PERSPECTIVE article
Front. Educ.
Sec. Digital Learning Innovations
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1697554
This article is part of the Research TopicArtificial Intelligence in Educational Technology: Innovations, Impacts, and Future DirectionsView all 6 articles
The Cognitive Mirror: A Framework for AI-Powered Metacognition and Self-Regulated Learning
Provisionally accepted
- 1Ritsumeikan Global Innovation Research Organization, Ritsumeikan Daigaku, Kyoto, Japan
- 2ImpactLab, Shiga, Japan
- 3Department of Biotechnology, College of Life Sciences, Ritsumeikan Daigaku, Kyoto, Japan
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Introduction: The dominant paradigm of generative artificial intelligence (AI) in education positions it as an omniscient oracle, a model that risks hindering genuine learning by fostering cognitive offloading. Objective: This study proposes a fundamental shift from “AI as Oracle” model to a “Cognitive Mirror” paradigm, which reconceptualizes AI as a teachable novice engineered to reflect the quality of a learner’s explanation. The core innovation is the repurposing of AI safety guardrails as didactic mechanisms to deliberately sculpt AI’s ignorance, creating a “pedagogically useful deficit.” This conceptual shift enables a detailed implementation of the “learning by teaching” principle. Method: Within this paradigm, a framework driven by a Teaching Quality Index is introduced. This metric assesses the learner’s explanation and activates an instructional guidance level to modulate the AI’s responses, from feigning confusion to asking clarifying questions. Results: Grounded in learning science principles, such as the Protégé Effect and Reflective Practice, this approach positions the AI as a metacognitive partner. It may support a shift from knowledge transfer to knowledge construction, and a re-orientation from answer correctness to explanation quality in the contexts we describe. Conclusion: By re-centering human agency, the “Cognitive Mirror” externalizes the learner’s thought processes, making their misconceptions objects of repair. This study discusses the implications on assessment, addresses critical risks, including algorithmic bias, and outlines a research agenda for a symbiotic human-AI coexistence that promotes effortful work at the heart of deep learning.
Keywords: Teachable agents, reflective practice, Role inversion, Instructional guidance level, Instructional scaffolding, Knowledge scope control, Protégé effect
Received: 02 Sep 2025; Accepted: 18 Sep 2025.
Copyright: © 2025 Tomisu, Ueda and Yamanaka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hayato Tomisu, tomisu@fc.ritsumei.ac.jp
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