Bridging Global AI Innovations with Local ESL Classrooms: A Conceptual Framework for Adaptive Language Learning Tools

Abstract

The integration of Artificial Intelligence (AI) in education has rapidly transformed sectors including English as a Second Language (ESL) learning. However, there remains a significant gap in adapting these global AI innovations to local educational contexts, particularly in ESL classrooms. This paper proposes a conceptual framework linking global AI advancements with local ESL practices by focusing on the development of adaptive learning tools to meet the diverse needs of ESL learners. The study synthesizes existing literature on AI applications in language learning and adaptive learning models, addressing both global trends and local educational challenges. Through this literature review, the paper explores AI’s potential to personalize learning, enhance language acquisition, and overcome barriers like the digital divide. Expected outcomes suggest that while AI shows promise in fostering engagement and personalized learning, its effective implementation in local ESL classrooms requires attention to contextual factors such as internet access, teacher involvement, and cultural relevance. The proposed framework recommends a hybrid approach that integrates AI tools with traditional teaching practices, ensuring an effective, contextually relevant learning environment. In conclusion, the paper advocates for collaboration among AI developers, educators, and policymakers to adapt AI tools to local needs, offering a direction for future research and implementation of adaptive language learning tools in ESL education.