Rethinking AI in Education: Highlighting the Metacognitive Challenge
Open accessAbstract
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.
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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)
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References 29 · Cited by 31
Research works printed but not linked: 5
- Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231-236). Routledge.
- Lévi-Strauss, C. (1966). The savage mind. University of Chicago Press.
- Polanyi, M. (2009). The tacit dimension. In L. Prusak (Ed.), Knowledge in organizations (pp. 135-146). Routledge
- 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.
- 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
- Publisher
- Asociatia EduSoft
- 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
- gold
- doi.org
- Licence
- 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
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