<![CDATA[The rapid integration of digital technology in education has emphasized the need for learning systems that are not only technologically advanced but also inclusive and equitable. This study aims to systematically review the implementation of coding and artificial intelligence (AI) in inclusive education and to examine their contributions to accessibility, personalization, and learning equity for students with special needs. This study employed a Systematic Literature Review (SLR). A total of 68 publications were identified from Scopus, Springer, DOAJ, Sinta, and Google Scholar databases. After applying inclusion criteria publication between 2019 and 2024, relevance to AI or coding in inclusive education, and methodological rigor 20 studies were selected for in-depth analysis. The selected studies were analyzed thematically using the Universal Design for Learning (UDL) framework as an analytical lens. The findings indicate that AI technologies, including speech-to-text systems, adaptive learning platforms, and image recognition tools, significantly enhance accessibility and personalized learning for students with hearing, visual, and cognitive impairments. Coding activities support the development of computational thinking, creativity, collaboration, and learner autonomy, particularly among students with special educational needs. However, implementation challenges remain, including limited digital infrastructure, insufficient teacher readiness, and a lack of local empirical research in developing countries such as Indonesia. This study uniquely integrates coding and artificial intelligence within the framework of inclusive education and contextualizes the findings in developing countries, particularly Indonesia, where empirical research on AI- and coding-based inclusive learning remains limited. Keywords: Coding, Artificial Intelligence, Inclusive Education, Systematic Literature Review, Universal Design for Learning.]]>
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