Global Social Science and Humanities Journal

Artificial Intelligence in Education Bridging or Widening the Digital Divide in Developing Countries

2026-10-08T13:00:10+00:00

Artificial intelligence (AI) is rapidly transforming education through personalized learning, intelligent tutoring, automated assessment, generative content creation, learning analytics, academic research support, and educational administration. Although these technologies offer significant opportunities for improving educational quality and accessibility, their implications for developing countries remain complex. Educational systems in low- and middle-income countries frequently operate under constraints that include inadequate digital infrastructure, unreliable connectivity, limited financial resources, insufficient teacher preparation, linguistic diversity, and unequal access to technology. Consequently, AI has the potential both to reduce longstanding educational inequalities and to create a new layer of digital exclusion. This article provides an integrative review of the influence of AI on education in developing countries, with particular attention to teaching, learning, assessment, higher education, administration, inclusion, and educational equity. It synthesizes recent academic literature and international policy reports, with particular emphasis on developments associated with generative AI since 2022. The review identifies personalized learning, expanded access to educational expertise, teacher support, multilingual assistance, administrative efficiency, and improved accessibility as important opportunities. However, connectivity inequalities, affordability, algorithmic bias, inadequate AI literacy, academic integrity concerns, privacy risks, cultural and linguistic underrepresentation, and excessive dependence on automated systems present substantial challenges. To explain these competing effects, the article proposes an Inclusive AI in Education Framework in which infrastructure, affordability, human capacity, localization, governance, and pedagogical integration determine whether AI contributes to greater educational inclusion or reinforces existing inequalities. The paper argues that the educational value of AI in developing countries will ultimately depend less on the availability of sophisticated technologies than on the institutional and societal capacity to deploy them equitably, responsibly, and in accordance with local educational priorities.

Integrating Cultural Heritage through Case-Based Pedagogy in Foreign Language Education in Uzbekistan: A Systematic Review

2026-10-08T13:15:35+00:00

Integrating learners’ cultural heritage into foreign language education raises an important pedagogical question: how can culturally meaningful knowledge be transformed into activities that require purposeful target-language use? Case-based pedagogy offers one potential approach by organizing learning around situations, problems, dilemmas, and interpretive tasks that require learners to mobilize language in context. This systematic review examines the intersection of case-based pedagogy, Uzbek cultural heritage, and foreign language education in Uzbekistan. Following PRISMA 2020, international bibliographic databases and relevant regional scholarly sources were searched for empirical studies addressing foreign language teaching or learning, pedagogical case use, and identifiable Uzbek cultural heritage or local cultural knowledge in the Uzbekistani context. Studies were examined in relation to cultural representation, case design, language-mediated learner activity, reported learning outcomes, and construct–measure alignment. The synthesis identified two related but largely parallel strands of research. One addresses case-based foreign language pedagogy in Uzbekistan without clearly establishing the integration of identifiable Uzbek heritage, whereas the other incorporates Uzbek cultural resources into foreign language education without clearly establishing pedagogical case use. The available evidence therefore does not establish a sufficiently developed empirical literature in which both components are systematically integrated and evaluated within the same instructional design. Rather than demonstrating established effectiveness, the review identifies an integration gap between case-based pedagogy and heritage-responsive foreign language education. To address this gap, the review proposes pedagogical alignment coherence among cultural representation, case architecture, language-mediated learner activity, intended learning outcomes, and assessment evidence as an analytical framework for future research. Future studies should explicitly transform Uzbek cultural heritage into case-based language-learning tasks and evaluate linguistic, cultural, intercultural, cognitive, affective, and identity-related outcomes using construct-appropriate measures.

Beyond Family Socioeconomic Status Academic Engagement and Home Learning Environment in Undergraduate Academic Achievement in China

2026-10-08T13:58:38+00:00

Socioeconomic inequalities can remain relevant to students’ academic experiences after entry into higher education, yet family socioeconomic status alone may not fully account for variation in undergraduate achievement. This study examined the associations of family socioeconomic indicators, home learning environment, and academic engagement with academic performance among undergraduates at North China University of Science and Technology in China. Using a quantitative cross-sectional design, data were obtained from 400 valid student questionnaires selected through a stratified sampling approach. Academic performance was measured by grade point average (GPA), while family socioeconomic status was represented by parental education, family income, and parental occupation. Pearson correlation, multiple regression, and analysis of variance were employed. All three socioeconomic indicators were positively associated with GPA, with correlations of r = .29 for parental education, r = .25 for family income, and r = .21 for parental occupation. Stronger associations were observed for home learning environment (r = .41) and academic engagement (r = .47). The regression model explained 29% of the variance in GPA, with academic engagement (β = .33) and home learning environment (β = .28) showing the largest standardized coefficients among the variables included. Significant differences in GPA were also observed across low-, middle-, and high-SES groups, F(2, 397) = 6.12, p = .002. These findings indicate that socioeconomic background remains associated with undergraduate achievement, while academic engagement and supportive home learning conditions provide additional explanatory value beyond conventional socioeconomic indicators. The study highlights the importance of considering both family resources and students’ learning-related experiences when addressing academic disparities in higher education.

The Impact of Supply Chain Digitalization on Competitive Advantage at Maersk company in Saudi Arabia

2026-10-08T14:06:35+00:00

This study examines the impact of supply chain digitalization on competitive advantage at Maersk in Saudi Arabia. Digitalization is operationalized through internal functional integration supported by enterprise resource planning (ERP) systems and interorganizational information sharing supported by Internet of Things (IoT) connectivity. A descriptive-analytical approach was adopted, using a structured questionnaire with a five-point Likert scale. Responses from 66 employees were suitable for analysis. Cronbach’s alpha for the 20-item instrument was 0.933, indicating high internal consistency. Both digitalization dimensions were rated very highly: ERP-supported integration had a mean of 4.38 and an average item standard deviation of 0.67, while IoT-supported information sharing had a mean of 4.26 and an average item standard deviation of 0.77. Competitive advantage had an overall mean of 4.24. Indicators of internal operational capabilities, including continuous improvement and responsiveness, ranked above ultimate outcomes such as market share and entry into new markets. Response dispersion showed a systematic pattern: internal integration items exhibited less variation than items concerning information exchange with external partners, suggesting more consistent willingness to cooperate than execution of cooperation. Simple linear regression identified a strong, positive, statistically significant relationship between digitalization and competitive advantage (R = 0.757; R² = 0.574; F(1,64) = 86.128; reported p < 0.001). Digitalization thus explained approximately 57.4% of the variance in competitive advantage. Drawing on the resource-based view and dynamic capabilities perspective, the study argues that returns on digital investment depend on organizational routines built around technology. Recommendations address system integration, partner segmentation by digital maturity, data governance, and workforce development. Measurement and research-design limitations constrain generalizability and causal interpretation.

System Quality and Perceived Usefulness in Shaping Smart Learning Effectiveness and Smart Campus Design

2026-10-08T14:16:22+00:00

Smart campus initiatives are increasingly adopted to support the digital transformation of higher education, yet the extent to which system quality translates into meaningful learning benefits remains insufficiently understood. This study examines how key system quality dimensions are associated with smart learning effectiveness and investigates the role of perceived usefulness in this relationship. Drawing on the Information System Success Model and the Technology Acceptance Model, a research model was developed incorporating accessibility, system efficiency, information quality, and real-time capability as system-related antecedents of perceived usefulness and smart learning effectiveness. Data were collected through a questionnaire survey of 420 students and faculty members at the School of Light Industry, North China University of Science and Technology, and analyzed using partial least squares structural equation modeling with 5,000 bootstrap resamples. The results indicate that all four system quality dimensions are positively associated with perceived usefulness and smart learning effectiveness. Information quality exhibited the strongest relationship with both perceived usefulness (β = 0.569, p < 0.001) and smart learning effectiveness (β = 0.342, p < 0.001), while perceived usefulness was positively associated with smart learning effectiveness (β = 0.255, p < 0.001). The findings further indicate differences between students and faculty in selected relationships involving information quality and real-time capability. Building on these empirical findings, the study develops an empirically informed smart campus design framework that translates system quality requirements into architectural and service-design priorities. The study contributes to smart campus research by connecting system-level characteristics, users’ perceptions of usefulness, and learning effectiveness within an integrated framework, while providing evidence-based priorities for the design and improvement of smart campus environments.