This chapter examines the relationship between curiosity and metacognition as critical drivers of autonomous and self-regulated learning. We synthesize recent research to propose a unified framework integrating behavioral, computational, and psychoeducational dimensions, arguing that curiosity, i.e. the intrinsic drive to acquire new knowledge, relies fundamentally on metacognitive monitoring and control. From an educational perspective, we evaluate interventions designed to enhance curiosity in classroom settings. While promising, our review indicates that these interventions yield mixed results, often proving differentially effective for struggling learners, thereby underscoring the necessity for approaches tailored to individual profiles. Finally, we address the paradigm shift introduced by Generative AI. While Large Language Models (LLMs) offer unprecedented scalability for personalized inquiry, we argue that their default interaction modes pose significant risks to the dynamics of curiosity-driven learning. To mitigate these challenges, we review strategies to transform AI from a potential cognitive shortcut into a powerful partner for sustained epistemic development.

翻译：本章探讨了好奇心与元认知作为自主和自我调节学习的关键驱动力之间的关系。我们综合近期研究，提出一个统一框架，整合行为、计算和心理教育维度，论证好奇心（即获取新知识的内在动力）根本上依赖于元认知的监控与控制。从教育视角出发，我们评估了旨在提升课堂环境中好奇心的干预措施。尽管前景可观，我们的综述表明这些干预效果不一，往往对学习困难者效果差异显著，从而凸显了根据个体特征制定针对性方法的必要性。最后，我们探讨了生成式人工智能带来的范式转变。虽然大型语言模型（LLM）为个性化探究提供了前所未有的可扩展性，但我们认为其默认交互模式对好奇心驱动学习的动态机制构成了重大风险。为缓解这些挑战，我们综述了将人工智能从潜在的认知捷径转变为持续性知识发展的强大伙伴的策略。