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接触到人工智能生成的输出，这既可能是通过主动请求帮助，也可能是通过集成工具实现。对人工智能的信任会影响学生如何解读和利用这些输出，包括他们是否会对其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助手的信任如何与其合理依赖行为相关联，以及这种关系是否因学习者特征而异。在432名本科生参与的实验中，学生通过接收AI聊天机器人提供的建议和解释（包括准确和故意误导性的建议）来完成Python输出预测问题。我们将依赖行为操作化为学生回答反映对AI助手建议合理使用程度的指标——当建议正确时接受，错误时拒绝。任务前后的调查评估了学生对AI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，信任与合理依赖之间存在非线性关系，即更高信任度与更低合理依赖相关，表明对正确和错误建议的辨别力减弱。这种关系显著受到学生的人工智能素养和认知需求的调节。这些发现强调了未来需要开发教学和系统支持，以鼓励学生在问题解决过程中对AI辅助进行更反思性的评估。