This study examines the integration of halal tourism with global sustainability frameworks, particularly the United Nations Sustainable Development Goals (SDGs). Using PRISMA methodology, we analyzed 62 peer-reviewed publications from 2019 to 2025, employing bibliometric analysis and structural topic modeling to map research trends in this rapidly expanding field. Three distinct research domains emerged: halal tourism and sustainability integration, SDG alignment, and technology systems. However, analysis revealed field fragmentation, with researchers specializing in individual domains rather than developing comprehensive approaches that bridge religious compliance, environmental responsibility, and technological innovation. This specialization—where scholars focus exclusively on either religious, sustainability, or technological dimensions—creates missed opportunities for integrated sustainable halal tourism implementation. The study’s theoretical contribution lies in developing an integrated conceptual framework connecting previously fragmented research areas, demonstrating how Islamic environmental stewardship principles can enhance rather than constrain sustainability initiatives. This framework transcends the conventional dichotomy between religious requirements and environmental goals. Practically, findings provide actionable guidance: policymakers should develop coordinated frameworks addressing infrastructure challenges; tourism operators can implement certification systems signaling both halal compliance and sustainability commitments; financial institutions can leverage Sharia-compliant instruments supporting sustainable tourism development. The research highlights halal tourism’s significant potential to contribute meaningfully to global sustainability objectives while preserving religious authenticity. Future research should prioritize interdisciplinary studies integrating technological innovations with religious compliance and expand geographical coverage beyond Southeast Asia.

Psychological well-being is a cornerstone of student success in higher education. However, many students struggle with mental health challenges like stress, anxiety, and depression during and even after the Covid-19 pandemic. These challenges, often stemming from academic, personal, social, or career concerns, negatively impact student learning and development. This underscores the need for robust support systems within higher education (HE). Artificial intelligence (AI) emerges as a promising field in educational technology, offering students readily available guidance on their path to well-being. This research, guided by the PRISMA Statement 2015, provides an overview of AI applications in higher education through a systematic review. From an initial pool of 270 publications identified between year 2021 and 2023, finally, 24 articles met our inclusion criteria and were analyzed for the final synthesis. This paper revealed three key areas where AI-based systems can support student well-being: i) AI’s Advancement and Potential: Exploring the evolving capabilities and promise of AI in this context. ii) Building Effective AI Systems: Identifying crucial components for successful AI-based well-being interventions. iii) Barriers to Implementing AI in Higher Education: Addressing ethical considerations and challenges unique to academic settings. The conclusions and the road ahead from this research is the critical need for ethical, well-designed AI-based systems to overcome existing barriers and deliver exceptional student well-being support services. By prioritizing student mental health and providing them with the necessary tools and resources, we can empower them to achieve their full potential and thrive in their academic endeavors.

The exponential growth of the elderly population poses considerable obstacles to healthcare systems on a global scale, hence requiring the implementation of inventive strategies to identify and mitigate age-related illnesses at an early stage. The primary objective of this study is to explore the use of big data analytics to improve healthcare practices. Specifically, the emphasis is on identifying possible risk factors and developing proactive treatments for senior citizens. The research technique used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration of 2020. This approach is utilised to ensure a thorough and transparent review of the relevant literature. Moreover, the use of Rstudio software is prevalent in the field of data processing, statistical analysis, and visualisation. By conducting a comprehensive examination of academic databases and medical literature, this study undertakes an analysis of a collection of pertinent papers to explore the significance of big data analytics in the early diagnosis and prevention of diseases in senior populations. The studies that have been chosen include a wide range of healthcare fields, such as cardiology, neurology, cancer, and geriatrics. This selection aims to provide a thorough comprehension of existing practises and identify any possible areas that may need more attention. The results of this study emphasise the significant impact that big data analytics may have on healthcare for the elderly. Using extensive and varied datasets, sophisticated analytical methodologies such as machine learning algorithms and data mining allow the detection of nuanced patterns and correlations that might function as precursors for age-related ailments.

Amid Vietnam’s accelerated digital transformation, teacher education is undergoing a pivotal shift—demanding innovative pedagogical and assessment strategies to foster self-directed learning (SDL) competencies. This need is particularly acute in Mathematics, a discipline that requires advanced logical reasoning, autonomy, and metacognitive engagement. In response, this study proposes a theoretically grounded and empirically validated formative assessment (FA) model designed to enhance SDL among pre-service primary Mathematics teachers. The model integrates both internal factors (e.g., motivation, metacognitive skills, and self-assessment competences) and external elements (e.g., technological tools, instructional support, and learning environments). Data were collected from 438 pre-service Mathematics teachers across multiple universities and analyzed using structural equation modeling (SEM). The findings identify two primary predictors of effective SDL: learners’ perceptions and attitudes toward FA and their intrinsic motivation and persistence. These factors significantly impact learning outcomes and are further amplified when supported by AI-driven tools and digital learning platforms. The study highlights the mediating role of technological and pedagogical scaffolds in strengthening SDL and emphasizes the critical integration of formative feedback, metacognitive strategies, and adaptive instructional design. Despite encouraging results, several challenges remain, including limited learner autonomy, inconsistent technology adoption, and reliance on instructor-led practices. This study contributes to a deeper understanding of how AI-enhanced FA can promote learner autonomy, critical thinking, and sustainable learning behaviors in Mathematics education. It offers actionable insights for policymakers and educators in designing resilient, context-sensitive teacher training frameworks aligned with the demands of the digital era. Future research should focus on piloting the model across diverse educational settings and leveraging big data and emerging technologies to improve predictive validity and pedagogical impact.

Mobile learning applications have proliferated across educational contexts without systematic understanding of which pedagogical design patterns effectively support learning across different educational levels. This study developed a comprehensive pedagogical evaluation framework through systematic bibliometric analysis of mobile learning literature to identify design patterns and assess their effectiveness across K-12, higher education, and professional training contexts. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we analyzed 53 documents (2006–2025) from Scopus using bibliometric techniques, network analysis, and thematic clustering to examine conceptual structures, collaboration patterns, and knowledge evolution. The analysis revealed fundamental fragmentation: while mobile learning demonstrates robust growth (9.89% annual increase) and dominates as the field’s organizing principle (betweenness centrality: 450.668), critical pedagogical concepts including instructional design, student engagement, and collaborative learning remain isolated with zero betweenness centrality. Educational level representation proved uneven (K-12: n = 9; higher education: n = 11; professional training: n = 5), with minimal comparative analysis across contexts. Ten distinct research clusters operated in isolation, with instructional design showing high internal consistency (density: 127.958) but minimal mainstream integration (centrality: 2.591). The predominance of short-term studies (60.4%) and limited international collaboration (15.09%) further constrains understanding of sustained impact and cross-cultural applicability. These findings expose a persistent technology-pedagogy divide undermining mobile learning’s educational potential, necessitating integrated frameworks that prioritize pedagogical design over technological innovation. Future research requires longitudinal investigations, systematic cross-level comparisons, and proactive examination of emerging technologies’ interaction with pedagogical principles to establish evidence-based guidelines for context-appropriate mobile learning design.

In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed.