Advancements in Large Language Models (LLMs), such as ChatGPT, offer significant opportunities to enhance instructional support in introductory programming courses. While extensive research has explored the effectiveness of LLMs in supporting student learning, limited studies have examined how these models can assist instructors in designing instructional activities. This work investigates how instructors' expertise in effective activity design can be integrated with LLMs' ability to generate novel and targeted programming problems, facilitating more effective activity creation for programming classrooms. To achieve this, we employ a participatory design approach to develop an instructor-authoring tool that incorporates LLM support, fostering collaboration between instructors and AI in generating programming exercises. This tool also allows instructors to specify common student mistakes and misconceptions, which informs the adaptive feedback generation process. We conduct case studies with three instructors, analyzing how they use our system to design programming problems for their introductory courses. Through these case studies, we assess instructors' perceptions of the usefulness and limitations of LLMs in authoring problem statements for instructional purposes. Additionally, we compare the efficiency, quality, effectiveness, and coverage of designed activities when instructors create problems with and without structured LLM prompting guidelines. Our findings provide insights into the potential of LLMs in enhancing instructor workflows and improving programming education and provide guidelines for designing effective AI-assisted problem-authoring interfaces.

翻译：以ChatGPT为代表的大型语言模型（LLMs）的进步为增强入门编程课程的教学支持提供了重要机遇。尽管已有大量研究探讨了LLMs在支持学生学习方面的有效性，但关于这些模型如何协助教师设计教学活动的探索仍较为有限。本研究探讨如何将教师在有效活动设计方面的专业知识与LLMs生成新颖且有针对性的编程问题的能力相结合，从而促进编程课堂中更有效的活动创建。为实现这一目标，我们采用参与式设计方法开发了一款集成LLM支持的教师创作工具，以促进教师与人工智能在生成编程练习方面的协作。该工具还允许教师指定学生常见的错误和迷思概念，从而为自适应反馈生成过程提供依据。我们与三位教师进行了案例研究，分析他们如何使用我们的系统为其入门课程设计编程问题。通过这些案例研究，我们评估了教师对LLMs在教学目的问题陈述创作中的有用性和局限性的看法。此外，我们比较了教师在有无结构化LLM提示指导的情况下创建问题时，所设计活动的效率、质量、有效性和覆盖范围。我们的研究结果为LLMs在优化教师工作流程和改进编程教育方面的潜力提供了见解，并为设计有效的人工智能辅助问题创作界面提供了指导原则。