Generative AI (GenAI) tools allow for effortless task completion, potentially fostering cognitive and metacognitive laziness in students. While surveys indicate widespread GenAI use among students as young as 11, their interactions strategies remain under-explored. A critical indicator of these interactions' quality is the ability to lead Question-Asking (QA) cycles: initiating goal-oriented inquiries, critically evaluating AI responses, and regulating subsequent strategies. While these behaviors predict robust learning in traditional settings, their role in AI-mediated environments remains unclear. Addressing this gap, this study investigates middle school students' (N=63, aged 14--15) capacity to adopt these behaviors with GenAI during science investigation tasks. We analyzed their proficiency in distinguishing efficient goal-oriented prompt from inefficient ones, their critical evaluation of AI responses, and their ability to generate follow-up questions to regulate learning in alignment with their informational needs. Findings reveal a pattern of over-reliance: students struggled to discriminate between prompt types, failed to detect vague AI explanations, and frequently terminated inquiry prematurely, without follow-up. Consequently, task performance remained moderate despite unrestricted AI access and high self-reported prior knowledge. Notably, positive AI attitudes were negatively associated with interaction quality, suggesting a disconnect between perceived and actual competence, whereas higher metacognitive skills predicted superior sensitivity to prompt quality. These results underscore the necessity for AI literacy interventions that move beyond technical understanding to explicitly train metacognitive regulation strategies, required for meaningful and sustainable QA-based learning with GenAI.

翻译：生成式人工智能（GenAI）工具能够轻松完成任务，但可能导致学生认知与元认知层面的惰性。尽管调查显示11岁及以上学生已广泛使用GenAI，但其交互策略仍缺乏深入研究。衡量此类交互质量的关键指标在于引导提问（QA）循环的能力：发起目标导向的探究、批判性评估AI回复、并调控后续策略。虽然这些行为在传统学习环境中预示着扎实的学习成效，但它们在AI中介环境中的作用尚不明确。为填补这一研究空白，本研究探讨了初中生（N=63，年龄14-15岁）在科学探究任务中运用GenAI时采纳这些行为的能力。我们分析了他们区分高效目标导向提示与低效提示的熟练程度、对AI回复的批判性评估能力，以及根据信息需求生成后续问题以调控学习的能力。研究结果揭示了一种过度依赖的模式：学生难以区分提示类型，未能察觉AI的模糊解释，且经常过早终止探究而未提出后续问题。因此，尽管拥有无限制的AI访问权限并自报具备较高的先验知识，学生的任务表现仍处于中等水平。值得注意的是，对AI的积极态度与交互质量呈负相关，表明感知能力与实际能力之间存在脱节；而较高的元认知技能则预示着对提示质量更敏锐的判别力。这些结果强调，AI素养干预必须超越技术理解层面，需明确训练元认知调控策略，这是实现基于QA的生成式人工智能有意义且可持续学习的关键。