Automated feedback generation plays a crucial role in enhancing personalized learning experiences in computer science education. Among different types of feedback, next-step hint feedback is particularly important, as it provides students with actionable steps to progress towards solving programming tasks. This study investigates how students interact with an AI-driven next-step hint system in an in-IDE learning environment. We gathered and analyzed a dataset from 34 students solving Kotlin tasks, containing detailed hint interaction logs. We applied process mining techniques and identified 16 common interaction scenarios. Semi-structured interviews with 6 students revealed strategies for managing unhelpful hints, such as adapting partial hints or modifying code to generate variations of the same hint. These findings, combined with our publicly available dataset, offer valuable opportunities for future research and provide key insights into student behavior, helping improve hint design for enhanced learning support.

翻译：自动反馈生成在增强计算机科学教育中的个性化学习体验方面发挥着至关重要的作用。在不同类型的反馈中，下一步提示反馈尤为重要，因为它为学生提供了可操作的步骤，以推进编程任务的解决。本研究探讨了学生在集成开发环境（IDE）学习环境中如何与AI驱动的下一步提示系统进行交互。我们收集并分析了来自34名学生在解决Kotlin任务时的数据集，其中包含详细的提示交互日志。我们应用了过程挖掘技术，识别出16种常见的交互场景。对6名学生的半结构化访谈揭示了处理无效提示的策略，例如调整部分提示或修改代码以生成同一提示的变体。这些发现，结合我们公开可用的数据集，为未来研究提供了宝贵的机会，并提供了对学生行为的关键洞察，有助于改进提示设计以增强学习支持。