Bibliometric Analysis of Human‑Centered AI Research in Southeast Asia (2015–2025)

Main Article Content

Loso Judijanto
Ni Desak Made Santi Diwyarthi

Abstract

This study does a⁠ bibli⁠ometric analysis of human-cent⁠ered artificial intelligence (HCAI) research in Southeast Asia from 2015 to 2025,⁠ with th⁠e objective of mapping publishing trends, conceptual frameworks, and collaborative networks in the region. The investigation, utilizing the Scopus database an⁠d v⁠isual⁠ization tools l⁠ike VOSviewer and Bibliometrix, indicates that fundamental AI con⁠cepts—namely artificial inte⁠lligence, machine⁠ learning, and deep learni⁠ng—function as pivotal anc⁠hors in the literature. These technical themes increasingly converge with human-centered areas, including explain⁠able AI, user-centered design, ethic⁠al tech⁠nology, and healthcare applications. The research designates S⁠ingapore as the pr⁠eeminent center for regional and international col⁠laboration, succeeded by Malaysia, Indonesia, Vietnam, and the Philippines. Institutional networks⁠ prioritiz⁠e significant contributions from technological universities⁠ and⁠ medical research institutions. The results demonstrate a distinct transition towards in⁠tegrative and value-oriented AI r⁠esearch that incorporates transparency, user empowerment, and social accountability in technical⁠ advancemen⁠t. This study offers a comprehensive assessment of current scholarship and identifies prospects for future research, pol⁠icymaking, and international collaboration in pr⁠omoting human-centered AI throu⁠gh⁠out Southeast Asia.

Article Details

How to Cite
Judijanto, L., & Diwyarthi, N. D. M. S. (2025). Bibliometric Analysis of Human‑Centered AI Research in Southeast Asia (2015–2025). The Eastasouth Journal of Information System and Computer Science, 3(02), 214–226. https://doi.org/10.58812/esiscs.v3i02.799
Section
Articles

References

B. Shneiderman, “Human-centered artificial intelligence: Reliable, safe & trustworthy,” Int J Hum Comput Interact, vol. 36, no. 6, pp. 495–504, 2020, doi: 10.1080/10447318.2020.1741118.

H. Ashari and T. P. Nugrahanti, “Household economy challenges in fulfilling life needs during the Covid-19 pandemic,” Global Business and Economics Review, vol. 25, no. 1, pp. 21–39, 2021.

H. Suresh and J. V Guttag, “A framework for understanding sources of harm throughout the machine learning lifecycle,” Equity and Access in Algorithms, Mechanisms, and Optimization, vol. 1, no. 1, pp. 1–12, 2021.

I. Agustina, H. Khuan, B. Aditi, S. A. Sitorus, and T. P. Nugrahanti, “Renewable energy mix enhancement: the power of foreign investment and green policies,” International Journal of Energy Economics and Policy, vol. 13, no. 6, pp. 370–380, 2023.

ASEAN Secretariat, “ASEAN AI Governance and Ethics Guidelines,” ASEAN Secretariat, Jakarta, 2021.

Indonesia Ministry of Communication and Information Technology, “Indonesia National AI Strategy 2020–2045: Making Indonesia 4.0,” Ministry of Communication and Information Technology, Jakarta, 2020.

F. Rahim and D. Seng, “Responsible artificial intelligence adoption in Asia: A systematic review,” AI and Ethics, vol. 3, no. 4, pp. 913–928, 2023, doi: 10.1007/s43681-022-00246-9.

O. Kudina and P. P. Verbeek, “Ethics from within: Google Glass, the Collingridge dilemma, and the mediated value of privacy,” Sci Technol Human Values, vol. 44, no. 2, pp. 291–314, 2019, doi: 10.1177/0162243918793711.

N. Redi, S. Lim, and C. Tan, “Sociotechnical perspectives on AI in Southeast Asia,” Inf Commun Soc, vol. 25, no. 6, pp. 789–806, 2022, doi: 10.1080/1369118X.2021.1896837.

N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J Bus Res, vol. 133, pp. 285–296, 2021, doi: 10.1016/j.jbusres.2021.04.070.

J. Jobstreibizer, T. Beliaeva, M. Ferasso, S. Kraus, and A. Kallmuenzer, “The impact of artificial intelligence on business models: a bibliometric-systematic literature review,” Management Decision, vol. 63, no. 13, pp. 372–396, 2025.

I. Zupic and T. Čater, “Bibliometric methods in management and organization,” Organ Res Methods, vol. 18, no. 3, pp. 429–472, 2015, doi: 10.1177/1094428114562629.

M. Aria and C. Cuccurullo, “Bibliometrix: An R-tool for comprehensive science mapping analysis,” J Informetr, vol. 11, no. 4, pp. 959–975, 2017, doi: 10.1016/j.joi.2017.08.007.

Y. Guo, Z. Hao, S. Zhao, J. Gong, and F. Yang, “Artificial intelligence in health care: bibliometric analysis,” J Med Internet Res, vol. 22, no. 7, p. e18228, 2020.

N. J. van Eck and L. Waltman, “Visualizing bibliometric networks,” in Measuring Scholarly Impact, Y. Ding, R. Rousseau, and D. Wolfram, Eds., Cham: Springer, 2014, pp. 285–320. doi: 10.1007/978-3-319-10377-8_13.

M. G. Sono, “Bibliometric Analysis of The Term ‘Marketing Sustainability,’” West Science Interdisciplinary Studies, vol. 1, no. 06, pp. 314–325, 2023.

D. Wang, Q. Yang, A. Abdul, and B. Y. Lim, “Designing theory-driven user-centric explainable AI”.

A. S. Albahri et al., “A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion”.

M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A Systematic Review of Human-Computer Interaction and Explainable Artificial Intelligence in Healthcare with Artificial Intelligence Techniques”.

V. Sounderajah et al., “Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: The STARD-AI protocol”.

H. Li, R. Zhang, Y.-C. Lee, R. E. Kraut, and D. C. Mohr, “Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being”.

V. Sounderajah et al., “A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI”.

U. A. Usmani, A. Happonen, and J. Watada, “Human-Centered Artificial Intelligence: Designing for User Empowerment and Ethical Considerations”.

B. X. Tran et al., “The current research landscape of the application of artificial intelligence in managing cerebrovascular and heart diseases: A bibliometric and content analysis”.

L. Stapleton et al., “Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders”.

P. Shah, D. Mishra, M. Shanmugam, B. Doshi, H. Jayaraj, and R. Ramanjulu, “Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy—Artificial intelligence versus clinician for screening”.