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

2026-04-28

AI Summary

This study addresses the challenge that generative AI may undermine learners’ intrinsic motivation to inquire by proposing a unified theoretical framework that integrates curiosity and self-regulated learning through a metacognitive lens. Drawing on educational psychology, computational modeling, and large language model interaction design, the research systematically examines the heterogeneous effectiveness of existing interventions and advances individualized instructional strategies that account for learner differences. It reconceptualizes generative AI not as a replacement for inquiry-based learning but as a cognitive partner that supports—rather than supplants—the processes of exploration and discovery. By doing so, the work provides both a theoretical foundation and practical pathways for designing personalized intelligent educational systems that foster autonomous, 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.

Problem

• curiosity
• metacognition
• autonomous learning
• Generative AI
• educational intervention

Innovation

• curiosity
• metacognition
• generative AI
• self-regulated learning
• personalized inquiry