发表机构
University of Michigan; KAIST（密歇根大学; 韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HelpCoach通过现场支架帮助学生在问题解决中提出针对性AI求助，提升求助技能与知识保留，优于仅任务前训练。
AI 中文摘要
学生越来越多地转向AI寻求问题解决的帮助，然而过多的AI支持本身可能削弱学习效果。为了从AI中获益，学生需要在其问题中明确所需的知识和支架类型。然而，由于缺乏识别和选择有效帮助选项的元认知技能，学生难以提出如此有针对性的问题。我们开发了HelpCoach，一个聊天界面的附加组件，帮助学生在问题解决过程中提出针对知识和支架的具体问题，并获得有针对性的帮助。HelpCoach持续评估学生的求助表现，并通过自适应修订模板提示学生改进。以往的工作大多在学习任务之外教授求助技能，而HelpCoach的现场支架使学生能够在元认知技能上进行具体练习，并立即修订求助行为。在一项涉及40名学习Web编程的大学生的研究中，与仅进行任务前求助训练相比，HelpCoach在聊天机器人互动中带来了更具体的问题，并提高了知识保留率。
英文摘要
Students increasingly turn to AI for help with problem-solving, yet too much AI support can undermine learning itself. To benefit from AI, students need to specify the necessary knowledge and scaffold type in their questions. However, they struggle to formulate such targeted questions because they lack metacognitive skills to recognize and select effective help options. We developed HelpCoach, an add-on for chat interfaces that helps students formulate knowledge- and scaffold-specific questions and receive targeted help during problem solving. HelpCoach continuously assesses students' help-seeking performance and prompts students to improve through an adaptive revision template. Whereas prior work has largely taught help-seeking skills apart from learning tasks, HelpCoach's in situ scaffold enables concrete practice on metacognitive skills and immediate revisions to help-seeking behavior. In a study with 40 college students learning web programming, HelpCoach led to more specific questions during chatbot interactions and greater knowledge retention than pre-task help-seeking training alone.
