ABSTRACT The study investigated how a culturally grounded 6C instructional model aligns with assessment practices in C‐STEAM education across 30 Ghanaian secondary schools ( N = 720; 600 students, 120 teachers). It was found that integrating local cultural content significantly increased student engagement and ownership. Futhermore, the results showed significant regional differences in Cultural Identity Integration ( F = 7.04, p < 0.001**, η 2 = 0.042), Creativity in Task Completion ( F = 6.23 , p < 0.001**, η 2 = 0.038), and Metacognitive Awareness ( F = 5.82 , p = 0.001 , η 2 = 0.035). School type also influenced outcomes, notably Cultural Identity Integration ( F = 6.38, p = 0.002, η 2 = 0.026) and Creativity ( F = 5.62, p = 0.004**, η 2 = 0.022). Regression models indicated that C‐STEAM exposure strongly predicted Cultural Identity Integration ( β = 0.48, p < 0.001, R 2 = 0.35), Creativity ( β = 0.44, p < 0.001**, R 2 = 0.29), and Metacognitive Awareness ( β = 0.41, p < 0.001, R 2 = 0.26). Correlations among learning outcomes were uniformly positive (MA & AE: r = 0.62**, CC: r = 0.63**, all p < 0.01**). Qualitative findings highlighted that integrating local culture, promoting student agency, reflective learning, and collaborative engagement increased motivation, ownership, and the connection between school learning and community practices. It was recommended that schools should implement performance‐based assessments that directly align with C‐STEAM learning activities to ensure engagement and creativity are accurately evaluated.
ABSTRACT Artificial intelligence (AI) is creating new possibilities for connecting research‐led teaching with authentic industry projects, yet its educational value depends on how educators organize inquiry rather than on technological use alone. This study examined the relationships among AI‐supported research‐led teaching (AIRLT), educator questioning (EQ), educator feedback (EF), student inquiry engagement (SIE), and authentic project learning outcomes (APLO) within Syntegrative Project‐Based Learning. A quantitative cross‐sectional survey was conducted with 898 undergraduate students from 12 universities in Vietnam who had participated in authentic industry or organizational projects involving AI‐supported inquiry and educator guidance. Partial least squares structural equation modeling (PLS‐SEM) showed that AIRLT was positively associated with EQ ( β = 0.642) and EF ( β = 0.611). Both EQ ( β = 0.463) and EF ( β = 0.349) were positively associated with SIE, which, in turn, showed a strong positive association with APLO ( β = 0.689). All six specific indirect effects were significant, indicating statistically significant indirect associations among AI‐supported teaching, educator guidance, student inquiry, and perceived authentic learning outcomes. The findings contribute an educator‐mediated perspective to Syntegrative Education by showing that AI is most educationally meaningful when embedded in purposeful questioning, feedback, and sustained student inquiry. The study highlights the importance of designing AI‐supported industry projects around evidence evaluation, iterative feedback, reflection, and active investigation of authentic problems rather than treating AI use as an educational objective in itself.
ABSTRACT Recruiting, training, and retaining qualified teachers remain matters of public concern in many countries, including Belarus. Drawing on the Systems Theory Framework, this study examined the influences shaping the career development of Chinese language teachers in Belarus across individual, social, and environmental‐societal contexts. Q methodology was employed using a 40‐statement Q set. The study involved 45 teachers, comprising 44 women and one man. The findings revealed four distinct perspectives with limited consensus, reflecting different career orientations: considering alternative career paths, staying in teaching without pursuing leadership, considering leaving the profession, and pursuing school leadership. Salary, workload, professional recognition, and career advancement emerged as important influences on teachers' career decisions. The study offers implications for teacher career development, workforce retention, and policy planning in the context of Chinese language education in Belarus.
ABSTRACT This article consolidates and operationalises SYNAPSE, a human‐centered model that structures learning in four interrelated phases—Sensory Input, Network Adaptation, Participation, and Storage & Embodiment—for education in the age of artificial intelligence (AI). Extending an earlier conference presentation of the model, the article grounds SYNAPSE in a systematic review of 162 scientific sources, positions it against established instructional and self‐regulation frameworks, and translates it into phase‐specific design principles and observable indicators for AI‐augmented learning. Findings from a new exploratory qualitative study with teacher‐education students ( N = 8), who used bounded generative AI within SYNAPSE‐informed lesson sequences, suggest that AI can support reflective engagement and metacognitive self‐regulation when its use is aligned with human learning phases, and underscore the fundamental role of the educator who designs the teaching sequence. The article offers implications for educators, researchers, and developers seeking AI‐mediated learning environments that promote motivation, learner autonomy, and durable knowledge construction.
ABSTRACT In the 21st century, the governance of international student mobility has undergone a fundamental transformation, driven primarily by securitization, geoeconomic competition, and domestic political pressures that conventional market‐based frameworks such as the push‐pull model and human capital theory cannot adequately explain. Drawing on new geopolitics as the theoretical framework and employing interpretive policy analysis as its methodological framework, this study examines how the governing logic of international student mobility has evolved in a global context. The analysis traces five cumulative stages of governing logic from 2000 to 2025, revealing a shift from market expansion and revenue optimization toward securitized scrutiny, conditional regulation, and domestically driven restriction. From this trajectory, three analytical concepts are proposed to capture the emergent patterns of mobility governance: stratification, conditionality, and uncertainty. Stratified mobility illustrates hierarchical differentiation of access by nationality, discipline, and study level. Conditional mobility captures the expanding and constantly shifting regulatory conditions that students must navigate. Uncertain mobility refers to the structural instability generated by contradictory policy signals. These three patterns of mobility are not mutually exclusive but coexist in different proportions across national contexts. The findings suggest that the critical variable shaping international student mobility is no longer the relative strength of push and pull factors but the stability of the institutional environment itself and that new geopolitical pressures operate not only from above through interstate rivalry but also from below through the pressures of domestic governance.
