summarize the trade-offs into a provisional design framework (malleability, specification, interoperability) to inform future generative UI in LLM-assisted analysis workflows.
CommentsAccepted to the 2026 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2026)
