Unlike static and rigid user interfaces, generative and malleable user interfaces offer the potential to respond to diverse users' goals and tasks. However, current approaches primarily rely on generating code, making it difficult for end-users to iteratively tailor the generated interface to their evolving needs. We propose employing task-driven data models-representing the essential information entities, relationships, and data within information tasks-as the foundation for UI generation. We leverage AI to interpret users' prompts and generate the data models that describe users' intended tasks, and by mapping the data models with UI specifications, we can create generative user interfaces. End-users can easily modify and extend the interfaces via natural language and direct manipulation, with these interactions translated into changes in the underlying model. The technical evaluation of our approach and user evaluation of the developed system demonstrate the feasibility and effectiveness of the proposed generative and malleable UIs.

翻译：与静态且固化的用户界面不同，生成式与可塑用户界面具备响应用户多样化目标与任务的潜力。然而，现有方法主要依赖于生成代码，这使得终端用户难以根据其不断变化的需求对生成的界面进行迭代式定制。我们提出以任务驱动数据模型——即表征信息任务中核心信息实体、关系及数据——作为用户界面生成的基础。我们利用人工智能技术解析用户提示，生成描述用户预期任务的数据模型，并通过将数据模型与用户界面规范进行映射，从而创建生成式用户界面。终端用户可通过自然语言与直接操作轻松修改和扩展界面，这些交互操作将被转化为底层模型的相应变更。对本方法的技术评估以及对所开发系统的用户评估，共同证明了所提出的生成式与可塑用户界面的可行性与有效性。