AI Tools Scaffolding Metacognition in STEM

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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

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

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

Related Pages

• engagement-forecasting-its — Feature-based engagement forecasting reduces MAE 22-33% vs heuristics; effort dr

• learnmate2-llm-adaptive-learning — Metacognitive support through adaptive system design

• computational-thinking-ai-agent-creation — Tailoring AI support to student readiness levels

• metacognition — Core construct: planning, monitoring, evaluation of cognition

• intelligent-tutoring-systems — ITS as scaffolding tool for metacognitive development

• adaptive-learning-systems — Adaptive platforms (ALEKS, Carnegie Learning)

• ai-literacy — Teacher preparation in AI tools and metacognitive scaffolding

• human-in-the-loop-ai — Human-centered paradigm: AI as supportive tool

• learning-analytics — Dashboards and tracking for metacognitive externalization

• posthumanist-ai-education — Emerging paradigm: AI as co-regulator (stub needed)

• tutoring-specific-vs-general-ai — Domain-specific tutoring vs. generic chatbots

• metacognitive-awareness-experiential-vs-instructional — Experiential Versus Instructional Approaches for Eliciting Metacognitive Awarene

Sources

• 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 (2005–2025). Journal of Intelligence, 13(1), 148. https://doi.org/10.3390/jintelligence13110148