Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.

翻译：摘要：由于需要规划、交互设计和编程方面的专业知识，社交机器人的编程对新手程序员来说具有挑战性。虽然大型语言模型通过从自然语言描述生成代码展现出巨大潜力，但它们可能模糊编程的关键要素并取代设计者的意图，最终导致过度依赖而非发展编程技能。本文通过研究性设计过程，探索了基于大语言模型的社交机器人编程工具如何支持新手程序员。我们设计并原型化了Robo-Blocks，这是一个积木式编程环境，利用大语言模型通过结构化叙事为新手程序员提供生成式脚手架，将高层次想法连接到可执行的机器人行为。通过在新手间的部署，我们发现了生成式脚手架的新兴用户画像和使用模式，并展示了这种脚手架如何塑造终端用户的设计与编程策略。我们提出了有效使用生成式脚手架及其融入社交机器人编程实践的设计见解。