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The rapid growth of web-based education has intensified the need for personalized learning approaches capable of addressing individual differences among learners. Traditional PHP-based educational systems often rely on static learning structures that fail to adapt to students’ varying abilities, learning pace, and preferences. This limitation reduces learning effectiveness and learner engagement. In response to this challenge, artificial intelligence (AI) offers new opportunities for designing adaptive and personalized learning environments. This article proposes an AI-driven framework for generating personalized learning paths within PHP-based educational systems. The approach leverages learner activity data, assessment results, and interaction patterns to dynamically recommend educational content tailored to individual needs. Machine learning techniques are conceptually integrated into the PHP architecture to analyze learner behavior and adjust learning sequences accordingly. The proposed system architecture demonstrates how AI components can be embedded into existing PHP platforms without requiring a complete system redesign. The results indicate that AI-driven personalization can significantly enhance learner engagement and learning efficiency by providing adaptive content recommendations and individualized learning trajectories. The findings highlight the practical potential of combining PHP web technologies with artificial intelligence to develop intelligent educational systems. This research contributes to the field of educational technology by presenting a scalable and adaptable model for personalized learning path generation in PHP-based environments.
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