面向入门编程的基于大语言模型的下一步提示生成
Next-Step Hint Generation for Introductory Programming Using Large Language Models
AI总结：
本研究探索大语言模型在入门编程教育中生成自动化下一步提示的应用，构建StAP-tutor并经学生实验与专家评估验证其个性化提示效果，同时指出作业末期提示存在的不足及后续研究方向。
摘要：
大语言模型具备答疑、撰写文章或解答编程习题等能力。由于这类模型易于获取，研究人员已针对其在编程教育中的能力与风险展开研究。本工作探讨LLM如何通过为学生提供自动化下一步提示支持，助力编程教育。我们研究了可生成有效下一步提示的提示词实践方法，并基于这些发现构建了StAP-tutor（分步提示导师）。我们通过开展学生实验及专家评估对该导师进行评测。结果显示，大多数LLM生成的反馈信息都描述了一个具体的下一步，且能针对学生的代码与解题思路实现个性化。不过，当学生接近作业完成阶段时，提示可能包含误导性信息，且细节充足度不足。本工作证明了LLM生成反馈的潜力，但其实践落地仍需进一步研究。
英文摘要：
Large Language Models possess skills such as answering questions, writing essays or solving programming exercises. Since these models are easily accessible, researchers have investigated their capabilities and risks for programming education. This work explores how LLMs can contribute to programming education by supporting students with automated next-step hints. We investigate prompt practices that lead to effective next-step hints and use these insights to build our StAP-tutor. We evaluate this tutor by conducting an experiment with students, and performing expert assessments. Our findings show that most LLM-generated feedback messages describe one specific next step and are personalised to the student's code and approach. However, the hints may contain misleading information and lack sufficient detail when students approach the end of the assignment. This work demonstrates the potential for LLM-generated feedback, but further research is required to explore its practical implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
