Abstract: 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 understanding and preference for learners of adaptive systems, as observed in user studies. AI technologies have exciting potential to deliver scalable, personalized education for young learners, as demonstrated in this work.

Masuma Akter Semi, Md Borhan Uddin, Sharmin Sultana, Motmainna Tamanna, Azim Uddin and Khandakar Rabbi Ahmed, “AI-Driven Education: Integrating Machine Learning and NLP to Transform Child Learning Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160603

@article{Semi2025,
title = {AI-Driven Education: Integrating Machine Learning and NLP to Transform Child Learning Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160603},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160603},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Masuma Akter Semi and Md Borhan Uddin and Sharmin Sultana and Motmainna Tamanna and Azim Uddin and Khandakar Rabbi Ahmed}
}