Ethical frameworks for AI and academic integrity regulations

Abstract

This article analyzes how international ethical principles on artificial intelligence can be transformed into institutional criteria for academic integrity in teaching and research. Based on qualitative research using comparative document analysis, the author reviews regulatory frameworks, specialized literature, and university guidelines to identify areas of convergence, tension, and gaps. The text argues that, although a solid international ethical foundation already exists on issues such as transparency, human oversight, accountability, and equity, universities still exhibit insufficient, reactive, and ineffective regulatory responses to generative AI. Among its main contributions is the proposal to articulate AI ethics with a broader vision of academic integrity, not limited to plagiarism, but understood as ethical training, responsible authorship, and a redesign of assessment practices. Its value lies in offering a useful roadmap for updating institutional policies in higher education, especially in Latin American contexts where binding regulatory frameworks are still lacking.