Abstract: Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners’ cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for English as a second language (ESL). The following research steps include simplifying system complexity while adding multimodal learning signals including eye monitoring and speech recognition and further developing the model across various educational subject areas. The research works as a promising foundation which propels AI real-time adaptive education systems for students from various backgrounds.

Komminni Ramesh, Christine Ann Thomas, Joel Osei-Asiamah, Bhuvaneswari Pagidipati, Elangovan Muniyandy, B. V. Suresh Reddy and Yousef A.Baker El-Ebiary, “Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160457

@article{Ramesh2025,
title = {Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160457},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160457},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Komminni Ramesh and Christine Ann Thomas and Joel Osei-Asiamah and Bhuvaneswari Pagidipati and Elangovan Muniyandy and B. V. Suresh Reddy and Yousef A.Baker El-Ebiary}
}