Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI
- URL: http://arxiv.org/abs/2604.25648v1
- Date: Tue, 28 Apr 2026 13:45:17 GMT
- Title: Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI
- Abstract summary: We propose a unified framework integrating behavioral, computational, and psychoeducational dimensions.<n>We evaluate interventions designed to enhance curiosity in classroom settings.<n>We address the paradigm shift introduced by Generative AI.
- Score: 12.130550783674364
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Informal Learning Emerges in Everyday Human-LLM Interaction [74.53081471113929]
We show that everyday human-LLM interaction contains measurable, selective and conditionally organized behavioural signatures of informal learning.<n>They shift AI evaluation from answer-delivery efficiency toward the preservation of cognitive opportunities for users to reason, test ideas and construct understanding.
arXiv Detail & Related papers (2026-07-20T07:56:41Z) - Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment [17.265229093607676]
We argue that disrupting cognitive passivity requires more dynamic and adaptive strategy through cognitive alignment.<n>In the framework, we provide the mapping between AI's interaction mode (transmissive or deliberative) and users' cognitive demand (receptive or deliberative)
arXiv Detail & Related papers (2026-04-03T06:47:55Z) - Who Is Doing the Thinking? AI as a Dynamic Cognitive Partner: A Learner-Informed Framework [0.0]
This study proposes a framework positioning AI as a dynamic cognitive partner whose function shifts across learning situations.<n>We identified nine interrelated dimensions through which learners described AI as partnering with their cognition.<n>Students distinguished between productive support that extends understanding and unproductive reliance that replaces cognitive effort, indicating situational awareness of when AI should and should not be used.
arXiv Detail & Related papers (2026-02-17T15:07:06Z) - From Individual Prompts to Collective Intelligence: Mainstreaming Generative AI in the Classroom [2.7886031629181987]
We argue for a shift toward collective intelligence (CI)-focused pedagogy, where GenAI acts as a catalyst for peer-to-peer learning.<n>We implement Generative CI activities in two undergraduate engineering courses, engaging 140 students through thinking routines.<n>Results demonstrate that students value the combination of human collaboration with strategic AI support.
arXiv Detail & Related papers (2026-01-07T08:28:20Z) - When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
arXiv Detail & Related papers (2025-06-05T20:48:16Z) - The Superalignment of Superhuman Intelligence with Large Language Models [63.96120398355404]
We discuss the concept of superalignment from the learning perspective to answer this question.<n>We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation.<n>We present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing.
arXiv Detail & Related papers (2024-12-15T10:34:06Z) - Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism.
We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
arXiv Detail & Related papers (2024-03-27T05:10:38Z) - Causal Reinforcement Learning: A Survey [57.368108154871]
Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty.
One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world.
Causality offers a notable advantage as it can formalize knowledge in a systematic manner.
arXiv Detail & Related papers (2023-07-04T03:00:43Z) - A Novel Multimodal Approach for Studying the Dynamics of Curiosity in
Small Group Learning [2.55061802822074]
We propose an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states.
We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions.
This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts.
arXiv Detail & Related papers (2022-04-01T16:12:40Z) - From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven
Learning in Artificial Intelligence Tasks [56.20123080771364]
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition.
In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning.
CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
arXiv Detail & Related papers (2022-01-20T17:07:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.