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September 23, 2025East African Journal of Arts and Social Sciences3 citationsOpen Access

The Dark Side of Generative AI in Higher Education: Challenges, Ethical Concerns, and Implications

SOSalmon Oliech OwidiMasinde Muliro University of Science and Technology JLJoanne Nabwire LyandaMasinde Muliro University of Science and Technology EOElijah Owuor OkonoMasinde Muliro University of Science and Technology

Key Points

The meta-analysis reveals significant ethical concerns associated with generative AI in higher education, particularly around academic integrity and data privacy.
Key findings emphasize that 300 peer-reviewed articles were analyzed, highlighting issues of algorithmic bias and institutional readiness.
Employing a thematic coding approach, the study identified recurrent challenges in accountability, assessment validity, and the roles of faculty.
The implications suggest a need for structured training and clear policy development for effective AI integration in academic settings.

Abstract

This paper presents a meta-analysis of literature examining the dark side of generative artificial intelligence (GenAI) in higher education, with a focus on the ethical, pedagogical, and institutional challenges arising from its rapid integration. Drawing from a comprehensive review of 300 peer-reviewed articles, reports, and policy documents published between 2018 and 2024, the study synthesises key concerns across academic integrity, algorithmic bias, data privacy, faculty roles, and policy development. Sources were systematically retrieved from major academic databases, including Scopus, Web of Science, ERIC, JSTOR, IEEE Xplore, and Google Scholar. The literature was selected based on inclusion criteria that emphasised peer-reviewed status, relevance to higher education, and a clear discussion of the ethical or practical challenges of GenAI deployment. Exclusion criteria involved studies focused solely on K–12 education, regions outside of Africa without relevance to the African higher education context or works that concentrated purely on the technical development of AI without exploring its pedagogical, ethical, or institutional implications within educational environments. The meta-analysis employed a thematic coding approach, identifying recurrent concerns in areas such as accountability, fairness, accessibility, assessment validity, and institutional readiness. The appropriate and improper uses of ChatGPT and related technologies in academic work, including assignments, research, and tests, should be clearly defined in these rules. The study highlights the necessity of structured training programs for teachers and students in addition to policy creation. The findings provide a foundation for future research and offer actionable insights for developing ethically grounded, context-sensitive strategies for AI integration in higher education.

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