Cited by 31Artificial Intelligence in EducationInnovative Teaching and Learning MethodsArtificial Intelligence in Healthcare and Education

Rethinking AI in Education: Highlighting the Metacognitive Challenge

Open access
Levin, Ilya, Marom, Michal, Kojukhov, Andrei·2025·BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE

Abstract

Generative Artificial Intelligence (GenAI) is revolutionising education, but in doing so, it is often seen simply as a tool for automation and efficiency. This paper challenges this narrow perspective, arguing that GenAI’s transformative potential lies in fostering Meta-AI skills—specialised metacognitive competencies for engaging AI as a cognitive partner. Our objective is to explore how GenAI reshapes learning environments and necessitates a pedagogical shift, hypothesising that traditional education paradigms are insufficient without Meta-AI skills to navigate GenAI’s dynamic outputs. Drawing on constructionist theory, we conceptually analyse seven phenomena: learners’ perceptions, reasoning transitions, scientific research paradigms, information processing shifts, tacit knowledge articulation, multimodal interactions, and early AI education.. The methodology involves synthesising theoretical and empirical literature to frame Meta-AI skills as essential for modern learning. Key results reveal that GenAI transforms education into interactive, exploratory spaces, shifting from symbolic to indexical processing and generalisation-based to situated reasoning. Multimodal GenAI enhances metacognitive awareness, while tacit knowledge exploration deepens self-reflection—outcomes requiring Meta-AI skills beyond conventional metacognition. Practically, educators can integrate these skills into curricula through scaffolded prompt engineering (e.g., refining AI queries), multimodal projects (e.g., creating cross-media narratives), and critical evaluation of AI outputs, empowering students to leverage GenAI effectively. We conclude that GenAI’s integration demands a reevaluation of pedagogy, with Meta-AI skills bridging theoretical shifts and practical applications.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.70594/brain/16.s1/21

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Few studies directly measure how students monitor, evaluate, and regulate their learning when interacting with ChatGPT.

    “Although metacognition is often invoked in discussions of AI-supported learning, it is rarely treated as a primary object of empirical investigation [53].Few studies directly measure how students monitor, evaluate, and regulate their learning when interacting with ChatGPT.”

    From Fluency Illusion: A Review on Influence of ChatGPT in Classroom Settings (2026) · cites this paper as [53]

    The Introduction calls for assessing students’ metacognitive engagement with generative AI, but it does not say that empirical studies rarely make metacognition their primary focus or that few directly measure students’ monitoring, evaluation, and regulation while using ChatGPT. Searches for empirical measurement and a research gap found no passage establishing that scarcity claim; the Introduction also describes a growing body of related research. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 29 · Cited by 31

Research works printed but not linked: 5

  1. Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231-236). Routledge.
  2. Lévi-Strauss, C. (1966). The savage mind. University of Chicago Press.
  3. Polanyi, M. (2009). The tacit dimension. In L. Prusak (Ed.), Knowledge in organizations (pp. 135-146). Routledge
  4. Touretzky, D. S., & Gardner-McCune, C. (2022). Artificial intelligence thinking in K-12. In S.-C. Turkle, S. (2005). The second self: Computers and the human spirit (20th anniversary ed.). MIT Press.
  5. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304).

Authors
Levin, I., Marom, M., & Kojukhov, A.
Affiliations
  • Holon Institute of Technology (IL)
  • Tel Aviv University (IL)
Venue
BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
Published
2025-07-29
Volume
16
Issue
1, Sup. 1
Pages
250-263
ISSN
  • 2068-0473 (ISSN-L)
  • 2067-3957
OpenAlex
W4409198262
Cited by
31
References
29
Citations by year
2026: 25 · 2025: 6
FWCI
32.09
Type
journal article
Language
en
Open access
Locations
BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI in education, metacognition, AI as cognitive partner, constructionism, tacit knowledge, situated reasoning, prompt engineering, artificial intelligence in education, pedagogical shift, self-reflection

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review