As generative AI systems are integrated into educational settings, students often encounter AI-generated output while working through learning tasks, either by requesting help or through integrated tools. Trust in AI can influence how students interpret and use that output, including whether they evaluate it critically or exhibit overreliance. We investigate how students' trust relates to their appropriate reliance on an AI assistant during programming problem-solving tasks, and whether this relationship differs by learner characteristics. With 432 undergraduate participants, students' completed Python output-prediction problems while receiving recommendations and explanations from an AI chatbot, including accurate and intentionally misleading suggestions. We operationalize reliance behaviorally as the extent to which students' responses reflected appropriate use of the AI assistant's suggestions, accepting them when they were correct and rejecting them when they were incorrect. Pre- and post-task surveys assessed trust in the assistant, AI literacy, need for cognition, programming self-efficacy, and programming literacy. Results showed a non-linear relationship in which higher trust was associated with lower appropriate reliance, suggesting weaker discrimination between correct and incorrect recommendations. This relationship was significantly moderated by students' AI literacy and need for cognition. These findings highlight the need for future work on instructional and system supports that encourage more reflective evaluation of AI assistance during problem-solving.

翻译：随着生成式人工智能系统融入教育环境，学生在完成学习任务时常常遇到AI生成的输出，无论是通过主动请求帮助还是通过集成工具。对AI的信任会影响学生如何解读和使用这些输出，包括他们是否进行批判性评估或表现出过度依赖。我们研究了学生在编程问题解决任务中对AI助手的信任与其适当依赖之间的关系，以及这种关系是否因学习者特征而异。在432名本科生的参与下，学生完成Python输出预测问题，同时接收来自AI聊天机器人的建议和解释，包括准确和故意误导性的建议。我们将依赖行为化操作定义为学生回应在多大程度上反映了对AI助手建议的适当使用——当建议正确时接受，错误时拒绝。任务前后调查评估了对助手的信任、AI素养、认知需求、编程自我效能感和编程素养。结果显示一种非线性关系：更高的信任与更低的适当依赖相关，表明对正确与错误建议的辨别能力较弱。这种关系受到学生的AI素养和认知需求的显著调节。这些发现突出了未来研究在指导和系统支持方面的必要性，以鼓励在问题解决过程中对AI辅助进行更具反思性的评估。