The rapid adoption of generative artificial intelligence (GenAI) in schools raises concerns about students' uncritical reliance on its outputs. Effective use of large language models (LLMs) requires not only technical knowledge but also the ability to monitor, evaluate, and regulate one's interaction with the system, processes closely tied to metacognitive regulation. These skills are still developing in middle school, making students particularly vulnerable to over-trust and premature acceptance of AI outputs. Because classroom time and teacher training resources are constrained, there is a pressing need to develop and evaluate AI literacy interventions that can be implemented under realistic school conditions. We report a controlled classroom study examining whether a two-hour AI literacy workshop improves students' interaction strategies and quality of final answers in LLM-supported science problem solving. A total of 116 students (grades 8-9; ages 13-15) completed six science investigation tasks using a generative AI system. Two days prior, the intervention group attended the workshop, which combined information about how LLMs work and fail with practical guidance on prompting and response evaluation; the control group received no training. Trained students showed less uncritical reliance on the system: they more often reformulated queries, asked follow-up questions, and more accurately judged response correctness, leading to better performance. In contrast, GenAI and metacognitive self-report scores did not predict performance, suggesting that effective use of generative AI depends less on self-reported measures and more on explicit training in interaction regulation. Overall, the results show that brief, scalable AI literacy instruction can meaningfully improve how middle-school students use generative AI in school-like learning activities.

翻译：生成式人工智能（GenAI）在学校中的快速普及引发了人们对学生不加批判地依赖其输出的担忧。有效使用大型语言模型（LLMs）不仅需要技术知识，还需要监控、评估和调节与系统交互的能力，这一过程与元认知调节紧密相关。这些技能在中学阶段尚在发展之中，使得学生特别容易过度信任并过早接受AI的输出结果。由于课堂时间和教师培训资源有限，迫切需要开发并评估能够在现实学校条件下实施的AI素养干预措施。我们报告了一项受控的课堂研究，考察两小时的AI素养工作坊能否改善学生在LLM支持的科学问题解决中的交互策略和最终答案质量。共计116名（8-9年级；13-15岁）学生使用生成式AI系统完成了六项科学探究任务。两天前，干预组参加了工作坊，该工作坊结合了关于LLM工作原理及缺陷的信息，以及关于提示和回答评估的实用指导；对照组未接受培训。受过培训的学生对系统的批判性依赖有所降低：他们更频繁地重新表述查询、提出后续问题，并更准确地判断回答的正确性，从而获得了更好的表现。相比之下，GenAI和元认知的自我报告分数未能预测表现，这表明有效使用生成式AI更少依赖于自我报告测量，而更多依赖于交互调控的明确培训。总体而言，结果表明简短、可扩展的AI素养教学能够切实改善中学生在校内类似学习活动中使用生成式AI的方式。