Pedagogical approaches focusing on stereotypical code solutions, known as programming plans, can increase problem-solving ability and motivate diverse learners. However, plan-focused pedagogies are rarely used beyond introductory programming. Our formative study (N=10 educators) showed that identifying plans is a tedious process. To advance plan-focused pedagogies in application-focused domains, we created an LLM-powered pipeline that automates the effortful parts of educators' plan identification process by providing use-case-driven program examples and candidate plans. In design workshops (N=7 educators), we identified design goals to maximize instructors' efficiency in plan identification by optimizing interaction with this LLM-generated content. Our resulting tool, PLAID, enables instructors to access a corpus of relevant programs to inspire plan identification, compare code snippets to assist plan refinement, and facilitates them in structuring code snippets into plans. We evaluated PLAID in a within-subjects user study (N=12 educators) and found that PLAID led to lower cognitive demand and increased productivity compared to the state-of-the-art. Educators found PLAID beneficial for generating instructional material. Thus, our findings suggest that human-in-the-loop approaches hold promise for supporting plan-focused pedagogies at scale.

翻译：聚焦于典型代码解决方案（称为编程方案）的教学方法能够提升问题解决能力并激励多样化学习者。然而，方案导向教学法在入门编程之外的应用极为有限。我们的形成性研究（N=10名教育者）表明，识别编程方案是一个繁琐的过程。为在应用导向领域推进方案导向教学法，我们创建了一个基于大语言模型的自动化流程，通过提供用例驱动的程序示例和候选方案，减轻教育者方案识别过程中的繁重工作。在设计研讨会中（N=7名教育者），我们通过优化与大语言模型生成内容的交互，确立了最大化教师方案识别效率的设计目标。我们最终开发的工具PLAID使教师能够：访问相关程序语料库以启发方案识别，通过代码片段比较辅助方案优化，并协助将代码片段组织为结构化方案。我们在受试者内用户研究（N=12名教育者）中评估PLAID，发现相较于现有最优方法，PLAID能降低认知负荷并提升工作效率。教育者认为PLAID对生成教学材料具有显著价值。因此，我们的研究表明人机协同方法有望为大规模实施方案导向教学法提供有效支持。