The Disclosure-Detection Nexus in Generative AI: An Integrated Analysis of Ethical Implications in Academia
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The rapid integration of Generative Artificial Intelligence (GenAI) into academic settings presents a complex ethical landscape, necessitating a critical examination of how institutions can effectively balance transparent AI usage (disclosure) with the need to identify misuse (detection). This study employs a comprehensive structured literature review utilizing thematic analysis to explore the multifaceted ethical implications of GenAI. Key issues synthesized include academic integrity, evolving notions of authorship, equity of access, intellectual property rights (IPRs), data privacy, and human agency, particularly highlighting the challenges posed by the “black box” nature of GenAI models, the potential for deskilling, and impacts on critical thinking and trust. This study underscores the inherent conflicts and interdependencies between disclosure and detection, noting the significant unreliability and biases of current AI detection tools that often lead to false positives and erode trust. It argues for a strategic shift towards a synergistic balance, where robust disclosure fosters transparency and pedagogical integrity, minimizing reliance on unreliable detection as a reactive measure. Actionable recommendations are provided for policymakers, educators, and technology developers to foster responsible AI integration, preserve academic values, and mitigate associated risks. The study concludes by outlining crucial future research directions, including longitudinal impact studies and empirical evaluations of assessment redesigns, to address the evolving challenges posed by GenAI in higher education.
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