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会遇到人工智能生成的输出——无论是主动寻求帮助还是通过集成工具获得。对人工智能的信任会影响学生如何解读和运用这些输出，包括他们是进行批判性评估还是表现出过度依赖。我们研究了学生在编程问题解决任务中对人工智能助手的信任如何影响其合理依赖行为，以及这一关系是否因学习者特征而异。在432名本科生参与的研究中，学生需完成Python输出预测题，同时接收来自人工智能聊天机器人的建议与解释（包括准确和故意误导性建议）。我们将依赖行为操作化为学生回应反映其对人工智能助手建议的合理使用的程度——正确时接受、错误时拒绝。任务前后通过问卷评估了对助手的信任、人工智能素养、认知需求、编程自我效能感和编程素养。结果显示，信任与合理依赖呈非线性关系：较高信任度与较低合理依赖相关联，表明学生对正确与错误建议的区分能力减弱。这一关系受到学生人工智能素养和认知需求的显著调节。这些发现凸显了未来需研究如何通过教学和系统支持，鼓励学生在解决问题时对人工智能辅助进行更反思性的评估。