AI-powered planning tools show promise in supporting programming learners by enabling early, formative feedback on their thinking processes prior to coding. To date, however, most AI-supported planning tools rely on students' natural-language explanations, using LLMs to interpret learners' descriptions of their algorithmic intent. Prior to the emergence of LLM-based systems, CS education research extensively studied trace-based planning in pen-and-paper settings, demonstrating that reasoning through stepwise execution with explicit state transitions helps learners build and refine mental models of program behavior. Despite its potential, little is known about how tracing interacts with AI-mediated feedback and whether integrating tracing into AI-supported planning tools leads to different learning processes or interaction dynamics compared to natural-language-based planning alone. We study how requiring learners to produce explicit execution traces with an AI-supported planning tool affects their algorithmic reasoning. In a between-subjects study with 20 students, tracing shifted learners away from code-like, line-by-line descriptions toward more goal-driven reasoning about program behavior. Moreover, it led to more consistent partially correct solutions, although final coding performance remained comparable across conditions. Notably, tracing did not significantly affect the quality or reliability of LLM-generated feedback. These findings reveal tradeoffs in combining tracing with AI-supported planning and inform design guidelines for integrating natural language, tracing, and coding to support iterative reasoning throughout the programming process.

翻译：AI驱动的规划工具通过在学习者编码前提供关于其思维过程的早期形成性反馈，在支持编程学习者方面展现出潜力。然而迄今为止，大多数AI支持的规划工具依赖学生的自然语言解释，利用LLM来解读学习者对其算法意图的描述。在基于LLM的系统出现之前，计算机科学教育研究广泛研究了纸笔环境下的基于追踪的规划，证明通过具有显式状态转换的逐步执行进行推理，有助于学习者构建和完善程序行为的心理模型。尽管具有潜力，但关于追踪如何与AI中介反馈相互作用，以及将追踪整合到AI支持的规划工具中是否会带来与纯自然语言规划不同的学习过程或交互动态，目前知之甚少。我们研究了要求学习者使用AI支持的规划工具生成显式执行追踪如何影响其算法推理。在一项涉及20名学生的组间研究中，追踪使学习者从类似代码的逐行描述转向更多以目标驱动的程序行为推理。此外，它导致了更一致的部分正确解决方案，尽管最终的编码性能在不同条件下保持可比性。值得注意的是，追踪并未显著影响LLM生成反馈的质量或可靠性。这些发现揭示了将追踪与AI支持的规划相结合的权衡，并为整合自然语言、追踪和编码以支持整个编程过程中的迭代推理提供了设计指导。