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AI Tools Scaffolding Metacognition in STEM

● high · 2026-05-08metacognition, stem-education, learning-analytics, intelligent-tutoring, posthumanist, ai-literacy, human-in-the-loop-ai, posthumanist-ai-education, adaptive-learning-systems, rag

A bibliometric–systematic review of AI tools in STEM education:

Systematic review (2005–2025) mapping how AI tools scaffold and co-regulate metacognitive development in STEM classrooms through bibliometric and qualitative synthesis.

Scope and Methodology

Tsakeni et al. (2025) conducted a bibliometric–systematic review of AI tools in STEM education:

  • Dataset: 135 peer-reviewed articles (2005–2025) from Scopus and Web of Science
  • Core studies: 24 studies selected via PRISMA 2020 protocols for in-depth review
  • Methods: Bibliometric mapping (Biblioshiny, VOSviewer) + qualitative thematic synthesis
  • Theoretical frameworks: Flavell's metacognition theory, General Systems Theory, Human-Centered vs. Posthumanist paradigms

    • Key Findings

    1. Theoretical Frameworks Evolution

    ParadigmCore IdeaAI Role
    Human-Centered (traditional)AI supports human agency with ethical oversightSupportive tool under teacher control
    Posthumanist (emerging)Learning = co-regulated process distributed between humans and AIAI as co-agent in reflective processes

    Finding: While most research remains grounded in human-centered conceptualizations, there are emerging indications of posthumanist framings where AI systems are positioned as co-regulators of learning.

    2. AI Tools Scaffolding Metacognition

    Tool CategoryExamplesMetacognitive Function
    Intelligent Tutoring Systems (ITS)Carnegie Learning, ALEKSPersonalized feedback, real-time monitoring, strategic prompts
    Adaptive PlatformsDeep RL-based systemsEnhance metacognitive outcomes via adaptive scaffolds
    Learning AnalyticsDashboards, tracking toolsExternalize metacognitive processes, support reflection
    Generative AIChatGPT (GPT-4.1, 4.5, 5), conversational agentsMetacognitive prompts, chatbot feedback, ME-CoT approaches

    Core shift: From individual reflection → system-level regulation and distributed cognition.

    3. Metacognitive Outcomes

    Studies consistently show AI tools improve:

  • Reflective thinking and independent inquiry
  • Self-regulation and strategic monitoring (planning, monitoring, evaluation)
  • Motivation and reasoning (especially for low-achieving and underrepresented groups)
  • Academic performance in mathematics, science, chemistry, physics

    • 4. Ethical Concerns and Pitfalls

    Despite benefits, scholars warn of:

  • Cognitive overload from excessive AI-generated feedback
  • Reduced learner autonomy when AI replaces (not scaffolds) reflection
  • Algorithmic bias if AI prioritizes automation over deep reflection
  • Inconsistent measurement methods across studies (theoretical fragmentation)

    • Connection to Existing Work

    vs. Metacognition in AI Education

  • This review maps the field systematically (135 publications, 24 core studies)
  • Confirms: metacognition is central to STEM success but challenging to foster
  • AI tools offer scalable scaffolding, but require teacher preparation in AI literacy

    • vs. Adaptive Learning Systems

  • ALEKS, Carnegie Learning highlighted as successful adaptive platforms for metacognition
  • Deep reinforcement learning enhances metacognitive outcomes (vs. static adaptive rules)
  • Aligns with: system-level regulation > individual reflection

    • vs. Intelligent Tutoring Systems

  • ITS identified as key scaffolding tool for metacognitive development
  • Combines content mastery (object level) with reflective monitoring (meta level)
  • Consistent with: Tutoring Specific Vs General AI — domain-specific tutoring outperforms generic chatbots

    • vs. AI Literacy

  • Critical finding: AI literacy must be integrated into teacher preparation
  • Teachers need skills to: select tools, interpret analytics, maintain ethical oversight
  • Aligns with: human-centered paradigm (AI as tool, teacher as agentic decision-maker)

    • The Posthumanist Turn

    The review identifies an emerging paradigm shift:

    Human-Centered (Traditional):

    ```

    Teacher → AI Tool → Student

    (AI as instrument)

    ```

    Posthumanist (Emerging):

    ```

    Teacher ↔ AI System ↔ Student

    (Learning as distributed, co-regulated process)

    ```

    Implications:

  • AI systems as co-regulators (not just tools)
  • System-level effects (not just individual cognition)
  • Challenges the human–machine binary in education

    • Implications for AI in Education

    For Researchers

  • Theoretical integration needed: Flavell + General Systems Theory + posthumanist perspectives
  • Measurement standardization: Inconsistent methods hinder meta-analysis
  • Longitudinal studies: Most research is cross-sectional; need to track long-term metacognitive development

    • For Educators

  • AI literacy is prerequisite: Teachers must understand metacognitive scaffolding to select tools effectively
  • Balance automation with reflection: AI should enhance (not replace) metacognitive processes
  • Leverage multiple tools: ITS + learning analytics + generative AI for comprehensive scaffolding

    • For Tool Developers

  • Ethical design priority: Avoid cognitive overload and reduced autonomy
  • Metacognitive scaffolds: Build in reflection prompts, self-monitoring dashboards
  • Transparency: Teachers need to understand AI decision-making to maintain pedagogical agency

    • Connected Concepts

  • Metacognition
  • Adaptive Learning
  • Intelligent Tutoring
  • AI Literacy

    • Connected Articles

  • Tutoring Specific Vs General AI

    • Citation

    Tsakeni, M., Nwafor, S. C., Mosia, M., & Egara, F. O. (2025). Mapping the Scaffolding of Metacognition and Learning by AI Tools in STEM Classrooms: A Bibliometric–Systematic Review Approach. Journal of Intelligence.