Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramesh, Komminni; Thomas, Christine Ann; Osei-Asiamah, Joel; Pagidipati, Bhuvaneswari; Muniyandy, Elangovan; Reddy, B. V. Suresh; El-Ebiary, Yousef A. Baker Title A name given to the resource Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications;Volume;16;Issue;4;pp.564-576 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14569/IJACSA.2025.0160457" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2025.0160457</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004016496?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004016496?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramesh K., BoS, Anurag Engineering College, Suryapet District, Telangana, Kodad, India; Thomas C.A., Department of English and Cultural Studies, Christ University, Bengaluru, India; Osei-Asiamah J., Department of Science and Technology Education, University of South Africa (Unisa), Gauteng Province, Pretoria, South Africa; Pagidipati B., Dept. of English and Foreign Languages, Sagi Rama Krishnam Raju Engineering College (A), West Godavari Dt, Andhra Pradesh, Bhimavaram, 534204, India; Muniyandy E., Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India, Applied Science Research Center, Applied Science Private University, Amman, Jordan; Reddy B.V.S., Department of CSE, Koneru Lakshmaiah Education Foundation, AP, Vaddeswaram, India; El-Ebiary Y.A.B., Faculty of Informatics and Computing, UniSZA University, Malaysia Description An account of the resource 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. (2025), (Science and Information Organization). All Rights Reserved. Subject The topic of the resource adaptive content personalization; artificial intelligence-based English as a secondary language learning; Cognitive load management Publisher An entity responsible for making the resource available Science and Information Organization Relation A related resource ISSN: 2158107X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online