Rethinking AI in Education: Highlighting the Metacognitive Challenge
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.
Authors
- Ilya Levin (ORCID: https://orcid.org/0000-0002-0298-4547)
- Michal Marom
- Andrei Kojukhov
Institutions
- Holon Institute of Technology (IL)
- Tel Aviv University (IL)
Publication Details
- Journal
- BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
- Published
- 2025-04-06
- DOI
- https://doi.org/10.70594/brain/16.s1/21
- Citations
- 11
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 18.63