Mapping the future of distance higher education: a bibliometric-SLR integration on Industry 5.0 technologies toward world-class online university development

Global higher education is undergoing rapid transformation driven by digitalization, globalization and the growing demand for flexible and inclusive learning. Online learning has evolved from a complementary approach into a strategic pillar of modern education, accelerated by the COVID-19 pandemic and the expansion of hybrid and fully online models supported by advanced learning technologies (Husain, 2025). However, ensuring quality, competitiveness and global relevance in distance higher education remains a critical challenge, requiring not only technological infrastructure but also innovative pedagogy and adaptive institutional strategies (Pham et al., 2025).

In response, Industry 5.0 has emerged as a transformative framework integrating technologies such as artificial intelligence, extended reality, Internet of Things (IoT) and blockchain to enable adaptive, personalized and inclusive learning environments (Supriya et al., 2024). These developments redefine the concept of world-class online universities, shifting the focus from physical prestige toward digitally connected, human-centered and globally competitive learning ecosystems that emphasize flexibility, inclusivity and continuous innovation (Awotunde et al., 2023). Such universities are characterized by their ability to balance technological sophistication with human-centered pedagogy, ensuring long-term adaptability, inclusivity and educational quality in diverse global contexts.

Recent studies highlight growing interest in Industry 5.0 applications in higher education, including bibliometric analyses, empirical investigations and emerging pedagogical frameworks. These studies emphasize human–machine collaboration, digital competencies and institutional transformation, yet remain fragmented and often context-specific (Grabowska et al., 2022; Naixin and Leong, 2024). Moreover, comprehensive global syntheses linking Industry 5.0 adoption to the development of world-class online universities are still limited.

Therefore, this study aims to address these gaps by integrating bibliometric and systematic literature review (SLR) approaches to map research trends, identify key themes and collaborations and provide a holistic understanding of how Industry 5.0 technologies shape the future of distance higher education.

This study employs an integrated bibliometric-systematic literature review (SLR) approach to examine Industry 5.0 technologies in distance higher education. By combining co-citation, co-word and bibliographic coupling analyses, it maps research trends, key contributors and thematic structures. Data were retrieved exclusively from the Scopus database due to its broad multidisciplinary coverage and robust citation indexing. The search was conducted on June 1, 2025, using TITLE-ABS-KEY fields with keywords related to distance higher education and Industry 5.0 technologies (e.g. artificial intelligence, virtual reality (VR) and augmented reality (AR), IoT and personalized learning). The main search terms included TITLE-ABS-KEY ((“distance education” OR “online learning” OR “online university” OR “virtual university” OR “digital university” OR “remote learning” OR “online higher education” OR “online teaching”) AND (“artificial intelligence” OR “virtual reality” OR “augmented reality” OR “Internet of things” OR “robotics” OR “collaborative robots” OR “personalized learning”)).

The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework (Page et al., 2022) to ensure a systematic and transparent selection process. Inclusion criteria comprised (1) research articles, (2) final publications, (3) journal sources, (4) English language and (5) publication period between 2019 and 2025, aligning with the emergence of Industry 5.0. The initial search yielded 7,883 records, which were filtered based on inclusion criteria, resulting in 1,734 articles. After full-text screening, 1,556 articles were retained for analysis. The selection process is illustrated in Figure 1.

This study integrates performance analysis and science mapping using VOSviewer and Bibliometrix (Arruda et al., 2022). Performance analysis identifies key contributors (authors, journals and countries), while science mapping reveals intellectual structures and thematic clusters. These results support the SLR in identifying dominant research areas and gaps.

Scientific output on Industry 5.0 in distance higher education in Figure 2 shows a strong upward trend, increasing from 77 publications in 2019 to a peak of 370 in 2024, indicating rapidly growing academic interest in technology-driven online university development. The slight decline in 2025 is likely due to incomplete data.

The subject area analysis in Figure 3 confirms the multidisciplinary nature of the field, dominated by computer science, followed by social sciences and engineering, highlighting the integration of technological, pedagogical and system-level perspectives. Additional contributions from mathematics, medicine, psychology and business-related fields reflect the increasing importance of analytics, human behavior and governance in shaping world-class online universities.

Leading contributions in this field are concentrated in high-impact, technology-oriented journals (see Table 1) such as IEEE Access and IEEE Internet of Things Journal, reflecting strong productivity and influence in advancing Industry 5.0 applications. Meanwhile, Computers and Education demonstrates the highest cumulative impact, indicating its central role in shaping research on digital learning technologies. Other journals, including Education and Information Technologies and the International Journal of Emerging Technologies in Learning, show a balance between productivity and impact, highlighting the interdisciplinary nature of the field.

The most influential publications (Table 2) further confirm the dominance of artificial intelligence (AI), VR and adaptive learning as key research themes. Highly cited studies emphasize immersive learning environments, AI-driven personalization and digital competencies, reflecting the rapid evolution of technology-enhanced education in the post-pandemic era.

China dominates scientific production in Table 3, followed by the United States of America and India, indicating their central role in advancing Industry 5.0 research in distance higher education. However, higher average citation impact in countries such as Norway and Hong Kong suggests more focused and influential contributions. The United States of America maintains strong research quality, while several European countries show a balance between productivity and impact. Although Indonesia ranks among the top contributors, its relatively lower citation impact highlights the need to strengthen research quality and international collaboration. Global collaboration patterns further position China and the United States of America as key hubs, with increasing participation from both developed and developing countries.

The thematic evolution map is a visualization technique in bibliometric analysis that describes the development and shift of research themes from one period to the next (Arruda et al., 2022). Based on Figure 4, there is a significant transformation of research themes in the period 2019–2023 to 2024–2025. The theme of e-learning remains consistent as the primary focus, demonstrating its central role in developing distance higher education. Meanwhile, the early period's dominant theme of learning systems developed into various advanced themes such as the IoT, task analysis, decision-making and contrastive learning. It reflects integrating innovative technology and analytics systems in managing digital learning. In addition, AI emerged as an increasingly powerful topic in the latter period, showing a significant increase in its utilization for personalization, automation and data-driven educational decision-making.

Figure 5 presents the evolution of key research themes from 2024 onward using a maturity matrix that assesses keyword density and centrality (Aria and Cuccurullo, 2017). Studies show a shift in developing world-class online universities through Industry 5.0 technologies. “E-learning” and “online learning” remain dominant, forming the foundation of digital transformation. Emerging technologies such as IoT, deep learning and reinforcement learning are basic yet developing themes, while niche topics like adaptive control and decision-making emphasize intelligent adaptation. New directions, including adversarial and contrastive learning, highlight efforts to enhance personalization, quality and security in future online education.

Co-occurrence analysis identifies conceptual relationships and dominant research themes within the field. As shown in Figure 6, the keyword network is structured into 10 thematic clusters, reflecting the multidimensional development of distance higher education in the Industry 5.0 era.

These clusters can be grouped into four major dimensions. First, the technological infrastructure dimension (Clusters 1, 3, 8 and 10) highlights AI-driven systems, edge and/or fog computing, blockchain and large-scale digital platforms that enable adaptive, secure and scalable learning environments. Second, the pedagogical and psychological dimension (Clusters 2, 4 and 5) emphasizes learner-centered approaches, including gamification, self-regulation, motivation and affective computing for personalized learning. Third, the immersive and interactive learning dimension (Clusters 7 and 9) reflects the growing role of generative AI, VR and metaverse environments in enhancing engagement and interaction. Finally, the inclusivity and lifelong learning dimension (Cluster 6) underscores the importance of accessibility, adult learning and data-driven decision-making. Overall, the 10 clusters illustrate a co-evolution of technology, pedagogy and governance, indicating a shift toward adaptive, personalized and human-centered learning ecosystems in the development of world-class online universities. Detailed descriptions of each cluster are summarized in Table 4.

Bibliographic coupling identifies intellectual linkages among studies based on shared references, revealing the structural composition of the research field. As shown in Figure 7, the analysis generated 12 interconnected clusters representing the evolution of Industry 5.0 technologies in distance higher education.

Rather than operating independently, these clusters can be synthesized into four major thematic axes. First, the human-centric AI axis (Clusters 1, 3, 5, and 11) emphasizes AI-driven personalization, learning analytics and generative AI applications such as chatbots, highlighting the shift toward adaptive, data-driven and learner-centered systems. This stream also underscores the importance of integrating ethical AI literacy and human–AI interaction in educational contexts.

Second, the immersive and embodied learning axis (Clusters 2, 6, 7 and 9) focuses on VR, AR and metaverse-based environments that support experiential, interactive and collaborative learning. These technologies are increasingly integrated with pedagogical frameworks to enhance engagement, inclusivity and cognitive immersion. Third, the decentralized and intelligent infrastructure axis (Clusters 4, 8 and 10) highlights the role of edge and/or fog computing, reinforcement learning, IoT and distributed architectures in enabling scalable, real-time and resource-efficient learning systems. This reflects a transition toward autonomous and adaptive educational ecosystems.

Finally, the ethical and governance axis (Cluster 12) addresses critical issues of trust, privacy, bias and accountability in AI-driven education, emphasizing the need for transparent and responsible implementation frameworks. Overall, these clusters demonstrate a paradigm shift from conventional e-learning toward integrated ecosystems that combine intelligent technologies, immersive environments and ethical governance. A detailed description of each cluster is provided in Table 5.

This study advances e-learning theory by integrating Industry 5.0 into a human-centric, AI-driven and immersive learning framework. It highlights a shift from conventional e-learning toward adaptive and intelligent ecosystems, incorporating perspectives such as self-determination theory, community of inquiry and AI-based learning analytics. Three key conceptual contributions emerge: AI–human pedagogical synergy, where AI augments rather than replaces educators; immersive–reflexive learning architecture, combining VR and AR with learner autonomy, and technological–ethical equilibrium, emphasizing the balance between innovation and ethical integrity. These findings redefine the notion of a world-class university as flexible, inclusive and digitally connected rather than physically bounded.

From a managerial perspective, the results emphasize the need for institutional transformation toward data-driven and adaptive learning systems. Higher education institutions should strengthen AI-enabled learning management systems, integrate emerging technologies (e.g. IoT and edge computing) and promote multidisciplinary collaboration. This also requires redefining academic roles, where educators must develop competencies in AI, immersive technologies and digital pedagogy. Curriculum design should become more flexible, modular and responsive to learner data to support personalized and engaging learning experiences.

From a policy perspective, the findings call for a paradigm shift toward supporting digital infrastructure, ethical AI governance and global collaboration. Policymakers need to ensure transparency, accountability and fairness in AI-driven education, particularly in areas such as assessment and data privacy. In addition, policies should encourage international research collaboration and investment in emerging technologies to accelerate the development of globally competitive online universities, especially in developing countries.

This study maps the strategic direction of distance higher education in the Industry 5.0 era using bibliometric and SLR. Research since 2019 shows significant growth with strong international collaboration. Key themes include AI, deep learning, IoT and VR, driving personalized, adaptive and data-driven learning. The literature highlights a shift from e-learning toward intelligent, immersive systems integrating computer science, pedagogy and social sciences. Cluster analyses stress cross-disciplinary approaches linking technology, instructional design, governance and ethics. Building world-class online universities requires systemic transformation through advanced technologies, data-driven personalization, global collaboration, inclusivity and sustainability. This study provides foundations for future policies, curricula and strategies.

This study has several limitations that should be acknowledged. First, the bibliometric-SLR approach relies on secondary data, which may not fully capture the dynamic evolution of Industry 5.0 in practice. Second, the use of a single database (Scopus) and English-language publications may limit geographical representation. Third, practical and policy dimensions, particularly in developing country contexts, remain underexplored. Future research is encouraged to adopt mixed-method approaches, incorporate multiple databases and further examine the ethical, social and long-term impacts of Industry 5.0 on equity, access and institutional effectiveness in online higher education.

Muhammad Alfarizi contributed to conceptualization, methodology, data curation, formal analysis, funding acquisition, visualization and preparation of the original draft, as well as reviewing and editing the manuscript. Ngatindriatun contributed to methodology, data curation, formal analysis, project administration, preparation of the original draft and manuscript review and editing and validation. Suci Megawati contributed to investigation, resources, software, validation and manuscript review and editing.

The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20077177.

The authors would like to express their sincere gratitude to the three collaborating universities for their valuable support, contributions and academic collaboration throughout this research. The authors also acknowledge the use of artificial intelligence (AI)-assisted tools solely for language refinement and grammar improvement. All intellectual content, analysis and interpretations presented in this manuscript are entirely the responsibility of the authors. The manuscript has been carefully reviewed to ensure academic integrity, originality and full compliance with ethical publication standards.