Critical Success Factors and Challenges of AI Adoption in Higher Institute of Sciences and Technology: A Case Study of Libya

Abstract

The integration of Artificial Intelligence in technical and vocational education stands as a powerful catalyst for preparing a workforce aligned with the demands of the Fourth Industrial Revolution. In post-conflict Libya, Higher Institutes of Science and Technology (HISTS) face a critical transition: they must recover from systemic geopolitical disruptions while modernizing their pedagogical and administrative frameworks to bridge the gap between academic theory and market realities. This systematic review investigates the critical success factors and structural challenges of adopting Artificial Intelligence in Libyan higher technical institutes. Adopting an integrated framework that synthesizes the Technology-Organization-Environment model, the Diffusion of Innovations theory, and the Technology Acceptance Model, this study analyzes empirical data compiled from multiple academic cohorts across Libya, representing over eight hundred combined institutional stakeholders. The findings reveal a significant dichotomy between user readiness and institutional capacity. While students demonstrate a highly optimistic attitude toward Artificial Intelligence, and pilot programs report academic performance gains such as a fifteen percent improvement in student grades, adoption remains severely constrained by systemic barriers. These barriers include severe technological infrastructure deficits, a sixty percent faculty training gap, linguistic biases in existing software tools, and the absence of a cohesive national regulatory framework. Conversely, proactive administrative leadership, curriculum co-design with local industries, and the localized implementation of institutional governance policies are identified as core success factors. This study synthesizes these dimensions to construct a phased, capacity-driven implementation roadmap designed to facilitate sustainable, equitable, and culturally aligned digital transformation within resource-constrained educational environments