ARTIFICIAL INTELLIGENCE – DRIVEN PERSONALIZATION: SYSTEMATIC REVIEW OF FEATURE INFLUENCE ON JAWI LITERACY MODEL FOR DYSLEXIA
DOI:
https://doi.org/10.35631/JISTM.1142012Keywords:
Artificial Intelligence, Dyslexia, Jawi, Personalized LearningAbstract
The effectiveness of personalized educational technology is important for students with complex orthographic processing disorders. Considering the unique challenges posed by the Jawi script among dyslexic students, the objective of this research is to identify the artificial intelligence (AI) features that influence the development of personalized model for Jawi literacy among dyslexic students. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a Systematic Literature Review (SLR) was conducted using advanced search strategies across Scopus and Web of Science (WoS) databases. A total of 23 published papers were included. From these, 21 categories of grouped codes which were synthesized into five analytical themes reflecting the development of personalized models: Data Processing and Input Features, Core AI Architecture and Models, Adaptation and Personalization, Interface and Presentation, and Feedback, Fluency and Assessment. This research identifies five themes of AI features demonstrates for intervention development, concluding that successful personalization relies on the synergistic integration of multimodal feature extraction and cognitive load mitigation techniques. The findings demonstrate significant practical implications for educators, developers and policymakers aiming to advances the inclusive educational technology through a shift from standardized digital tools toward neurodiverse centric design. Thus, it moves beyond to create a more responsive literacy environment for dyslexic students.
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