Metacognitive Cultural Intelligence in Educational Contexts: A Systematic Review of Psychological Mechanisms and Learning Outcomes

Abstract

This is a systematic review that works out the empirical evidence about metacognitive cultural intelligence (MCQ) to be used as a regulation framework in educational policies in the global context, with particular emphasis on the Chinese settings. After searching five databases (2002-2024) according to PRISMA, 1,142 records were selected, and 62 eligible studies were identified after the screening (κ =.84-.88). The cross-sectional survey designs were the most frequently used ones (66.1%), followed by a small number of longitudinal (9.7%) and experimental (4.8%). The most common mode of analysis (43.5%) was structural equation modelling. There was consistently high measurement reliability (Cronbach’s α reported in 100% of studies; mean α = .86), though cross-cultural measurement invariance was only studied in 29.0 % of studies. In the literature, MCQ was shown to have similar positive correlations with the outcome of learning. Direct effects ranged between β ≈.21 to b =.48 (mean β ≈ .34). Self-regulated learning was found to be the strongest mediator (β ≈ .28 -.47; mediation significant in 73 out of 100 models; n = 26), then intergroup anxiety reduction (β ≈ .19 -.36; 68) and perspective-taking (β ≈ .22 -.41; 61). The most common ones were academic outcomes (e.g., GPA, n = 11), but the socio-emotional and collaborative outcomes were also well-represented. The regional analysis showed that there was some contextual variation in the focus of outcomes, with Chinese studies focusing on academic indicators (52%), Western contexts focused on collaborative outcomes (up to 43% in North America). In general, the data speaks in favor of a cascading regulatory mechanism according to which metacognitive planning, monitoring, affective control, and strategic adaptation lead to more positive academic and collaborative achievements. The field has a high level of conceptual convergence but is still methodologically focused and requires longitudinal, experimental and cross-culturally invariant designs to enhance causal inference.