International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Revolutionizing Moroccan Education with AI: A Path to Customized Learning

Author(s) Rachid Ejjami
Country Morocco
Abstract This integrative literature review analyzes how AI, specifically Machine Learning (ML) and Large Language Models (LLMs), is used in Moroccan education. The study emphasizes the significance of modernizing traditional educational techniques. It investigates how artificial intelligence may personalize learning experiences, minimize educational disparities, and foster a culturally diverse and
technologically sophisticated learning environment. The ILR uses a conceptual framework that highlights customized learning, equitable education, technological innovation, and a systematic methodology that incorporates extensive literature synthesis. The research process thoroughly examines
multiple academic sources, including peer-reviewed articles, books, conference papers, and reports. The ILR identifies relevant patterns, barriers, and opportunities associated with integrating AI in Moroccan education by carefully studying and synthesizing data. The findings demonstrate the enormous potential of ML and LLMs in revolutionizing teaching methods, encouraging active student participation, and closing educational inequalities across Morocco. The paper finishes by underlining the need to incorporate AI into educational practice and identifying areas for future research. It emphasizes
investigating how AI may help Morocco and other countries modernize their education systems.
Keywords Artificial intelligence, Machine learning, Large language models, Education, Morocco, Personalized learning, Educational disparities, Cultural inclusivity
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-04
Cite This Revolutionizing Moroccan Education with AI: A Path to Customized Learning - Rachid Ejjami - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19462
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19462
Short DOI https://doi.org/gttbgd
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