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.

研究了大型语言模型在编程教育中的应用，通过提供自动化的下一步提示来支持学生，发现大多数由大型语言模型生成的反馈信息描述了一个具体的下一步，并针对学生的代码和方法进行个性化处理，但当学生接近任务结束时，提示可能包含误导性信息并缺乏足够的细节。该研究展示了大型语言模型生成反馈的潜力，但需要进一步研究来探索其实际应用。