Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies and the interaction between student approaches and LLM responses. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Our findings suggest a nuanced relationship between the guidance provided and LLM's role in either answering or refining student input. Based on our findings, we provide design recommendations for optimizing learner-LLM interactions.

个性化聊天机器人教学助手在解决教室规模扩大的问题上至关重要，特别是在教师直接存在有限的情况下。大型语言模型（LLM）提供了一个有前途的途径，不断有研究探索它们在教育上的实用性。然而，挑战不仅在于建立LLM的效能，而且还在于理解学习者与这些模型之间的互动细微差别，这会影响学习者的参与和结果。我们在一个本科计算机科学课堂（N=145）进行了一个形成性研究，并在Prolific（N=356）上进行了一项受控实验，探索四种教学指导策略的影响以及学生方法与LLM响应之间的互动关系。直接的LLM答案稍微提高了表现，而改进学生解决方案培养了信任。我们的发现表明，所提供的指导和LLM在回答或改进学生输入方面的角色之间存在微妙的关系。根据我们的发现，我们提供了优化学习者-LLM互动的设计建议。

L2使用指导和互动策略对学习者表现和感知的影响