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.

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