Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies

In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner’s learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random…