Learning With Integrity: The Future Of Ethical Artificial Intelligence In Academia

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.