Significant regional imbalances have long been observed in China’s development, with the level of educational development in the western region consistently lagging behind the national average. Structural disparities are also evident among the provinces within the region. To systematically identify the determinants of these disparities and to characterize the spatial development patterns, a multidimensional evaluation framework was constructed using six indicators: number of schools, number of teachers, average years of schooling, public library collection size, governmental fiscal education expenditure, and number of internet users. Panel data from 12 western provinces (including municipalities and autonomous regions) for 2008 and 2017 were employed. Indicator weights were determined using the entropy method, followed by cluster analysis to classify the levels of educational development across the region. The findings indicate a steady overall improvement in educational development in western China, although substantial interprovincial disparities persist. Based on these results, policy recommendations are presented, including the optimization of the education policy system, improvement of resource allocation structures, strengthening of high-quality talent recruitment and incentive mechanisms, and coordinated planning of educational resources. The conclusions provide empirical support and policy guidance for enhancing educational equity and promoting balanced development in western China.
In the context of the digital era and ongoing reforms in higher education, how to cultivate and enhance university teachers’ digital teaching competence has become a prominent topic among scholars. Accordingly, this study examined the relationships between university assessment mechanisms and teachers’ digital teaching competence based on 422 valid questionnaires. The results indicate that instructional assessment, research assessment, administrative assessment, and qualification assessment all exert positive effects on teachers’ digital teaching competence. Social support moderates the negative relationship between work stress and digital teaching competence, and further moderates the mediating role of work stress in the relationships between the four dimensions of assessment mechanism rationality and digital teaching competence. The findings provide insights and recommendations for optimizing assessment mechanisms and promoting the modern professional development of university teachers.
The reduction of excessive academic burden in China’s basic education system has been established as a central objective of national education reform and has become a subject of intense policy debate. To elucidate the complex strategic interactions that shape the implementation of the “Double Reduction” policy, a multi-agent evolutionary game model was constructed incorporating three principal stakeholder groups: government authorities, schools and teachers, and students and parents. Replicator dynamic equations were employed to examine the evolutionary stability of stakeholder strategies and the conditions under which equilibrium outcomes emerge. Through numerical simulations, the influence of regulatory enforcement intensity on behavioral trajectories and convergence patterns was evaluated. The results reveal that asymptotically stable equilibria exist, with optimal system performance achieved when government bodies maintain active and credible regulatory oversight, educational institutions engage in substantive and sustained burden-reduction efforts, and families adopt cooperative and adaptive responses. By clarifying the mechanisms through which stakeholder interactions determine collective outcomes, this study provides theoretical support for the refinement of policy coordination and the long-term enhancement of education governance capacity. These findings contribute not only to the understanding of the “Double Reduction” policy’s systemic impact but also to broader discussions on the role of evolutionary game theory in evaluating multi-agent policy interventions in education systems.
The psychometric validity of multiple-choice questions (MCQs) generated by an advanced Artificial Intelligence (AI) language model (ChatGPT) was evaluated in comparison with those developed by experienced human instructors, with a focus on mathematics teacher education. Two parallel 30-item MCQ tests—one human-designed and one AI-generated—were administered to 30 mathematics teacher trainees. A comprehensive psychometric analysis was conducted using six metrics: item difficulty index (Pi), discrimination index (D), point-biserial correlation, item-test correlation (Rit), Cronbach’s alpha (α) for internal consistency, and score variance. The analysis was facilitated by the Analysis of Didactic Items with Excel (AnDIE) tool. Results indicated that the human-authored MCQs exhibited acceptable difficulty (mean Pi = 0.55), moderate discrimination power (mean D = 0.31), and strong internal consistency (Cronbach’s α = 0.752). In contrast, the AI-generated MCQs were found to be substantially more difficult (mean Pi = 0.22), demonstrated weak discrimination (mean D = 0.16), and yielded negative internal consistency reliability (Cronbach’s α = −0.1), raising concerns about their psychometric quality. While AI-generated assessments offer advantages in terms of scalability and speed, the findings underscore the necessity of expert human review to ensure content validity, construct alignment, and pedagogical appropriateness. These results suggest that AI, in its current form, is not yet equipped to autonomously generate assessment instruments of sufficient quality for high-stakes educational settings. A hybrid test design model is therefore advocated, wherein AI is leveraged for initial item drafting, followed by rigorous human refinement. This approach may enhance both efficiency and quality in the development of educational assessments. The implications extend to educators, assessment designers, and developers of educational AI systems, highlighting the need for collaborative human-AI frameworks to achieve reliable, valid, and pedagogically sound testing instruments.
This study investigates the role of Artificial Intelligence (AI) in sustainable education through a bibliometric analysis, aiming to explore research trends, key contributors, citation analysis, co-authorship, and thematic developments in the field. As AI becomes increasingly integrated into Education, it is crucial to understand its impact on learning personalization, institutional efficiency, and sustainability. The study also identifies research gaps and provides recommendations for future exploration. The study employs a bibliometric and content analysis methodology using Scopus data. Two hundred seventy-six documents (2016-2025) were analyzed through descriptive statistics, citation analysis, co-word analysis, and co-authorship networks, utilizing VOSviewer and Biblioshiny for data visualization. The analysis examines publication trends, top-cited articles, leading institutions, and international collaborations to map the intellectual landscape of AI in sustainable education. The findings indicate a significant increase in AI-related publications after 2019, reflecting growing global interest. India, the USA, and China lead research output, while Sustainability (Switzerland) and Lecture Notes in Networks and Systems are the most prominent publication sources. The co-authorship analysis highlights strong global research collaborations, with the UK, Brazil, and China playing key roles. Thematic clustering reveals four major research areas: AI-driven Environmental Education, AI in Education, sustainable education frameworks, and AI's technical advancements in learning systems. This study provides a comprehensive, macro-level bibliometric analysis that maps global research dynamics, identifies intellectual structures, and visualizes collaborative networks in AI and sustainable education. Despite its contributions, the study has several limitations. First, while Scopus offers broad and reputable coverage of peer-reviewed literature, the exclusive reliance on this database limits the inclusion of potentially relevant studies indexed in other databases such as Web of Science (WoS). This may restrict the diversity and comprehensiveness of the findings. Future research should consider cross-validating results using multiple databases to ensure a more holistic understanding of AI in sustainable education. Second, the exclusion of non-English publications may limit the diversity of perspectives. Third, the study primarily focuses on journal articles and conference papers, excluding books and institutional reports that might offer more profound insights.
Although methodological innovations have reshaped many aspects of scientific writing, literature reviews remain one of its most structurally underdeveloped and conceptually inconsistent components. Existing approaches often fail to communicate the functional role of individual sources within the research argument, leaving both readers and reviewers without a transparent sense of contribution, coherence, or originality. This paper introduces the Pyramid of Contribution Review (PCR), a novel framework that visually and functionally maps references according to their role in the manuscript (Introduction, Methodology, Results, Discussion, Gap) and their level of relevance. Through a mixed-methods validation process, including expert-based Delphi design (n = 28) and a large-scale evaluation survey (n = 118), the method was rigorously tested across disciplines. Statistical analyses reveal that manuscripts perceived to employ the PCR model are 3.45 times more likely to be rated as publishable compared to those relying on conventional narrative reviews. Experts overwhelmingly endorsed the model for its clarity, strategic value, and pedagogical utility. This study positions the PCR framework not only as a solution to a long-standing structural gap in scientific writing but as a forward-looking standard for literature organization in high-impact research. The future of scholarly communication requires not just citation density but citation precision, exactly what the PCR model provides.
The extent to which students’ cultural values influence mathematical problem-solving skills was investigated, with emphasis placed on the moderating effect of prior mathematical knowledge and the mediating role of student motivation. A mixed-methods design was employed to ensure both quantitative and qualitative dimensions of the inquiry were addressed, enabling a comprehensive understanding of the underlying relationships. A purposive sample of 370 students from culturally diverse regions of Ghana was selected to ensure contextual validity and sociocultural relevance. It was found that students’ cultural values significantly shaped their problem-solving performance, particularly in relation to cognitive processing strategies and the selection of problem-solving heuristics. The relationship between cultural values and problem-solving skills was moderated by students’ prior knowledge, with students possessing a stronger foundational understanding of mathematics deriving greater benefit from cultural alignment. Contrary to expectations, the mediating effect of student motivation on this relationship was not supported, suggesting that while motivation is influenced by cultural values, it does not serve as a direct conduit for enhanced problem-solving capability. These findings underscore the necessity of incorporating culturally responsive pedagogical strategies that recognize and harness cultural value systems as cognitive assets. Furthermore, implications for curriculum development and teacher training were discussed, with a recommendation that future research explore context-specific interventions that operationalize cultural capital to improve mathematical learning trajectories.
The effectiveness of a microlearning-supported flipped classroom model in improving learning achievement and student attitudes was investigated among vocational college students enrolled in Information Technology (IT) courses in Zibo City, China. While the flipped classroom model—characterized by pre-class engagement with instructional content and in-class participatory learning—has been widely adopted in vocational education, concerns regarding cognitive overload and limited student engagement persist. To address these challenges, microlearning was integrated to deliver content in concise, targeted segments intended to enhance comprehension and reduce extraneous cognitive load. A quasi-experimental design was employed involving 60 first-year students, who were randomly assigned to either an experimental group (microlearning-supported flipped classroom) or a control group (traditional flipped classroom). Learning outcomes were evaluated using a 50-item IT achievement test, while student attitudes were assessed through a 20-item Likert-scale questionnaire covering four attitudinal dimensions. High instrument validity, i.e., average Scale-level Content Validity Index (SCVI/ave) = 0.977, and internal reliability (Cronbach’s α = 0.958) were established. No significant differences were observed in the pre-test scores between groups, confirming baseline equivalence. Post-intervention results demonstrated a statistically significant improvement in the experimental group (M = 52.733, SD = 3.805) compared to the control group (M = 49.600, SD = 3.838), t (58) = 3.376, p = 0.002), indicating enhanced academic performance. Favorable shifts in learning attitudes were also observed among students exposed to the microlearning-enhanced model, although the four-week intervention period constrained the generalizability of these attitudinal outcomes. These findings suggest that the incorporation of microlearning elements into flipped classroom pedagogies can foster more effective engagement and lead to measurable improvements in academic performance within vocational IT education contexts. Future research involving extended implementation periods and larger, more diverse sample populations is recommended to further validate the durability and scalability of these effects.
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