CORTEXA
← Browse
openalexFrontiers in Psychology2026-07-24Cited by 0

The System 3 Method: integrating intuition, reason and creative metacognition for deep learning

Juan Carlos Chávez-Autor

Educational psychology increasingly needs frameworks that connect deep learning, generative learning, learner agency, creative metacognition, and metacognitive control into a coherent account of how students learn. This Conceptual Analysis introduces the System 3 Method, a pedagogical framework in which learners are guided through iterative cycles that integrate intuitive salience and rational articulation under implicit and explicit metacognitive control. The article clarifies that System 3 is not an anatomical module and not a synonym for metacognition. It is defined as a functional coordination construct: the regulated coupling through which learners shift among intuitive orientation, generative construction, rational verification, integration, and transfer. The method translates this construct into GRIM T : Generation, Reflection, Integration/Iteration, Metacontrol, and Time. It further proposes NUD—novelty, usefulness, and diversity—as a complementary profile for evaluating creative learning trajectories alongside academic mastery, with novelty understood primarily as learner-relative mini-c creativity. The revised analysis situates the method more explicitly in relation to self-regulated learning, ICAP, generative learning, curiosity-driven conceptual change, knowledge integration, and learner-centered pedagogies. It also provides a worked instructional example, clarifies developmental and domain-specific boundary conditions, and distinguishes content-relevant meaningfulness from merely interesting or seductive details. Technology and artificial intelligence are discussed as possible generative scaffolds, adaptive critics, metacognitive mirrors, and process memories only when a human gate preserves authorship, verification, persistence, and transfer. The central contribution is a falsifiable framework for a pedagogy of creative metacognition: education should train learners not only to know more, but to participate more intelligently in constructing, evaluating, integrating, and transforming what they know.

View free PDFSource page

Related papers

crossrefFrontiers in Psychology2026-07-02

The determination of human creative thinking by employing machine learning classification on EEG signals

Jilin Zou, Fang Yuan, Silin Zhou, Jiaqin Yang, Chunlei Liu

Background and objective Traditional creativity assessments are limited by subjectivity and high labor costs. Although machine learning (ML) offers objective alternatives, its application to EEG-based creativity evaluation remains scarce. This study aimed to classify high and low…

View free PDFSource page
openalexFrontiers in Psychology2026-07-24

Designing a precision career-guidance model based on student psychological profiling in higher education

Ce Wang

Introduction In the rapidly evolving landscape of higher education, the demand for personalized and data driven career guidance has become increasingly critical. Traditional career counseling methods often rely on standardized assessments and static recommendations, which fail to…

View free PDFSource page
crossrefFrontiers in Psychology2026-07-03

Cognitive load, prior knowledge, and sustained learning intention in a generative-AI-supported digital cultural learning context

Hongqing Huang, Qiongxue Zhao, Buling Xia

Background Generative artificial intelligence is increasingly embedded in learning as a source of explanation, feedback, and interactive support. However, it remains unclear how such support shapes learners' cognitive processing and their subsequent willingness to continue learni…

View free PDFSource page
openalexFrontiers in Psychology2026-07-24

Cognitive encoding modeling of musical sequences for vocal performance and intelligent music composition

Shaojie Lin, Guang Zeng

Introduction The cognitive encoding of musical sequences is a complex process that involves capturing the intricate structure, temporal dynamics, and inherent uncertainties of musical data. Traditional methods often struggle to preserve the non-Euclidean geometric properties of m…

View free PDFSource page
crossrefFrontiers in Psychology2026-07-09

Divergent generative AI pathways in higher education: a parallel mediation analysis of autonomous use and human–machine synergy

Yan Cheng, Xinran Liu, Haibo Liu

Introduction The integration of Generative Artificial Intelligence (GenAI) into higher education has raised important questions regarding its influence on student learning outcomes. Drawing on Self-Regulated Learning (SRL) theory, this study examines how learning motivation (LM)…

View free PDFSource page
openalexFrontiers in Psychology2026-07-23

Mechanisms and outcomes of flipped music education: a conceptual framework linking self-directed learning, collaboration, and student wellbeing

Qisen Zhu, Li Jinglong

Flipped music education has increasingly emerged as a learner-centered instructional approach that promotes active participation, self-regulated learning, and collaborative engagement. In music theory education, students often encounter difficulties in understanding abstract conc…

View free PDFSource page