AI-enhanced differentiated instruction: Leveraging technology to support multiple intelligences in language education settings
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Abstract
This paper investigates the intersection of artificial intelligence (AI) and differentiated instruction (DI) through the framework of Gardner's theory of multiple intelligences in language education. With increasing diversity in learners' linguistic backgrounds, cognitive profiles, and proficiency levels, the need for personalized language teaching is more critical than ever. This study explores how AI-powered adaptive learning platforms and multimodal systems can facilitate differentiated instruction by responding to learners' dominant intelligences, linguistic, logical-mathematical, spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalistic. Through systematic analysis of 15 empirical studies, three longitudinal case studies, and a comprehensive examination of 42 AI-powered language learning platforms, this research demonstrates significant improvements in learning outcomes when AI tools are aligned with learners' intelligence profiles. Results indicate 23-45% improvement in retention rates, 67% increase in learner engagement, and 89% teacher satisfaction with AI-enhanced differentiated approaches. By analyzing practical applications, case studies, implementation frameworks, and ethical considerations, the paper offers both a theoretical foundation and actionable guidance for educators and policymakers seeking to implement inclusive, AI-driven language instruction strategies in diverse educational contexts.
Keywords: Adaptive learning; artificial intelligence; differentiated instruction; educational technology; language education; multiple intelligences; personalized learning
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