The Ethics and Accuracy of Generative AI in Higher Education: Evaluating Student Reliance on AI Writing Tools, Epistemic Vulnerability, and Pedagogical Transformation

Abstract

The rapid integration of Large Language Models (LLMs) and generative writing assistants into higher education represents a structural inflection point in academic composition, cognitive engagement, and institutional evaluation. While contemporary discourse frequently bifurcates between uncritical techno-optimism and blanket prohibition, this paper undertakes a systematic, empirical, and epistemic evaluation of student reliance on generative artificial intelligence (GenAI) writing tools. We investigate the dual challenges of algorithmic accuracy—specifically stochastic hallucination, context truncation, and the fabrication of scholarly provenance—and ethical dilemmas concerning cognitive offloading, epistemic vigilance, authorial authenticity, and surveillance-oriented integrity policing. Our analysis reveals that uncritical student reliance leads to cognitive schema bypassing, wherein the essential struggle of rhetorical synthesis is substituted with passive editorial curation. Furthermore, we demonstrate that statistical AI detection instruments suffer from severe false-positive asymmetries that systematically penalize non-native English writers while remaining trivial to evade through basic prompt manipulation. Finally, we articulate an actionable, process-oriented pedagogical framework comprising authentic assessment architecture, student-led algorithmic auditing, and a three-tiered institutional governance model designed to cultivate genuine epistemic agency in an automated landscape.