标题： 闭环：一种教师在回路中的AI辅助系统，用于支持编程教育中的学生求助行为 显示英文标题

标题： Closing the Loop: An Instructor-in-the-Loop AI Assistance System for Supporting Student Help-Seeking in Programming Education

摘要： 及时且高质量的反馈对于编程课程中的有效学习至关重要；然而，在大规模情况下提供这种支持仍然是一项挑战。 虽然基于人工智能的系统可以提供可扩展且即时的帮助，但它们的回复有时可能不准确或不够充分。 相比之下，人类教师可能具备更有价值的专业知识，但在时间和可用性方面受到限制。 为了解决这些局限性，我们提出了一种混合帮助框架，将人工智能生成的提示与升级机制相结合，使学生在人工智能支持不足时可以请求教师的反馈。 这种设计利用了人工智能在规模和响应速度上的优势，同时将教师的努力保留到最需要的时刻。 我们在一门有82名学生的数据科学编程课程中部署了这个工具。 我们观察到，在总共673条人工智能生成的提示中，学生将146条（22%）评为无帮助。 其中，只有16条（11%）的情况被升级到教师那里。 对教师回应的定性研究显示，这些反馈实例有一半左右是错误的或不够的。 这一发现表明，当人工智能支持失败时，即使是有专业知识的教师也可能需要更加注意以避免犯错。 我们将公开发布该工具，以便更广泛地采用，并在其他教室中进行进一步的研究。 我们的工作提供了一种实现高质量支持的实用方法，并为未来在教育中有效整合人工智能和人类提供了指导。 显示英文摘要

摘要： Timely and high-quality feedback is essential for effective learning in programming courses; yet, providing such support at scale remains a challenge. While AI-based systems offer scalable and immediate help, their responses can occasionally be inaccurate or insufficient. Human instructors, in contrast, may bring more valuable expertise but are limited in time and availability. To address these limitations, we present a hybrid help framework that integrates AI-generated hints with an escalation mechanism, allowing students to request feedback from instructors when AI support falls short. This design leverages the strengths of AI for scale and responsiveness while reserving instructor effort for moments of greatest need. We deployed this tool in a data science programming course with 82 students. We observe that out of the total 673 AI-generated hints, students rated 146 (22%) as unhelpful. Among those, only 16 (11%) of the cases were escalated to the instructors. A qualitative investigation of instructor responses showed that those feedback instances were incorrect or insufficient roughly half of the time. This finding suggests that when AI support fails, even instructors with expertise may need to pay greater attention to avoid making mistakes. We will publicly release the tool for broader adoption and enable further studies in other classrooms. Our work contributes a practical approach to scaling high-quality support and informs future efforts to effectively integrate AI and humans in education.