Artificial Intelligence in Higher Education: Safety, Reliability, and Risk Management in a Socio-Technical System
TL;DR
The findings indicate that artificial intelligence enhances operational efficiency and supports academic tasks; however, its use for content generation is associated with reduced cognitive engagement and risks to knowledge reliability and academic integrity.
Abstract
The study conceptualizes artificial intelligence in higher education as a socio-technical system in which human cognition, institutional norms, and algorithmic processes interact. It aims to develop a model of functional differentiation that preserves students’ cognitive responsibility while enabling the safe and effective use of artificial intelligence. The study employs a mixed-methods design combining conceptual analysis with empirical data from student surveys conducted in higher education institutions in Georgia. The findings indicate that artificial intelligence enhances operational efficiency and supports academic tasks; however, its use for content generation is associated with reduced cognitive engagement and risks to knowledge reliability and academic integrity. The study concludes that clear functional boundaries, process-oriented assessment, AI literacy, and institutional governance are essential for responsible integration.