The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been reported in the literature. However LLMs also present challenges, one of the most commonly cited being that of student over-reliance. This challenge is compounded by the fact that LLMs are always available to provide instant help and solutions to students, which can undermine their ability to independently solve problems and diagnose and resolve errors. Providing instructor oversight of LLM-generated content can mitigate this problem, however it is often not practical in real-time learning contexts. Online class discussion forums, which are widely used in computing education, present an opportunity for exploring instructor oversight because they operate asynchronously. Unlike real-time interactions, the discussion forum format aligns with the expectation that responses may take time, making oversight not only feasible but also pedagogically appropriate. In this practitioner paper, we present the design, deployment, and evaluation of a `bot' module that is controlled by the instructor, and integrated into an online discussion forum. The bot assists the instructor by generating draft responses to student questions, which are reviewed, modified, and approved before release. Key features include the ability to leverage course materials, access archived discussions, and publish responses anonymously to encourage open participation. We report our experiences using this tool in a 12-week second-year software engineering course on object-oriented programming. Instructor feedback confirmed the tool successfully alleviated workload but highlighted a need for improvement in handling complex, context-dependent queries. We report the features that were viewed as most beneficial, and suggest avenues for future exploration.

翻译：将大型语言模型（LLMs）融入计算教育可为学生学习带来诸多潜在益处，文献中已报道了若干新颖的教学方法。然而，LLMs也带来了挑战，其中最常被提及的是学生过度依赖问题。这一挑战因LLMs始终可随时为学生提供即时帮助与解决方案而加剧，这可能削弱学生独立解决问题、诊断及修正错误的能力。对LLM生成内容实施教师监督可缓解此问题，但在实时学习场景中往往难以实现。广泛运用于计算教育的在线课堂讨论论坛因其异步特性，为探索教师监督机制提供了契机。与实时互动不同，讨论论坛的形式符合回复可能需要时间的预期，使得监督不仅可行，且在教学设计上更为适宜。在这篇实践报告中，我们介绍了一个由教师控制并集成于在线讨论论坛的“机器人”模块的设计、部署与评估。该模块通过生成学生问题的草拟回复来协助教师，这些回复在发布前需经审核、修改与批准。其关键功能包括利用课程材料、访问历史讨论记录以及匿名发布回复以鼓励开放参与。我们报告了在一门为期12周、面向大二学生的面向对象编程软件工程课程中使用该工具的经验。教师反馈证实该工具有效减轻了工作负担，但指出其在处理复杂、依赖上下文的查询方面仍需改进。我们总结了被视为最具价值的核心功能，并提出了未来探索的方向。