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Learning With Integrity: The Future Of Ethical Artificial Intelligence In Academia Abdurrahim; Zulfikar, Rizka; Purboyo; Widyanti, Rahmi; Basuki
Applied Business and Administration Journal Vol. 4 No. 2 (2025): Financial Accountability, Technological Innovation, and Sustainable Development
Publisher : Ebiz Prima Nusa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62201/abaj.v4i02.216

Abstract

This study explored the evolution of scholarly research addressing ethical concerns related to artificial intelligence (AI) within academic settings. Despite the growing use of AI technologies in higher education—ranging from instructional tools to administrative applications—limited empirical work has systematically examined how ethical issues have been conceptualized and discussed. A bibliometric analysis was conducted to address this gap, using data extracted from the Scopus database and 107 documents covering 2015 and 2025. The study employed the PRISMA method for data screening. Bibliometric mapping was performed using Biblioshiny-R, which enabled comprehensive visualization through co-occurrence networks, thematic maps, and trend analyses. The findings revealed a significant increase in scholarly output and interdisciplinary collaboration on AI ethics in academia. Key themes included algorithmic bias, transparency, accountability, fairness, and responsible innovation. Notably, the research highlighted a progressive shift from technical concerns toward more socially grounded issues such as inclusivity, data governance, and digital justice.  The study identified core publications shaping the field and suggested that ethical AI in education remains an emerging but critical area for future inquiry. These findings provide a robust foundation for developing evidence-based, globally relevant policy frameworks that promote fair, transparent, and accountable AI integration in higher education.
Tracing The Digital Transformation: A Bibliometric Investigation Of Artificial Intelligence Adoption In Higher Education Wicaksono, Teguh; Zulfikar, Rizka; Purboyo; Yulianti, Farida; Lamsah
Applied Business and Administration Journal Vol. 4 No. 2 (2025): Financial Accountability, Technological Innovation, and Sustainable Development
Publisher : Ebiz Prima Nusa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62201/abaj.v4i02.217

Abstract

This study aimed to track the digital transformation journey through the lens of AI adoption in higher education from 2010 to 2025. Using a data-based bibliometric method from Scopus, this study identified the dominant theories used in AI adoption intention studies and conceptual structures. The literature selection process was carried out systematically using the PRISMA method to ensure transparency and accuracy in document selection. Data analysis used bibliometric techniques to analyse the research landscape quantitatively and was conducted using VosViewer Software. The analysis results show that research on AI adoption intention has experienced an annual growth of 34.15%, with most publications using the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) approaches. Network visualisation revealed fragmentation in this research, where several clusters of theories develop separately without strong integration. Overlay visualisation showed a shift from technology acceptance model-based studies to exploration of ethical impacts, algorithm transparency, and AI regulation in higher education. Density visualisation confirmed that although technical factors have been widely studied, AI's social and policy aspects are still underexplored. This research provides a more comprehensive conceptual mapping and identifies research gaps that future studies can fill.