Utilizing AI-Driven Neural Network Model for Personalized and Effective Learning

While advancements in educational methods have made personalized learning a key approach to improving the student experience, the use of artificial intelligence to identify and predict learning styles remains limited. This research introduces an Artificial Neural Network (ANN) model for predicting student learning styles. Data from 118 Anesthesia nursing students at Ahvaz Jundishapur University of Medical Sciences were collected through the standard VARK questionnaire, which includes four styles: visual, auditory, read/write, and kinesthetic. Following data processing, the model's performance was evaluated. The model showed promise in predicting learning styles, although its accuracy needs further development. Optimizing the algorithm, refining the data, and increasing the sample size offer potential avenues for improvement. This study indicates that with sufficient optimization, artificial intelligence can significantly personalize learning and enhance educational outcomes .