A Comparative Study of AI Readiness in Language Teacher Education in the Global South

A growing body of global research supports the United Nations' Sustainable Development Goal 4, specifically Target 4.c, which emphasizes the importance of improving teacher quality through international cooperation and professional development. While Artificial Intelligence (AI) integration has garnered attention, language teacher education remains insufficiently addressed, underscoring the need for a dedicated framework to assess and enhance AI readiness in this field. Addressing this gap, the study utilizes Holmström's AI Readiness Framework to examine perceived and demonstrated readiness among language educators in Pakistan, Uzbekistan, and Saudi Arabia, exploring infrastructure, expertise, and institutional support. It identifies barriers and enablers to AI integration and proposes a replicable framework for contextually diverse teacher education systems. A two-phase mixed methods design was employed. In Phase 1, 400 language education professionals completed surveys, while focus group discussions were conducted with a purposive sample of 40 willing participants to explore contextual challenges in AI integration. Survey findings revealed the highest AI readiness in Saudi Arabia (M = 4.10), followed by Uzbekistan (M = 3.10) and Pakistan (M = 2.92). Readiness was correlated with infrastructure (r = 0.673), while faculty training explained 39.7% of the variance. Focus group discussions reinforced these findings, revealing ethical concerns, policy gaps, and limited training as persistent challenges. In Phase 2, 100 participants were stratified and completed a task-based AI readiness assessment. Strengths were noted in tool use and privacy awareness, while ethics and peer training were identified as areas for improvement. Recommendations include modular training, institutional readiness benchmarks, and localized policy reform. Future research should investigate the relationship between AI readiness and learner outcomes.