An inclusive AI learning design framework for supporting diverse learners

Abstract

Artificial Intelligence (AI) is reshaping education, employment, communication, and global socio-economic systems. However, access to AI education remains unequal, particularly in multilingual, rural, under-resourced, and socio-economically diverse environments such as those in the Global South. Learners from disadvantaged groups often lack exposure to AI tools, accessible learning environments, culturally relevant examples, and inclusive pedagogical support. These disparities highlight the need for inclusive, equitable, and culturally grounded AI literacy models. This paper proposes a comprehensive Inclusive AI Learning Design Framework (IAILD) grounded in Universal Design for Learning (UDL), Culturally Responsive Pedagogy (CRP), Social Constructivism, and emerging AI literacy research. It synthesizes global AI education trends, pedagogical theories, methodological insights, and accessibility principles to construct a holistic, context-sensitive model for diverse learners. The framework emphasizes multilingual resources, low-tech approaches, assistive technologies, culturally relevant content, and inclusive assessment practices. The paper concludes with recommendations for future empirical validation, teacher professional development, development of regional-language AI learning tools, and policy directions to support scalable, equitable AI literacy. The IAILD aims to ensure that every learner irrespective of background, language, socio-economic status, gender, or ability can meaningfully participate in an AI-driven world.

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