Machine learning for the creation of intelligent educational content

AZINDA, Abdelmounaim and Khaldi, Mohamed (2025) Machine learning for the creation of intelligent educational content. Global Journal of Engineering and Technology Advances, 22 (1). 017-020. ISSN 2582-5003

The article explores how machine learning can revolutionize the creation of educational content by making it smart and personalized. Traditional teaching methods, often uniform, struggle to meet the diverse needs of learners, which limits their effectiveness. With machine learning, it becomes possible to generate varied and tailored content, such as texts, videos, quizzes, or simulations, while personalizing the paths according to individual learning styles and paces. The goal is to improve engagement and performance by providing learners with interactive resources that meet their specific needs. To achieve this, the study proposes a structured methodology that includes an analysis of current gaps, the development of a theoretical framework based on pedagogical concepts such as constructivism, and the use of advanced algorithms (NLP, supervised learning, clustering) [1][2]. A prototype will be developed using open-source frameworks, followed by experiments to validate the hypotheses that these tools increase engagement and the quality of learning [3]. Expected contributions include theoretical advancements and the creation of a functional tool, paving the way for a more interactive, inclusive education tailored to multilingual and multicultural contexts. The study aims to transform educational practices by combining technology and pedagogy for a richer and more effective experience.