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.

翻译：在编程课程中，及时且高质量的反馈对有效学习至关重要；然而，大规模提供此类支持仍具挑战。虽然基于AI的系统能够提供可扩展的即时帮助，但其响应有时可能不准确或不充分。相比之下，人类教师虽能提供更具价值的专业知识，但受时间和可用性限制。为应对这些局限，我们提出一种混合式帮助框架，将AI生成的提示与升级机制相结合，允许学生在AI支持不足时向教师请求反馈。该设计利用AI在规模化和响应速度上的优势，同时将教师精力保留在最需要的时刻。我们在有82名学生参与的数据科学编程课程中部署了该工具。在总计673条AI生成的提示中，学生将146条（22%）评为无帮助。其中仅16例（11%）升级至教师处理。对教师反馈的定性分析表明，这些案例中约有一半存在错误或不充分的情况。这一发现提示，当AI支持失效时，即使是具备专业知识的教师也需要投入更多关注以避免失误。我们将公开该工具以促进更广泛的应用，并支持在其他教学场景中的进一步研究。本工作为规模化高质量支持提供了实践路径，并为未来在教育中有效整合AI与人类智慧提供了参考依据。