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Mathematics learning for children with special needs often faces significant challenges due to limitations in conventional teaching methods that are less adaptive, less interactive, and not tailored to individual learning abilities. The integration of mobile technology offers a promising solution by providing flexible, engaging, and personalized learning environments that can enhance understanding and motivation. This study develops and evaluates a mobile application designed as an interactive learning medium for mathematics education targeting children with special needs. The development process follows a user-centered design approach combined with iterative prototyping. The application integrates visual learning elements, interactive exercises, and adaptive feedback mechanisms. The system was tested through usability evaluation and learning effectiveness assessment involving special education teachers and selected students. The findings indicate that the developed mobile application significantly improves student engagement and comprehension in basic mathematics concepts such as numbers, addition, and subtraction. Usability testing shows a high level of acceptance from teachers, with an average System Usability Scale (SUS) score categorized as “excellent.” Students demonstrated improved learning outcomes after using the application compared to traditional learning methods, indicating its effectiveness as an assistive educational tool. The results suggest that mobile-based interactive learning media can effectively support inclusive education by addressing the diverse cognitive and learning needs of children with special needs. The combination of visual interactivity and adaptive feedback contributes to improved attention span and conceptual understanding. However, limitations remain in terms of scalability and the need for broader testing across different categories of special needs learners. The proposed mobile application provides a viable and effective learning medium for enhancing mathematics education for children with special needs. Future research should focus on expanding adaptive features using artificial intelligence and conducting large-scale experimental validation.
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Sari et al. (2026) studied this question.
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