The Disclosure-Detection Nexus in Generative AI: An Integrated Analysis of Ethical Implications in Academia

This article analyses the ethical challenges created by the growing use of Generative Artificial Intelligence (GenAI) in higher education, with particular focus on the tension between AI use disclosure and AI-generated content detection. Through a structured literature review and thematic analysis, it shows that current AI detection tools are unreliable, biased, and prone to false positives, often undermining academic trust and fairness. The study argues for a shift away from surveillance-driven detection toward a disclosure-led, trust-based approach in which transparent AI use supports academic integrity, while detection serves only as a limited, human-augmented safeguard. It offers practical recommendations for institutions, educators, and policymakers on disclosure policies, assessment redesign, AI literacy, and ethical governance, and outlines future research directions for responsible and sustainable GenAI integration in academia.