From Digital Divide to AI-Ready Classrooms: Lessons from COVID-19 for Designing Equitable AI-Supported Education in Jordan

Abstract

This study investigates Jordanian students’ experiences with emergency online learning during the COVID-19 pandemic, focusing on how resource limitations and access inequalities shape learning processes and outcomes. It further considers how lessons from the digital divide can inform the development of equitable, AI-ready education systems in Jordan. Drawing on Cooper’s framework, the Digital Inequality perspective and the Community of Inquiry model, the analysis explores the role of infrastructure, household resources, and instructional interaction. A non-probability sampling strategy combining purposive and snowball sampling techniques was employed. Teachers and parents from diverse geographical regions were selected to ensure broad representation and were invited to disseminate the survey through their networks. The online survey yielded 2,759 valid responses from students in public (93.6%) and private (6.4%) schools. The findings reveal substantial gaps between access and effective learning. Only 47.7% of students perceived online learning as effective, while 81.4% reported difficulties in understanding complex concepts. Resource constraints were widespread: 90% relied primarily on mobile phones, 82.2% shared devices, and 81.1% experienced unstable internet connectivity. Public school students were disproportionately affected, with 60.2% lacking essential home learning materials. These conditions limited interaction, contributed to platform-related difficulties (63.6%), and reduced engagement, with 80.8% preferring face-to-face learning. Perceived effectiveness correlated significantly with internet quality and device availability (p < .001). The study highlights that equitable online learning requires reliable connectivity, adequate devices, supportive home environments, and stronger instructional interaction, with implications for equitable AI adoption in education.