Professional creative software has steep learning curves for novices due to complex interfaces, limited guidance, and unfamiliar terminology. To support educators and tool creators in addressing learner challenges, we introduce TaskLens, an LLM-based method that automatically generates task-conditioned scaffolded UIs from natural language task descriptions. Our method uses LLMs to identify workflow stages and domain concepts, select task-relevant tools, generate implementation code, and execute the code to produce scaffolded interfaces. The interfaces surface relevant tools, organize them by workflow stage, link them to domain concepts, and progressively disclose advanced features. We evaluate TaskLens by deploying two LLM-generated scaffolded interfaces in Blender, a professional 3D modeling software. A user study with beginners (n=32) showed that our scaffolded interfaces significantly reduced perceived task load, improved task performance through embedded workflow guidance, and increased domain concept learning in Blender during task execution. A second study with experts (n=8) showed improved task efficiency and potential to create personalized UIs for productivity and creativity.

翻译：专业创意软件因界面复杂、指导有限且术语生僻，导致新手学习曲线陡峭。为支持教育者和工具开发者应对学习挑战，我们提出TaskLens——一种基于大语言模型的方法，可从自然语言任务描述自动生成任务条件式脚手架用户界面。该方法利用大语言模型识别工作流程阶段与领域概念，选取任务相关工具，生成实现代码，并执行代码以产出脚手架界面。这些界面将相关工具可视化、按工作流程阶段组织、链接至领域概念，并渐进式披露高级功能。我们在专业3D建模软件Blender中部署两个由大语言模型生成的脚手架界面以评估TaskLens。面向初学者的用户研究（n=32）表明，我们的脚手架界面显著降低了感知任务负荷，通过内嵌工作流指导提升了任务表现，并在任务执行过程中增加了Blender领域概念的学习。面向专家的第二项研究（n=8）显示，该界面提升了任务效率，并具备为生产力与创造力创建个性化用户界面的潜力。