Enhancing University Lecturers’ Digital Competence through Generative Artificial Intelligence: A Quasi-Experimental Study in Peru

Abstract

The post-COVID digital surge exposed a gap between 21st-century demands and Peruvian university lecturers’ actual technological skills, threatening educational quality advocated by SDG 4. This study examined whether generative artificial intelligence (GAI) enhances lecturers’ digital competence. A quasi-experimental pre/post-test design involved 60 lecturers (30 experimental, 30 control). Over eight weeks the experimental group completed fourteen online workshops on GAI tools (ChatGPT, Gemini, Claude, Overleaf AI) aligned with the DigCompEdu framework. Digital competence was assessed with the validated Tourón et al. questionnaire (68 items; α = .98). After checking normality and homogeneity, paired-t, Mann-Whitney U and Cohen’s d were applied. The experimental group achieved significant gains across all five dimensions (p < .001), with mean increases of 0.75-1.22 points and a very large overall effect (d = 2.05); digital-content creation improved most. The control group changed negligibly (≤ 0.09). Findings show that GAI effectively narrows lecturers’ digital divide, fosters pedagogical innovation and enables personalised learning, provided its adoption is coupled with ethical and critical training.