Elham Alzain

This study examines direct and indirect associations between perceived gamified English learning and self-reported cybersecurity-related English language proficiency (ELP) among undergraduate computer science students in Saudi Arabia, with technology acceptance, student engagement, and learning environment specified as potential mediators. Drawing on Self-Determination Theory, the Technology Acceptance Model, and Constructivist Learning Theory, the study employed a cross-sectional survey design with 417 students; 390 provided complete responses, which were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Gamification demonstrated a significant positive association with self-reported ELP (β = 0.172, p = 0.012). The estimated indirect associations through technology acceptance (β = 0.196, p = 0.002) and student engagement (β = 0.179, p < 0.001) were statistically significant, whereas the indirect association through the learning environment was not. The complete model accounted for 64.3% of the variance in self-reported ELP. Because ELP was measured through self-report and the design is cross-sectional, findings represent associations rather than confirmed causal effects. The study provides exploratory evidence of associations among students’ perceptions of gamified learning, technology acceptance, engagement, and cybersecurity-related English proficiency, informing hypotheses for future longitudinal and intervention research.

Rising non-performing assets (NPAs) remain a persistent threat to banking stability in emerging economies, including India. This study examines the role of conventional macroeconomic determinants in shaping NPA dynamics using annual panel data from 30 Indian banks over the period 2003–2022. Employing Robust Least Squares and dynamic modelling techniques, the analysis evaluates the impact of GDP growth, inflation, exchange rate movements, and repo rates, while addressing heteroscedasticity, autocorrelation, and bank-level heterogeneity. The findings indicate that currency depreciation significantly increases NPAs, whereas inflation and tighter monetary policy exert a moderating effect. GDP, however, does not exhibit a significant influence, suggesting limited macroeconomic transmission to banking asset quality. To ensure appropriate model specification, stationarity tests are conducted, guiding the inclusion of dynamic elements in the analysis. Once the model is adjusted accordingly, the results consistently highlight the relative importance of macroeconomic factors without yielding conflicting interpretations. While broader theoretical perspectives such as institutional memory and balance-sheet effects are acknowledged for contextual relevance, they are not empirically tested in this study. Overall, the findings emphasize that conventional macroeconomic variables play a meaningful, though selective, role in explaining NPA behaviour, offering clearer and more consistent insights for policy and banking practice.

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have enabled personalized education via intelligent tutoring, adaptive learning, automated assessments, and personalized feedback. Nevertheless, educational AI systems are still confronted with challenges, including high computational complexity, long inference time, unreliable content generation, limited explainability, and ineffective multiagent orchestration. To this end, this study proposes LITE-Trust, a Lightweight Trust-Aware Explainable multiagent Generative Artificial Intelligence Framework for personalized education, which consists of learner profiling, Knowledge State Estimation (KSE), the Educational Library and Readers Optimization (ELRA) algorithm, relevance-based selective agent orchestration, trust evaluation, and an Explainability Engine. The ELRA algorithm is applied to optimize learner-specific educational configurations by taking learning requirements, trust, and computational efficiency into account, and selective orchestration is conducted to reduce computational overhead by only activating relevant agents. Experiments on the EdNet and Open University Learning Analytics Dataset (OULAD) demonstrate that LITE-Trust achieves 10.5/9.5ms latency, 80.5/81.3 MB memory, and 1.92 M parameters, which is computationally more efficient than the compared methods. In addition, LITE-Trust attains 95.0%/94.6% fidelity, 94.1%/93.4% explanation quality, and 92.6%/92.1% interpretability on the two datasets. Besides, the ELRA algorithm significantly outperforms existing algorithms in terms of various metrics. Overall, the experimental results verify that LITE-Trust can effectively balance personalization, learning quality, trust, explainability, and computational efficiency, thereby facilitating the practical application of lightweight and trustworthy GenAI systems in personalized education.

AI-driven educational platforms increasingly rely on heterogeneous cloud workloads, including real-time tutoring, adaptive assessments, and large-scale learning analytics, which impose diverse and conflicting quality-of-service (QoS) requirements. Conventional cloud scheduling approaches fail to address such heterogeneity, often leading to service-level agreement (SLA) violations, increased execution cost, and poor resource utilization. This study proposes QoS-aware Adaptive Mirage Search Optimization Framework (QoS-AMSO), a QoS-aware multi-objective task scheduling framework based on an enhanced Mirage Search Optimization algorithm for intelligent workload management in multi-cloud environments. The proposed approach integrates four key innovations: adaptive refraction-based step sizing for improved convergence, QoS-feedback-driven dynamic weight adjustment, Lévy flight-based diversity preservation, and Pareto-based elite solution selection. Additionally, a task classification model categorizes workloads into latency-critical, throughput-intensive, deadline-soft, and batch-deferred classes, enabling context-aware scheduling decisions. The framework is evaluated using CloudSim 3.0 simulations with three cloud providers, 60 virtual machines, and workload sizes ranging from 100 to 1000 educational tasks under multiple workload scenarios. The QoS-AMSO achieves the best overall performance, reducing makespan to 798 s, execution cost to USD 292.8, SLA violation rate to 3.9%, and improving resource utilization to 97.2%. These results demonstrate the effectiveness and scalability of QoS-AMSO for QoS-aware scheduling in AI-powered educational multi-cloud environments.

This study investigates the interrelationships among intrinsic motivation (INM), extrinsic motivation (EXM), self-efficacy (SE), language learning strategies (LLS), and English proficiency (EP) within a unified Structural Equation Modeling (SEM) framework. The model integrates Self-Determination Theory (SDT), Social Cognitive Theory (SCT), and Oxford's Strategy Inventory for Language Learning (SILL). Data were collected from 303 undergraduate English as a Foreign Language (EFL) learners in Saudi Arabia and Yemen using a survey. The SEM analysis revealed that INM significantly predicted both SE (β = 0.530, p < 0.001) and EP (β = 0.228, p < 0.01), whereas EXM showed no significant direct or indirect effects. SE strongly predicted LLS use (β = 0.676, p < 0.001), which in turn was a robust predictor of EP (β = 0.523, p < 0.001). Mediation analyses supported the sequential pathway from INM through SE and LLS to EP (β = 0.187, p < 0.001). This confirms SE and LLS use as key cognitive and behavioral mechanisms linking motivation to outcomes. The model explained 42% of the variance in proficiency, 45.7% in LLS, and 32.7% in SF. These findings extend prior EFL research by empirically validating a combined theoretical approach and demonstrating that INM rather than EXM drives proficiency gains via cognitive beliefs and strategic behaviors. The results have theoretical and practical implications, informing curriculum design, instructional practices, and learner support strategies in Middle Eastern EFL contexts.

This study explores Margaret Atwood’s The Testaments through Mikhail Bakhtin’s theory of dialogism and polyphony to examine how multiple narrative voices articulate resistance against Gilead’s totalitarian regime. The purpose is to demonstrate how Atwood’s use of diverse narrators transforms rebellion into a dialogic process that challenges monologic authority. Employing a qualitative analytical approach grounded in Bakhtinian criticism, the paper interprets the perspectives of Aunt Lydia, Agnes, and Daisy as independent yet interrelated voices. Through textual analysis, the study identifies how Atwood’s polyphonic design allows each narrator to speak autonomously while engaging in a dynamic exchange with the others, thereby dismantling the dominance of a single authoritative discourse. The findings indicate that The Testaments functions as a polyphonic novel where meaning arises through interaction rather than authorial control. Rebellion becomes a collective act voiced through multiple, autonomous consciousnesses. The dialogic exchange among narrators illustrates Bakhtin’s belief that truth is relational and plural, never confined to a single viewpoint. This study contributes to Bakhtinian literary criticism by showing how Atwood’s narrative form embodies dialogic rebellion. It also provides a model for understanding reality through multiplicity and diversity of voice. Finally, it demonstrates the power of literature to resist ideological domination by allowing distinct voices to coexist, converse, and challenge oppressive authority.

This research examines the relatively underexplored role of Self-Help Groups (SHGs) in promoting women's social empowerment (SE) in rural India, focusing on three key aspects: microcredit (MC), training (TR), and organizational structure (OR). The study assesses SHGs' contributions to women's agency, social networks, and civic participation, alongside their recognized economic impact. Using empowerment and social capital theories, Partial Least Squares Structural Equation Modelling (PLS-SEM) was utilized to analyze survey data from 398 SHG members in the Dakshina Kannada and Udupi districts. The findings highlight that organizational resilience built on participatory governance and mutual trust is crucial for promoting social equity. While microcredit greatly enhances autonomy and inclusion, the impact of training remains limited, likely owing to resource shortages, bureaucratic obstacles, unsuitable content, or socio-cultural differences. The study emphasizes the combined importance of OR and financial structures while criticizing the limited social focus of TR initiatives. It enriches the empowerment discourse by empirically illustrating SHGs as mechanisms for strengthening bonding and bridging social capital. Furthermore, it advocates for reforms in TR and participatory governance within SHGs, as well as aligning policies with public services. These insights highlight that SHGs serve not only as financial instruments but also as catalysts for grassroots social development.

This study investigates the semiotic and cultural functions of character naming in the Yemeni television series Duroob al-Marjalah (Branching Paths of Manhood) (2024–2025). It applies onomastic theory and Barthesian semiotics to examine how Yemeni screenwriters employ names as narrative and ideological tools. A purposive sample of ten central characters was selected from a Yemeni drama series for qualitative analysis. Each name was examined for linguistic structure, semantic meaning, intertextual associations, and socio-cultural alignment. Semiotic interpretation followed Barthes’ signifier–signified–myth model to decode narrative and cultural symbolism. The findings indicate that character names function as multifaceted semiotic tools, conveying heritage, while occasionally employing stylization for satire or fostering empathy through cultural resonance. However, many lack grounding in Yemeni naming conventions, creating a tension between narrative dramatization and socio-onomastic realism. The results suggest that while Yemeni screenwriters show partial awareness of naming as a cultural and narrative tool, the creative process often privileges thematic resonance over ethnographic accuracy. This research contributes to onomastic theory, Arabic media studies, and semiotic analysis by evidencing how localized naming practices—or their absence—shape identity construction, world-building, and cultural recognition in regional television drama.