Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI

TL;DR

This chapter proposes a unified framework linking curiosity and metacognition as key to autonomous learning, evaluates educational interventions, and discusses AI's role in curiosity-driven learning.

Contribution

It introduces an integrated framework for understanding curiosity and metacognition, reviews educational strategies, and explores AI's impact on curiosity in learning.

Findings

Educational interventions show mixed results in enhancing curiosity.

Tailored approaches are needed for different learner profiles.

AI can both support and hinder curiosity-driven learning.

Abstract

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.