Actionable companion · Feedback & scaffolding

Learning with agency: Mapping the Scaffolding of Metacognition and Learning by AI Tools in STEM Classrooms: A Bibliometric–Systematic Review Approach (2005–2025)

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract This study comprehensively analyses how AI tools scaffold and share metacognitive processes, thereby facilitating students’ learning in STEM classrooms through a mixed-method research synthesis combining bibliometric analysis and systematic review. Using a convergent parallel mixed-methods design, the study draws on 135 peer-reviewed articles published between 2005 and 2025 to map publication trends, author and journal productivity, keyword patterns, and theoretical frameworks. Data were retrieved from Scopus and Web of Science using structured Boolean searches and analysed using Biblioshiny and VOSviewer. Guided by PRISMA 2020 protocols, 24 studies were selected for in-depth qualitative review. Findings show that while most research remains grounded in human-centred conceptualisations of metacognition, there are emerging indications of posthumanist framings, where AI systems are positioned as co-regulators of learning. Tools like learning analytics, intelligent tutoring systems, and generative AI platforms have shifted the discourse from individual reflection to system-level regulation and distributed cognition. The study is anchored in Flavell’s theory of metacognition, General Systems Theory, and posthumanist perspectives to interpret this evolution. Educational implications highlight the need to reconceptualise pedagogical roles, integrate AI literacy in teacher preparation, and prioritise ethical, reflective AI design. The review provides a structured synthesis of theoretical, empirical, and conceptual trends, offering insights into how human–machine collaboration is reshaping learning by scaffolding and co-regulating students’ metacognitive development in STEM education. Keywords: artificial intelligence, educational technology, metacognition, intelligent tutoring systems, posthumanism, STEM education

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Bibliometric / mapping study

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study adopted a convergent parallel mixed-method research synthesis design, combining bibliometric analysis with a systematic review. Both components were conducted independently but addressed complementary aspects of the research questions. The bibliometric analysis provided a quantitative map of trends, conceptual clusters, and publication networks spanning the period from 2005 to 2025. In parallel, the systematic review offered qualitative insights into how theoretical frameworks (including posthumanist paradigms) were employed in studies that integrated AI tools with metacognitive strategies in STEM classrooms. Findings from both strands were integrated during interpretation, allowing for cross-validation and thematic synthesis of patterns emerging from both data sources.

Limitations and uncertainty · source passage

This study contributes to the understanding of how artificial intelligence (AI) tools intersect with metacognition in STEM education by combining bibliometric and systematic review approaches. The analysis demonstrates that publications in this field have increased steadily, with a growing shift toward integrating AI-supported systems such as intelligent tutoring, adaptive feedback, and learning analytics into instructional design. Most of the reviewed studies focused on mathematics and science education, reflecting the dominance of these disciplines in AI-assisted metacognitive research. Conceptually, the review identified that human-centred paradigms continue to guide most AI-metacognition studies. At the same time, only a few papers adopt posthumanist or hybrid perspectives that view AI as a cognitive partner rather than a tool. Despite these advances, methodological, theoretical, and ethical limitations remain. Few studies employed longitudinal or experimental research designs capable of identifying causal or sustained effects of AI-mediated metacognitive interventions. Research also remains uneven across STEM domains, with limited representation in technology and engineering education as well as in early-childhood and teacher-education contexts. Ethical considerations concerning data privacy, transparency, and learner agency are often underexplored, underscoring the need for guiding frameworks that balance innovation with responsibility.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Maria Tsakeni; Stephen C Nwafor; Moeketsi Mosia; Felix O Egara. Mapping the Scaffolding of Metacognition and Learning by AI Tools in STEM Classrooms: A Bibliometric–Systematic Review Approach (2005–2025). Journal of Intelligence (2025-11-15). https://doi.org/10.3390/jintelligence13110148

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.