Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study

Abstract

Heba Kamal Chami, Laurence Ajaka and Faten Monzer Chami

This study examines the interplay between digital transformation initiatives, artificial intelligence (AI) integration, and student satisfaction in Lebanese higher education. An explanatory sequential mixed-methods approach was employed, with data collected from 300 undergraduates at three Lebanese University campuses. Quantitative findings revealed significant dissatisfaction with current digital systems (78%), together with substantial optimism concerning AI’s potential for personalized learning (85%). Regression analyses identified infrastructure quality (β = .42, p < .001), system reliability (β = .38, p < .001), and technical support (β = .31, p = .002) as significant predictors of satisfaction, accounting for 58% of the variance. Qualitative content analysis identified four main barriers: interface usability, functionality gaps, system integration challenges, and inadequate technical support. These results highlight the importance of human-centered, contextually appropriate digital transformation that prioritizes robust infrastructure, integrated systems, and sustainable AI implementation.

PDF