Addressing the challenge of generating personalized feedback for programming assignments is demanding due to several factors, like the complexity of code syntax or different ways to correctly solve a task. In this experimental study, we automated the process of feedback generation by employing OpenAI's GPT-3.5 model to generate personalized hints for students solving programming assignments on an automated assessment platform. Students rated the usefulness of GPT-generated hints positively. The experimental group (with GPT hints enabled) relied less on the platform's regular feedback but performed better in terms of percentage of successful submissions across consecutive attempts for tasks, where GPT hints were enabled. For tasks where the GPT feedback was made unavailable, the experimental group needed significantly less time to solve assignments. Furthermore, when GPT hints were unavailable, students in the experimental condition were initially less likely to solve the assignment correctly. This suggests potential over-reliance on GPT-generated feedback. However, students in the experimental condition were able to correct reasonably rapidly, reaching the same percentage correct after seven submission attempts. The availability of GPT hints did not significantly impact students' affective state.

翻译：针对编程作业生成个性化反馈面临诸多挑战，包括代码语法的复杂性以及解决任务的多种正确方式等。本实验研究采用OpenAI的GPT-3.5模型，在自动化评估平台上为学生编程任务生成个性化提示，实现反馈生成过程的自动化。学生对GPT生成提示的有用性给予积极评价。实验组（启用GPT提示）对平台常规反馈的依赖程度降低，但在启用GPT提示的任务中，连续提交尝试的成功提交率更高。当GPT反馈不可用时，实验组解决作业所需时间显著减少。此外，在GPT提示不可用的情况下，实验组学生最初正确解决作业的可能性较低，表明可能存在对GPT生成反馈的过度依赖。然而，实验组学生能够较快纠正错误，经过七次提交尝试后正确率与其他组持平。GPT提示的可用性未对学生情感状态产生显著影响。