Institutional and Governance Challenges in Recognizing Content Generated by Artificial Intelligence in Higher Education

Abstract

The development and spread of generative artificial intelligence (AI) tools pose new challenges for higher education, not only at the pedagogical level, but also at the institutional and management levels. Assessing student performance, maintaining academic integrity, and ensuring the reliability of quality assurance and evaluation processes are becoming increasingly strategic issues for organizations. Many higher education institutions already have guidelines and regulations on artificial intelligence, but there is currently no uniform, reliable solution that can be applied at the institutional level to determine the extent to which a given product can be considered the student's own work and where AI-generated content begins. The limited reliability of currently available AI detection tools (e.g., Turnitin, Quillbot) further increases decision-making uncertainty and poses a risk to assessment and quality assurance systems. The aim of the study is to review the possibilities of identifying content generated by generative AI based on the literature, focusing its linguistic, content, and technical characteristics, and to analyse its institutional applicability from the perspective of higher education management and quality assurance. The results may contribute to more informed management decisions on regulating the use of AI, transforming assessment practices, and maintaining academic integrity.