Predicting AI Adoption in Education: A Mixed-Methods Study in Israel

Abstract

This article explores the nuanced landscape of Artificial Intelligence (AI) adoption in education within Israel, with a specific focus on the psychological, cultural, and technological determinants influencing educators' intentions to integrate AI technologies. The study leverages a distinctive mixed-methods approach, strategically integrating both quantitative and qualitative questionnaire data. In the quantitative phase, validated psychometric scales-the Technology Acceptance Model (TAM), Sense of Coherence (SOC), and Technological Self-Efficacy (TSE) were employed alongside closed-ended questions. Data from 248 Jewish and Arab educators revealed a significant positive association between TSE and SOC and AI adoption intentions, with TAM constructs mediating these relationships. Independent samples t-tests further highlighted significant ethnic disparities in both AI adoption intentions and TSE. In the qualitative phase, responses to embedded open-ended questions within the questionnaire were analyzed to deepen understanding of educators' experiences and perceptions. Triangulation with quantitative findings via thematic analysis unveiled nuanced insights regarding perceived AI utility, differential access to knowledge and training resources, and a salient tension between technological enthusiasm and ethical-pedagogical concerns. This research offers a theoretical contribution by underscoring the complex interplay of psychological, cultural, and systemic factors in shaping technology adoption and provides actionable guidelines for policy development and professional learning initiatives promoting equitable and effective AI integration within multicultural educational systems on a global scale.