Mapping Global Publication Trends Studies on Artificial Intelligence: A Systematic Literature Review

Authors

  • Ika Malikatun Nisa Universitas Muhammadiyah Malang, Indonesia
  • Salahudin Universitas Muhammadiyah Malang, Indonesia

DOI:

https://doi.org/10.47709/ijmdsa.v5i2.7708

Keywords:

AI Governance, Ethics, Regulation, Policy, Accountability, Digital Transformation

Abstract

This study aims to map the development of research and scholarly publications on Artificial Intelligence (AI) Governance, with a particular focus on policy, ethics, and regulatory frameworks. The review seeks to identify major research trends, the evolution of key themes, and the interconnections among topics within the global AI governance literature. A Systematic Literature Review (SLR) approach was employed following the PRISMA protocol to select relevant articles from the Scopus database. A total of 2,815 articles were identified and analyzed using VOSviewer and CiteSpace to examine keyword relationships, publication trends, and thematic structures that illustrate the intellectual landscape of AI governance research during the period 2019–2025. The findings reveal five major thematic clusters in AI governance studies: AI policy and regulation, digital governance and public administration, algorithmic transparency and trust, AI adoption and industry transformation, and ethics and accountability. Among these, AI policy and regulation emerges as the most dominant theme, indicating that policy and regulatory issues remain central to global AI governance discourse. In addition, the relationships among clusters highlight strong linkages between ethics, public trust, and digital transformation in shaping responsible AI governance systems. This study contributes both conceptually and methodologically to AI governance research by providing a comprehensive mapping of global scholarly trends and thematic structures. However, the study is limited by its reliance on a single database (Scopus). Future research is therefore encouraged to incorporate additional databases such as Web of Science and EBSCO to expand the scope of analysis.

References

Bruneault, F., & Laflamme, A. S. (2021). AI Ethics: how can information ethics provide a framework to avoid usual conceptual pitfalls? An Overview. AI and Society, 36(3), 757–766. https://doi.org/10.1007/s00146-020-01077-w

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 376(2133). https://doi.org/10.1098/rsta.2018.0080

Chkarka, F., & Fatmi, H. (2026). Comparative Examination of Master’s Students’ and Faculty Members’ Maintenance of Academic Integrity in the Age of AI. Journal of Academic Ethics, 24(1). https://doi.org/10.1007/s10805-025-09684-2

Dagdag, D. T. (2021). Public governance in rural ecotourism: The case of developing economy. Journal of Governance and Regulation, 10(2 Special Issue), 269–289. https://doi.org/10.22495/jgrv10i2siart8

Fang, M., Nguyen, V. T., Le Minh, T., Louie, J., Pham, L. N., & Hewson, C. (2026). Leadership networks: Shaping AI innovations through responsible practices in Vietnamese tourism and hospitality firms. Tourism Management, 113. https://doi.org/10.1016/j.tourman.2025.105317

Kapsalis, P., Rimassa, G., Zeydan, E., Via, S., Risso, F. G., Chiasserini, C. F., & Vivo, G. (2024). A Next Generation Architecture for Internet of Things in the Automotive Supply Chain for Electric Vehicles. 428–433. https://doi.org/10.1145/3641512.3690035

Kist, C. (2024). Discursive AI Infrastructures: Envisioned and Overlooked Museum Futures. Proceedings of the Association for Information Science and Technology, 61(1), 194–204. https://doi.org/10.1002/pra2.1020

Liebig, L., Güttel, L., Jobin, A., & Katzenbach, C. (2024). Subnational AI policy: shaping AI in a multi-level governance system. AI and Society, 39(3), 1477–1490. https://doi.org/10.1007/s00146-022-01561-5

Mahendra, P., Doshi, P., Verma, A., & Shrivastava, S. (2025). A Comprehensive Review of AI and ML in Data Governance and Data Quality. 356–361. https://doi.org/10.1109/ICICI65870.2025.11069464

Nzembayie, K. F., & Urbano, D. (2026). Generative AI platforms as institutional catalysts of digital entrepreneurship: Enablement, dependence & power dynamics. Technology in Society, 84. https://doi.org/10.1016/j.techsoc.2025.103074

Radanliev, P. (2024). Integrated cybersecurity for metaverse systems operating with artificial intelligence, blockchains, and cloud computing. Frontiers in Blockchain, 7. https://doi.org/10.3389/fbloc.2024.1359130

Radanliev, P., Atefi, K., Santos, O., & Maple, C. (2026). Integrating agentic risk signalling in trusted research environments: Automating VEX with Agent2Agent protocols and model context protocol (MCP) in SACRO and TREvolution pipelines. Computer Standards and Interfaces, 96. https://doi.org/10.1016/j.csi.2025.104079

Vivoda, V., Borja, D., & Krame, G. (2026). AI’s energy paradox: Governing the trilemma of security, justice, and sustainability. Extractive Industries and Society, 25. https://doi.org/10.1016/j.exis.2025.101773

Downloads

Published

2026-04-06

How to Cite

Nisa, I. M., & Salahudin, S. (2026). Mapping Global Publication Trends Studies on Artificial Intelligence: A Systematic Literature Review. International Journal of Multidisciplinary Sciences and Arts, 5(2), 383–394. https://doi.org/10.47709/ijmdsa.v5i2.7708