Authors : Mst Masuma Akter Semi, Md Borhan Uddin, Sharmin Sultana, Motmainna Tamanna, Azim Uddin, Khandakar Rabbi Ahmed
Publication date : 2025/6/1
Source : International Journal of Advanced Computer Science & Applications
Volume : 16
Issue : 6
Description : An Artificial Intelligence-driven child learning system with a Machine Learning and Natural Language Processing-based approach to dynamically personalize educational experiences for children is proposed in this study. Using a Sentence-BERT model to encode student queries for the computation of semantic similarity and knowledge domains to be retrieved. A T5-based transformer model writes verbose, personalized feedback, and a Gradient Boosting Machine classifier predicts the appropriate learning outcomes. The content difficulty and personalization of educational trajectories across content are set by an integrated adaptive learning engine that monitors and adjusts for student performance. On the General Knowledge QA dataset, classification accuracy reaches 85.2%, and the ROC-AUC score is 0.912, which has been proven to be reliable in real-world cases. It also produces positive effects regarding the …
Total citations : Cited by 24
Scholar articles :
MM Akter Semi, MB Uddin, S Sultana, M Tamanna… – International Journal of Advanced Computer Science & …, 2025