Journal of Intelligence

In psychology, small sample sizes are a frequent challenge—particularly when studying specific expert populations or using complex and cost-intensive methods like human scoring of creative answers—as they reduce statistical power, bias results, and limit generalizability. They also hinder the use of frequentist confirmatory factor analysis (CFA), which depends on larger samples for reliable estimation. Problems such as non-convergence, inadmissible parameters, and poor model fit are more likely. In contrast, Bayesian methods offer a robust alternative, being less sensitive to sample size and allowing the integration of prior knowledge through parameter priors. In the present study, we introduce small-sample-size structural equation modeling to creativity research by investigating the relationship between creative fluency and nested creative cleverness with right-wing authoritarianism, starting with a sample size of N = 198. We compare the stability of results in frequentist and Bayesian SEM while gradually reducing the sample by n = 25. We find that common frequentist fit indexes degrade below N = 100, while Bayesian multivariate Rhat values indicate stable convergence down to N = 50. Standard errors for fluency loadings inflate 40–50% faster in frequentist SEM compared to Bayesian estimation, and regression coefficients linking RWA to cleverness remain significant across all reductions. Based on these findings, we discuss (1) the critical role of Bayesian priors in stabilizing small-sample SEM, (2) the robustness of the RWA-cleverness relationship despite sample constraints, and (3) practical guidelines for minimum sample sizes in bifactor modeling.

This article presents and validates the Metacognitive Knowledge Intervention for Thinking (MKIT)—an educational framework designed to assess and develop domain-general metacognitive knowledge (MK) in children aged 5 to 9. Moving beyond traditional approaches that examine metacognition within isolated subject areas, this research reconceptualizes MK as a transferable learning resource across content domains and developmental stages. Moreover, by employing a stepped-wedge design—a rigorous but rarely used approach in education—the study introduces a methodological advancement. Simultaneously, MK is operationalized through an ecologically valid and developmentally appropriate format, using visually engaging stories, illustrated scenarios, and interactive tasks integrated within classroom routines. These adaptations enabled young learners to engage meaningfully with abstract metacognitive concepts. Therefore, across three interconnected studies (N = 458), the MKIT provided strong psychometric evidence supporting valid inferences about metacognitive knowledge, age-invariant effects, and substantial gains among children with initially low MK levels. In addition, qualitative data indicated MK transfer across contexts. Thus, these findings position MKIT as a scalable tool, supported by multiple strands of validity evidence, that makes metacognitive knowledge teachable across domains—offering a practical approach to strengthening learning, reducing early achievement gaps, and supporting the development of core components of intelligence.

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.