Gastric cancer (GC) remains a leading cause of cancer-related morbidity and mortality worldwide, particularly in East Asia. Nanomedicine has emerged as a promising strategy to enhance the precision and efficacy of GC diagnosis and therapy. This study aimed to systematically evaluate global research trends and knowledge structures in GC nanomedicine during the early 21st century using bibliometric and visualization analyses. Relevant publications during the early 21st century were retrieved from the Web of Science Core Collection (WoSCC) and analyzed using R, VOSviewer, CiteSpace, Scimago Graphica, and Pajek. The included records spanned 2003-2026, with the two items labeled "2026" reflecting early access articles already indexed in WoSCC at the time of data retrieval. A total of 1,717 publications authored by 9,857 researchers across 2,192 institutions in 83 countries were included. China, Iran, and the United States were identified as leading contributors, with major research hubs at the Chinese Academy of Sciences, Shanghai Jiao Tong University, and Islamic Azad University. Research output exhibited a sustained upward trend, with citation frequency and H-index steadily increasing. Five primary research hotspots were identified: nano-drug delivery and chemotherapy optimization, targeted therapy and signaling pathway modulation, photothermal/photodynamic synergistic therapies, functionalized nanomaterials for diagnostics, and immunotherapeutic/antibacterial nanomedicine. Over time, the field has evolved from early nanocarrier design (2003-2015) to targeted therapy and diagnostic applications (2015-2020), and more recently toward immunotherapy and green synthesis strategies (2020-2026). Importantly, most studies in this field remain at the preclinical or early translational stage, underscoring the need for further validation using standardized preclinical models and well-designed clinical trials. This bibliometric study provides a comprehensive overview of the developmental trajectory, key contributors, and emerging trends in GC nanomedicine, offering valuable insights to guide future interdisciplinary research, clinical translation, and strategic resource allocation.

Targeted therapy has emerged as a promising precision medicine strategy for immunoglobulin A nephropathy (IgAN) through the modulation of specific pathogenic pathways. Although research in this area has accelerated, the literature remains scattered, and no bibliometric study has mapped its global knowledge structure or evolving hotspots. We conducted a bibliometric and visualization analysis of 678 publications indexed in the Web of Science Core Collection (1999-2025). CiteSpace 6.4R1, VOSviewer 1.6.20, and the R‑based bibliometric package were used to assess publication and citation trends; identify prolific countries, institutions, authors, and journals; and generate co‑authorship, co‑citation, and keyword co‑occurrence networks. Research frontiers were explored through thematic evolution mapping and keyword burst detection. Annual publications increased notably after 2015, indicating a shift from supportive care to molecularly targeted interventions. China and the United States produce over 60% of the global output, with expanding collaborations. The core themes clustered into three domains: complement inhibition (e.g., C5 blockade), B‑cell-directed therapy (including BAFF/APRIL modulation), and mucosal immune regulation. The gut-immune axis, particularly microbiome modulation, has emerged as a new frontier. Notably, recent trends highlight a growing interest in non-invasive biomarkers (e.g., urinary targets) to guide patient stratification, although clinical translation remains a challenge. This study delineates a rapidly evolving landscape of IgAN-targeted therapy. Precision approaches focusing on complement blockade, B‑cell pathways, mucosal immunity, and microbiome modulation hold substantial potential. While bibliometric data reflect a vibrant academic interest, future efforts should increasingly focus on translating candidate discoveries into clinical validation. Priority should be given to biomarker‑driven stratification and integrated diagnostic-therapeutic frameworks to accelerate translation and improve outcomes.

Patient knowledge is pivotal to inflammatory bowel disease (IBD) management, yet educational opportunities vary widely worldwide. This scoping review maps existing evidence on patient knowledge, explores regional and demographic differences, and identifies gaps to guide culturally adaptable interventions. Following PRISMA-ScR guidelines, PubMed and Embase were searched up to December 31, 2025, for studies assessing patient knowledge in IBD. Eligible adult and pediatric studies were independently screened and synthesized by two reviewers across key knowledge domains, including disease basics, treatment, complications, colorectal cancer (CRC) risk, surgery, vaccination, and diet. From 8464 records, 53 studies met inclusion criteria. Validated tools, including CCKNOW and IBD-KNOW, predominated in adult studies, whereas IBD-KID and IBD-KID2 were used in pediatric populations. Knowledge levels varied widely: correct understanding of anatomy (36%-68%), risk factors (16%-85%), CRC risk (24%-78%), and surgery (13%-16%) was frequently suboptimal. Only 5%-51% recognized azathioprine as an immunosuppressant. Awareness of vaccination (39%-78%) and dietary relevance (23%-65%) remained limited. Gastroenterologists were the primary information source (42%-96%). Higher knowledge was associated with both sociodemographic factors (female sex, younger age, higher educational attainment) and disease-related characteristics (longer disease duration, prior exposure to biologics or surgery, Crohn's disease phenotype). Patients in Europe and North America had higher awareness than those in Asia and the Middle East, probably due to better healthcare access and patient education infrastructure. Global IBD knowledge and awareness remain inadequate and uneven across regions and domains. Updated, culturally appropriate and adaptable assessment tools and multidisciplinary, technology-enabled educational strategies are needed to enhance IBD literacy.

Cross-sectoral knowledge spillover in the apparel value chain enables incumbent firms in the Global South to transform industry challenges into social innovation (SI) opportunities, redefining B2B dynamics through collaborative learning. This study explores knowledge spillover mechanisms in the B2B apparel value chain, particularly between dominant buyers in the Global North and apparel industries in the Global South. Still, it is largely unclear what knowledge spillover is made of and how it happens. It investigates how buyers facilitate SI practices and how local production firms adapt them to their operational environments. The research employs a qualitative approach using a single case study method. Data collection includes document analysis and in-depth interviews with managers, supervisors, and employees. Thematic analysis, supported by NVivo software, is used to interpret the data. The study finds that SI is embedded within new business models introduced by incumbent firms. Several SI practices contribute to sustainability transitions, enhancing corporate image and legitimacy amid global competition. Additionally, buyer-imposed conditions such as job quality, gender equality, and inclusion are transformed into economic empowerment and livelihood improvements for machine operators in local firms. The research provides valuable insights into how businesses in the Global South can effectively localize global SI practices to enhance sustainability and improve working conditions. It highlights the role of international buyers in shaping responsible business practices through knowledge spillover. The study underscores the importance of SI in addressing human rights concerns, improving labour conditions, and promoting economic empowerment within the apparel industry. This study contributes to the ongoing discourse on the business-society relationship by highlighting the transformative potential of SI in the apparel sector. It offers a nuanced perspective on how firms localize global knowledge to drive sustainability transitions, thereby enriching the understanding of knowledge spillover in the global value chains.

ABSTRACT Developing countries often have highly differentiated educational ecosystems, making it difficult for local students of educational leadership and management programs to connect their experiences to the global knowledge base on school effectiveness and improvement (SEI). Spread over a four-year period, this action research study contextualized the design and delivery of a graduate-level course in an educational leadership and management program in higher education settings in Pakistan. Using an adaptive-reflective approach and by engaging multiple sources of data in a mixed methods design, the course was successively improved to situate the stakeholder experiences in the global knowledge base on SEI. The results indicate that making explicit connections between indigenous and international discourse on SEI is necessary to contextualize learning. The study demonstrates action research as a fitting methodological paradigm and pedagogical tool to prepare would-be educational change agents who can effectively respond to and lead in the continuously changing landscape of SEI in the developing world contexts.

Targeting biofilm resistance in meat production: Postbiotics as a clean-label alternative, a bibliometric analysis review.

Hepatitis E virus (HEV) remains a leading cause of acute viral hepatitis globally, particularly in South Asia and Africa. However, epidemiological prioritization is hampered by fragmented data and discordant disease burden estimates. Following JBI and PRISMA-Sc guidelines, we conducted a scoping review of global HEV evidence. We used the PCC framework: (P) general and high-risk populations (pregnant women, immunocompromised, and displaced groups); (C) quantitative estimates of burden, risk factors, or virological gaps; and (C) global evidence across all WHO regions to include studies. We searched PubMed, Scopus, and Web of Science, supplemented by country-specific searches in Google Scholar and IHME. From 11,583 citations, 395 articles met the inclusion criteria. The temporal distribution shows a marked increase in research volume, with 65.3% of studies published after 2010; however, 54.9% relied on observational descriptive designs while experimental investigations remained infrequent (4.3%). We identified three estimates of the global burden of HEV: the IHME Global Burden of Disease (GBD) published in 2021 (19.4 million cases) and two widely cited systematic reviews published in 2012 (20.1 million infections) and 2020 (939 million infections). A significant virological "blind spot" was observed, as 47.8% of studies did not report genotype information, though Genotype 3 (21.8%) was the most frequently identified among specified reports. Key risk domains identified were environmental (sanitation/water contamination) and cultural/occupational practices. Pregnant women, immunocompromised patients, and patients with pre-existing liver conditions were high at-risk populations. Key knowledge gaps identified were limited confidence in burden of disease estimates: severe molecular blind spots and evidence deserts, limited public health resources for surveillance, diagnostics, and reporting of cases and deaths in highest risk settings; exclusion of outbreaks from estimates of the burden of disease and unreliable convenience sample derived estimates. Hepatitis E virus is often neglected by international communities, global actors and national governments. However, it is difficult for stakeholders to prioritize a pathogen with highly variable and unreliable global burden of disease estimates. Comprehensive country level data based on more access to routine testing could facilitate global initiatives to devise strategies for equitable vaccination and mitigate the morbidity and mortality associated with this vaccine-preventable disease.

Global and regional knowledge of antibiotic use and resistance among the general public: a systematic review and meta-analysis.

Statins have attracted increasing attention beyond cardiovascular prevention due to their potential anticancer effects; however, the intellectual structure and long-term evolution of statin-cancer research remain insufficiently characterized. This study aimed to systematically map the global development, thematic architecture, and collaborative landscape of statin-cancer research using bibliometric approaches. A total of 6100 article-type publications indexed in the Scopus database from 1940 to 2025 were analyzed using Bibliometrix and VOSviewer. Analyses included descriptive metrics, logistic growth modeling, geographic and institutional mapping, international collaboration networks, Bradford's Law of journal scattering, co-citation analysis, keyword dynamics, thematic evolution, and N-gram trend analysis. The field demonstrated sustained growth (annual growth rate 7.36%) and has recently entered a consolidation phase, supported by strong international collaboration and a stable core of high-impact journals, institutions, and scholars. Thematic analyses revealed a clear shift from cardiovascular and mechanistic foundations toward oncology-driven, drug-specific, and translational research, with breast cancer emerging as a dominant focus and simvastatin forming a highly developed niche theme. Emerging trends highlighted increasing emphasis on physiologically based pharmacokinetic modeling, drug-drug interactions, and clinical safety considerations. Overall, this study provides a comprehensive, data-driven overview of statin-cancer research, offering strategic insights to guide future experimental, clinical, and translational investigations.

At a historical juncture when Artificial Intelligence Generated Content (AIGC) is profoundly reshaping global knowledge production and the landscape of geopolitical analysis, traditional area studies confront a dual crisis: the declining explanatory power of qualitative paradigms and a “knowledge supply crisis” mediated by algorithmic systems. This paper argues that area studies must undergo a paradigmatic transformation—from “linguistic mediation” to “semantic governance,” and from tacit experiential knowledge to the construction of “semantic infrastructure.” By conceptualizing high-quality data annotation as both a confirmation of semantic rights and the building of cognitive infrastructure, the study proposes an integrated framework encompassing ontology construction, semantic alignment, and domain-specific evaluation. Through this engineering-oriented architecture, unstructured regional knowledge can be transformed into structured data assets. Driven by a dual-engine model integrating “United Nations normative corpora” and “Open Source INTelligence (OSINT),” area studies can evolve from post hoc interpretation toward a decision-support paradigm characterized by real-time perception, structured computation, and scenario simulation. Such a transformation will consolidate the epistemic foundations of China’s autonomous knowledge system and safeguard national cognitive sovereignty.

India is undergoing a major educational transformation driven by the National Education Policy (NEP) 2020, rapid digitalisation, and a renewed interest in Indic knowledge traditions. As the nation envisions Viksit Bharat@2047, the integration of Artificial Intelligence (AI) with Indic pedagogical frameworks presents a significant opportunity to design a future-ready, value-driven education ecosystem. This paper examines how AI-enabled systems can enhance, strengthen, and scale traditional Indian pedagogical philosophies such as Gurukul, Samskara-based learning, Anubhav (experiential learning), Swadhyaya (self-learning), and Sahavasa (collaborative living and learning). While global educational models highlight the use of AI for personalization, analytics, and adaptive learning, the Indic knowledge system brings a unique value dimension rooted in holistic development, ethics, community well-being, and spiritual intelligence. The study adopts a conceptual and analytical research methodology supported by literature from NEP 2020, UNESCO reports, AI-in-education research, and Indic knowledge sources. The paper identifies critical gaps in current teaching practices, such as over-reliance on content delivery, limited individualisation, and fragmentation between modern and traditional knowledge systems. Through systematic analysis, this research proposes a comprehensive AI-Integrated Indic Pedagogical Model consisting of five pillars: AI-enabled personalization, contextual learning linked to Indian ethos, competency and values-based assessment, teacher–AI collaboration, and ethical technology use grounded in Dharma-based principles. The findings suggest that the convergence of AI and Indic pedagogy can transform Indian higher education by creating a more humane, learner-centered, and technologically empowered learning environment. It supports NEP 2020’s vision of holistic, multidisciplinary, and future-oriented learning that fosters creativity, critical thinking, empathy, and global citizenship. The model offers practical pathways for universities, policymakers, and educators to implement AI tools while preserving India's civilizational strengths. This paper ultimately positions AI-Indic integration as a strategic catalyst for India's aspiration to become a global knowledge leader by 2047.

Health Technology Assessment (HTA) is a cornerstone of evidence for informing health policy and resource allocation globally. Rapid advancements and the proliferation of digital health technologies and artificial intelligence (AI) have prompted the re-examination of HTA processes and methods. While traditional approaches are manual and labor-intensive, HTA processes are now exploring the use of AI and other digital technologies for automation, decision support, and evidence synthesis. To date, however, there have been very limited studies that map the innovative technological solutions of HTA, the models of integration, and the associated barriers, facilitators, and governance considerations. As such, this scoping review aims to address this critical gap by mapping the landscape of the global knowledge and practices related to AI and DTs used in and for HTA and identifying the key barriers and enablers influencing their adoption, integration, and effective application within HTA processes. A scoping review will be conducted between August and November 2025, following the Arksey and O'Malley framework, enhanced by Joanna Briggs Institute (JBI) recommendations, and reported according to Preferred Reporting Items for Systematic Reviews and Meta‑Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Literature searches will be performed in electronic databases such as Medline (Ovid), Embase (Ovid), Global Health (Ovid), CINAHL (Ebsco), Scopus, Web of Science, and all regional indexes in the World Health Organization's Global Index Medicus, and other region-specific sources for studies published between 2020 and 2025. Eligible studies will include peer-reviewed articles and grey literature describing the integration of digitization, automation, and AI in global HTA processes. Dual independent screening, data extraction, and quality appraisal will be employed. Findings from this review will provide a map of how digitization, automation, and AI are integrated into HTA practice, highlighting key enablers, barriers, and knowledge gaps. The insights will be used to better guide researchers, policymakers, HTA agencies, and AI developers, further supporting future research and implementation strategies for better informed decision-making.

Abstract. The new global Feedback-based knowledge Repository for IntegrateD Assessments version 2.1 (FRIDAv2.1) Integrated Assessment Model (IAM) seeks to study the dynamics of the coupled human-Earth system. Connecting anthropogenic emissions to the resultant climate response is one part of the two-way feedback within this system, with the resultant climate impacts the other. This paper documents both the Climate Module within FRIDAv2.1, and the modified version separately simulated as a standalone simple climate model termed FRIDA-Clim version 1.0.1. This approach, based loosely on the existing FaIR simple climate model, simulates the key radiative forcings and the resultant temperature response, with process-based representations of the carbon cycle across the ocean, land, and atmosphere. When connected within the FRIDA IAM, it features deep connections to the other modules, being affected by processes such as water use for irrigation and land use change. In both uses, i.e. with the climate response interactively connected to the upstream human drivers and downstream climate impacts within the FRIDA IAM (coupled) and when ran separately as FRIDA-Clim driven by exogenous forcings (uncoupled), its climate drivers are simplified as compared to FaIR. This is to allow for this reduced set of key drivers to be interactively simulated within FRIDA, tightly coupling the evolution of the social and climate systems within the full model. Both the Climate Module and FRIDA-Clim are fully calibrated to accurately reproduce observations of key climate variables, with a systematic exploration of the uncertainty in the climate response. Together with the rest of the FRIDA model, this module is used to incorporate climate change systematically in the FRIDA System Dynamics IAM. As a standalone climate model, FRIDA-Clim comprises a simple climate model, enabling fast calculation of the global climate response to forcing; to explore this, the response of the model to both idealised CO2 emissions experiments and plausible future scenarios is also presented here. This setup will allow FRIDA-Clim to contribute to inter-model simple climate modelling initiatives, helping to explore the structural uncertainty in this modelling domain.

ABSTRACT Considering that World Class University (WCU) is always embedded in its national context as well as closely related to global knowledge systems, this study aims to establish a framework for integrating local dimensions into the WCU concept or what we call Globally Competitive and Locally Contributed University (GCLC-U). We use and extend Salmi’s 3-dimensional WCU model by adding the Local Relevance dimension to the model. Based on the literature review, we also try to operationalise the proposed framework and the four dimensions by formulating several related criteria to indicate and assess university quality that can be associated with these dimensions. The finding indicates that by adding a local relevance dimension to the proposed model consisting of 15 compartments compared to the new seven intersections, this framework can illustrate and differentiate locally relevant WCU (ABC) and locally relevant WCU or GCLC-U (ABCD). By upholding the notion of local relevance or local contribution, this study argues that an ideal university should no longer emphasise only global competitiveness and research-intensive universities, but also recognise universities that are globally competitive and locally rooted. Finally, implications for future research are discussed.

Coptisine, a significant isoquinoline alkaloid, has garnered extensive attention for its pharmacological activities, such as antibacterial and anti-inflammatory effects. In recent years, driven by the growing interest in natural medicinal plants, research on coptisine has intensified. However, a systematic bibliometric analysis to map the knowledge structure of this field remains lacking. This study retrieved publications related to coptisine from the Web of Science Core Collection (WoSCC) database covering the period 1995 to 2025 and conducted a bibliometric analysis to identify research hotspots and trends. Data analysis and visualization were performed using VOSviewer, CiteSpace, and the R package "bibliometrix". A total of 626 documents were identified, with China ranking first in publication volume, followed by South Korea. The Chinese Academy of Medical Sciences, the Chinese Academy of Sciences, and Chengdu University of Traditional Chinese Medicine were the most prominent institutions. The Journal of Ethnopharmacology published the most articles, and Hailin Qin, Xuegang Li, and Xiaoli Ye were among the most productive authors. The analysis revealed that early research focused on the extraction and purification of coptisine, while the pharmacological mechanisms of coptisine in cardiovascular diseases and cancer represent the current research hotspots. This study maps the global knowledge landscape of coptisine research, highlights emerging trends, and provides a valuable reference for future investigations.

ABSTRACT Academic freedom is considered a fundamental principle of higher education, indicating that scholars may engage in research without limitations, irrespective of geographical location, personal identity or institutional ties. However, trends in funding distribution, publication assessment, citation methodologies and promotion frameworks indicate a more limited reality. This article posits that the selection of research topics in global higher education is influenced not only by intellectual curiosity but also by incentive structures that favour specific subjects, methodologies and publication outlets, while making others professionally precarious. This paper utilises the sociology of knowledge, higher education studies and postcolonial and decolonial thought to introduce the concept of uneven universality, elucidating the mechanisms by which certain traditions achieve “universal” status while others are relegated to the category of local. The analysis of funding dependencies, journal hierarchies, accreditation systems, language dominance and evaluation metrics reveals that while academic autonomy is formally recognised, it remains structurally constrained. The article employs music scholarship as a case study to reconceptualise academic freedom as a conditional practice influenced by global governance. It concludes by proposing pathways towards more diverse forms of scholarly recognition within global higher education frameworks.

In the practical application of wind energy technology, the reliability of wind turbine systems is directly linked to the stable output of renewable electricity. Data imbalance in fault monitoring networks causes classification bias in diagnostic models, which increases the risk of misdiagnosis and ultimately undermines the stability of wind power. In the context of distributed monitoring, this issue is further exacerbated by the geographically dispersed layout of wind farms, which significantly amplifies data imbalance. To address these challenges, a global distribution-aware logits adjustment federated framework is proposed, along with a dynamic estimation mechanism for global fault distribution based on edge node logits. This mechanism accurately captures the global fault feature distribution and enhances attention to minority categories. Additionally, a feature compensation architecture for edge-cloud bidirectional collaboration is developed. By constructing a feature similarity matrix, knowledge distillation is performed at the feature level to transfer global feature knowledge to the edge, enabling local models to learn global common features while preserving the personalized feature extraction capabilities of edge nodes. On-site experiments at Kelmarsh and Xinjiang wind farms demonstrated that the proposed method achieved accuracies of 0.8617 and 0.9891, respectively, showcasing its effectiveness.

Abstract Accurate state-of-health (SOH) estimation of lithium-ion batteries under unseen operating conditions is essential for safe and efficient battery management systems. However, batteries exhibit high variability in degradation patterns across different working conditions, and labeled data are often unavailable in real-world engineering scenarios. To address these challenges, a novel multisource adversarial discrepancy regression network is proposed for unsupervised SOH estimation under diverse conditions. Initially, a shared-multibranch adversarial structure is presented to integrate both global knowledge sharing and source-specific refinement. Subsequently, a multi-level distribution alignment mechanism is developed to comprehensively mitigate domain discrepancies at the global, pairwise, and task-specific levels to enhance the transferability of degradation-sensitive features. Furthermore, a dynamic weighting strategy is designed to adaptively fuse predictions from multiple branches by jointly considering alignment quality and prediction uncertainty to enhance robustness and reliability. Extensive experiments conducted on public battery datasets demonstrate the effectiveness and generalization performance of the proposed approach in cross-condition SOH estimation.

Scholarly interest in the relationship between social media and cosmetic surgery has increased substantially, as digital platforms play an increasingly central role in shaping aesthetic norms, body image perceptions, and elective medical decision-making. However, the rapid expansion of this literature has yet to be systematically mapped at a global level. This study aims to provide a comprehensive bibliometric overview of research examining the relationship between social media and cosmetic surgery, with particular attention to publication trends, knowledge structures, collaborative patterns, and thematic evolution over time. A systematic bibliometric analysis was conducted on 525 publications retrieved from the Scopus and Web of Science databases, covering the period from 2010 to 2024. Studies were identified following the PRISMA 2020 framework and analyzed using VOSviewer to visualize annual publication trends, co-authorship networks, co-citation structures, and keyword co-occurrence clusters. Findings indicate an exponential rise in research activity post-2020, with the USA leading in publication output yet exhibiting relatively low international collaboration. Citation analysis highlights several seminal contributions that have shaped the field's clinical, psychological, and sociocultural dimensions. Thematic mapping reveals a notable shift from platform-specific studies (e.g., Facebook, Instagram) toward emerging concerns such as ethnic diversity, body dissatisfaction, medical ethics, and digital patient education. Furthermore, keyword trends suggest increasing academic attention to the psychosocial implications of algorithmic beauty standards. This bibliometric review demonstrates the rapid growth and increasing interdisciplinarity of scholarship on social media and cosmetic surgery, while also highlighting structural imbalances in global knowledge production. The findings clarify the intellectual landscape of the field and identify emerging research directions to inform future theoretical development and empirical inquiry. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Digital transformation and digital humanism are reshaping knowledge and interactions across diverse fields and industries. To adapt and fit within the modern digital economy, professional patterns and career pathways require critical and vital capabilities to herald new opportunities and prospects. In light of these dynamics, the research explores the implications of applied artificial intelligence and realities to bridge career prospects in the field of information science, while focusing on digital transformations to foster the development of novel applications. It evaluates essential emerging programs and applications to bridge and advance career projects in the field through the lens of digital transformation; demonstrates how to facilitate effective integration and adoption of applied artificial intelligence technologies and applications; explores factors that hinder these emerging trends and dynamic changes; and formulates a strategic framework to leverage applied artificial intelligence and digital transformation for future opportunities. Research applied content analysis and knowledge from diverse electronic journals, books, online databases, the Internet and the World Wide Web. Research publications and articles were identified and searched using a statistical approach of preferred reporting items for systematic reviews and meta-analyses (PRISMA) strategy and scoping review methodologies. A mixed method research design incorporating quantitative and qualitative approaches was applied to collect and analyze, with concurrent and sequential triangulation used to enhance the validity of the findings. First insights indicate that integration of emerging programs, such as artificial intelligence, machine learning, deep learning, generative artificial intelligence and large language models – reflects transformative technical expertise and strategic innovation in information science, which positions digital transformation as the defining framework to foster interdisciplinary competencies, enhance employability and advance sustainable technological and socio-economic development in the global knowledge economy. Second insights demonstrate that adoption of applied artificial intelligence depends on technological, human and ethical pillars, with digital infrastructure and cloud readiness emerging as the most influential. Third insights highlight multiple interrelated factors that hinder these emerging trends and dynamic changes - inadequate preparedness and training, limited institutional support and resources, resistance to change and lack of awareness of practical AI tools. Fourth insights determine a strategic framework to leverage artificial intelligence and digital transformation for future opportunities from curriculum coordination to technical instruction, as well as AI mentorship and leadership to effectively enhance market competitions and industrial portfolios.